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null | null | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga HannoRE -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga HannoRE -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga HannoRE
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "650.00 +/- 208.57", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | HannoRE/dqn-SpaceInvadersNoFrameskip-v4 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-13T13:57:13+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: URL
SB3: URL
SB3 Contrib: URL
Install the RL Zoo (with SB3 and SB3-Contrib):
If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:
## Training (with the RL Zoo)
## Hyperparameters
# Environment Arguments
| [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
43,
90,
73,
9,
5,
7
] | [
"passage: TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters# Environment Arguments"
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null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "285.80 +/- 18.04", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | imadjinn/ppo-v2-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-13T13:57:15+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers | # Prometh-MOEM-V.01 Model Card
**Prometh-MOEM-V.01** is a Mixture of Experts (MoE) model that integrates multiple foundational models to deliver enhanced performance across a spectrum of tasks. It harnesses the combined strengths of its constituent models, optimizing for accuracy, speed, and versatility.
## Model Sources and Components
This MoE model incorporates the following specialized models:
- [Wtzwho/Prometh-merge-test2](https://huggingface.co/Wtzwho/Prometh-merge-test2)
- [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
- [Wtzwho/Prometh-merge-test3](https://huggingface.co/Wtzwho/Prometh-merge-test3)
- [meta-math/MetaMath-Mistral-7B](https://huggingface.co/meta-math/MetaMath-Mistral-7B)
## Key Features
- **Enhanced Performance**: Specifically optimized for superior accuracy and efficiency in processing.
- **Versatility**: Exhibits outstanding versatility across a broad array of NLP tasks.
- **State-of-the-Art Integration**: Employs the latest in AI research to integrate multiple models effectively.
## Application Areas
Prometh-MOEM-V.01 excels in various applications, including:
- Text generation
- Sentiment analysis
- Language translation
- Question answering
## 💻Usage Instructions
To leverage Prometh-MOEM-V.01 in your projects, follow these steps:
```python
pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer, pipeline
import torch
model = "Wtzwho/Prometh-MOEM-V.01"
tokenizer = AutoTokenizer.from_pretrained(model)
# Setting up the pipeline
pipeline = pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
# Example query
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
### Merge configuration Details
```yaml
base_model: mistralai/Mistral-7B-Instruct-v0.2
gate_mode: hidden
dtype: bfloat16
experts_per_token: 2
experts:
- source_model: Wtzwho/Prometh-merge-test2
positive_prompts: ["You are a helpful general-purpose assistant."]
- source_model: mistralai/Mistral-7B-Instruct-v0.2
positive_prompts: ["You provide instruction-based assistance."]
- source_model: Wtzwho/Prometh-merge-test3
positive_prompts: ["You are helpful for coding-related queries."]
- source_model: meta-math/MetaMath-Mistral-7B
positive_prompts: ["You excel in mathematical problem solving."]
```
## Technical Specifications
### Advanced Optimization
**Quantization and Fine-Tuning**: Prometh-MOEM-V.01 can be fine tuned, offering pathways for both quantization and fine-tuning. These processes refine the model's performance and efficiency, catering to the nuanced demands of deployment environments.
#### Quantization
Quantization is a technique aimed at reducing the computational and memory burdens of model inference. It achieves this feat by transitioning from high-precision data types, like 32-bit floating point (float32), to more compact and efficient formats, such as 8-bit integers (int8). This transition not only shrinks the model's memory footprint but also accelerates its operational pace, making it more viable for embedded systems or devices with limited computational resources.
- **Benefits**:
- **Reduced Memory Footprint**: Occupies less storage, making the model more deployable on resource-constrained platforms.
- **Enhanced Performance**: Boosts inference speed due to the efficiency of integer arithmetic operations.
- **Energy Efficiency**: Consumes less power, a critical factor for mobile and embedded applications.
- **Application**:
- Prometh-MOEM-V.01 can be quantized post-training, adjusting to int8 without retraining from scratch. This method preserves the essence of its intelligence while adapting to the practical constraints of deployment environments.
#### Fine-Tuning
Beyond quantization, the model is primed for fine-tuning, allowing it to adapt to specific tasks or datasets with increased precision. This process involves additional training cycles on new data, thereby enhancing its acumen for particular applications.
- **Customization**: Tailors the model to specialized needs, optimizing its performance on tasks it was not originally designed for.
- **Versatility**: Ensures the model remains relevant and effective across a diverse array of use cases.
## Model Details and Attribution
- **Developed by:** [Iago Gaspar]
- **Shared by:** [AI Flow Solutions]
- **Model type:** Mixture of Experts Model
- **Language(s) (NLP):** en-en
- **License:** Apache-2.0
## Environmental Impact
## Out-of-Scope Use
The model is not intended for generating harmful or biased content.
## Bias, Risks, and Limitations
## Recommendations
Users should evaluate the model for biases and other ethical considerations before deploying it for real-world applications.
| {"language": ["en"], "license": "apache-2.0"} | text-generation | AIFS/Prometh-MOEM-V.01 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"has_space"
] | 2024-02-13T14:00:48+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mixtral #text-generation #conversational #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #has_space
| # Prometh-MOEM-V.01 Model Card
Prometh-MOEM-V.01 is a Mixture of Experts (MoE) model that integrates multiple foundational models to deliver enhanced performance across a spectrum of tasks. It harnesses the combined strengths of its constituent models, optimizing for accuracy, speed, and versatility.
## Model Sources and Components
This MoE model incorporates the following specialized models:
- Wtzwho/Prometh-merge-test2
- mistralai/Mistral-7B-Instruct-v0.2
- Wtzwho/Prometh-merge-test3
- meta-math/MetaMath-Mistral-7B
## Key Features
- Enhanced Performance: Specifically optimized for superior accuracy and efficiency in processing.
- Versatility: Exhibits outstanding versatility across a broad array of NLP tasks.
- State-of-the-Art Integration: Employs the latest in AI research to integrate multiple models effectively.
## Application Areas
Prometh-MOEM-V.01 excels in various applications, including:
- Text generation
- Sentiment analysis
- Language translation
- Question answering
## Usage Instructions
To leverage Prometh-MOEM-V.01 in your projects, follow these steps:
### Merge configuration Details
## Technical Specifications
### Advanced Optimization
Quantization and Fine-Tuning: Prometh-MOEM-V.01 can be fine tuned, offering pathways for both quantization and fine-tuning. These processes refine the model's performance and efficiency, catering to the nuanced demands of deployment environments.
#### Quantization
Quantization is a technique aimed at reducing the computational and memory burdens of model inference. It achieves this feat by transitioning from high-precision data types, like 32-bit floating point (float32), to more compact and efficient formats, such as 8-bit integers (int8). This transition not only shrinks the model's memory footprint but also accelerates its operational pace, making it more viable for embedded systems or devices with limited computational resources.
- Benefits:
- Reduced Memory Footprint: Occupies less storage, making the model more deployable on resource-constrained platforms.
- Enhanced Performance: Boosts inference speed due to the efficiency of integer arithmetic operations.
- Energy Efficiency: Consumes less power, a critical factor for mobile and embedded applications.
- Application:
- Prometh-MOEM-V.01 can be quantized post-training, adjusting to int8 without retraining from scratch. This method preserves the essence of its intelligence while adapting to the practical constraints of deployment environments.
#### Fine-Tuning
Beyond quantization, the model is primed for fine-tuning, allowing it to adapt to specific tasks or datasets with increased precision. This process involves additional training cycles on new data, thereby enhancing its acumen for particular applications.
- Customization: Tailors the model to specialized needs, optimizing its performance on tasks it was not originally designed for.
- Versatility: Ensures the model remains relevant and effective across a diverse array of use cases.
## Model Details and Attribution
- Developed by: [Iago Gaspar]
- Shared by: [AI Flow Solutions]
- Model type: Mixture of Experts Model
- Language(s) (NLP): en-en
- License: Apache-2.0
## Environmental Impact
## Out-of-Scope Use
The model is not intended for generating harmful or biased content.
## Bias, Risks, and Limitations
## Recommendations
Users should evaluate the model for biases and other ethical considerations before deploying it for real-world applications.
| [
"# Prometh-MOEM-V.01 Model Card\n\nPrometh-MOEM-V.01 is a Mixture of Experts (MoE) model that integrates multiple foundational models to deliver enhanced performance across a spectrum of tasks. It harnesses the combined strengths of its constituent models, optimizing for accuracy, speed, and versatility.",
"## Model Sources and Components\n\nThis MoE model incorporates the following specialized models:\n\n- Wtzwho/Prometh-merge-test2\n- mistralai/Mistral-7B-Instruct-v0.2\n- Wtzwho/Prometh-merge-test3\n- meta-math/MetaMath-Mistral-7B",
"## Key Features\n\n- Enhanced Performance: Specifically optimized for superior accuracy and efficiency in processing.\n- Versatility: Exhibits outstanding versatility across a broad array of NLP tasks.\n- State-of-the-Art Integration: Employs the latest in AI research to integrate multiple models effectively.",
"## Application Areas\n\nPrometh-MOEM-V.01 excels in various applications, including:\n\n- Text generation\n- Sentiment analysis\n- Language translation\n- Question answering",
"## Usage Instructions\n\nTo leverage Prometh-MOEM-V.01 in your projects, follow these steps:",
"### Merge configuration Details",
"## Technical Specifications",
"### Advanced Optimization\n\nQuantization and Fine-Tuning: Prometh-MOEM-V.01 can be fine tuned, offering pathways for both quantization and fine-tuning. These processes refine the model's performance and efficiency, catering to the nuanced demands of deployment environments.",
"#### Quantization\nQuantization is a technique aimed at reducing the computational and memory burdens of model inference. It achieves this feat by transitioning from high-precision data types, like 32-bit floating point (float32), to more compact and efficient formats, such as 8-bit integers (int8). This transition not only shrinks the model's memory footprint but also accelerates its operational pace, making it more viable for embedded systems or devices with limited computational resources.\n\n- Benefits:\n - Reduced Memory Footprint: Occupies less storage, making the model more deployable on resource-constrained platforms.\n - Enhanced Performance: Boosts inference speed due to the efficiency of integer arithmetic operations.\n - Energy Efficiency: Consumes less power, a critical factor for mobile and embedded applications.\n\n- Application:\n - Prometh-MOEM-V.01 can be quantized post-training, adjusting to int8 without retraining from scratch. This method preserves the essence of its intelligence while adapting to the practical constraints of deployment environments.",
"#### Fine-Tuning\nBeyond quantization, the model is primed for fine-tuning, allowing it to adapt to specific tasks or datasets with increased precision. This process involves additional training cycles on new data, thereby enhancing its acumen for particular applications.\n\n- Customization: Tailors the model to specialized needs, optimizing its performance on tasks it was not originally designed for.\n- Versatility: Ensures the model remains relevant and effective across a diverse array of use cases.",
"## Model Details and Attribution\n\n- Developed by: [Iago Gaspar]\n- Shared by: [AI Flow Solutions]\n- Model type: Mixture of Experts Model\n- Language(s) (NLP): en-en\n- License: Apache-2.0",
"## Environmental Impact",
"## Out-of-Scope Use\n\nThe model is not intended for generating harmful or biased content.",
"## Bias, Risks, and Limitations",
"## Recommendations\n\nUsers should evaluate the model for biases and other ethical considerations before deploying it for real-world applications."
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #has_space \n",
"# Prometh-MOEM-V.01 Model Card\n\nPrometh-MOEM-V.01 is a Mixture of Experts (MoE) model that integrates multiple foundational models to deliver enhanced performance across a spectrum of tasks. It harnesses the combined strengths of its constituent models, optimizing for accuracy, speed, and versatility.",
"## Model Sources and Components\n\nThis MoE model incorporates the following specialized models:\n\n- Wtzwho/Prometh-merge-test2\n- mistralai/Mistral-7B-Instruct-v0.2\n- Wtzwho/Prometh-merge-test3\n- meta-math/MetaMath-Mistral-7B",
"## Key Features\n\n- Enhanced Performance: Specifically optimized for superior accuracy and efficiency in processing.\n- Versatility: Exhibits outstanding versatility across a broad array of NLP tasks.\n- State-of-the-Art Integration: Employs the latest in AI research to integrate multiple models effectively.",
"## Application Areas\n\nPrometh-MOEM-V.01 excels in various applications, including:\n\n- Text generation\n- Sentiment analysis\n- Language translation\n- Question answering",
"## Usage Instructions\n\nTo leverage Prometh-MOEM-V.01 in your projects, follow these steps:",
"### Merge configuration Details",
"## Technical Specifications",
"### Advanced Optimization\n\nQuantization and Fine-Tuning: Prometh-MOEM-V.01 can be fine tuned, offering pathways for both quantization and fine-tuning. These processes refine the model's performance and efficiency, catering to the nuanced demands of deployment environments.",
"#### Quantization\nQuantization is a technique aimed at reducing the computational and memory burdens of model inference. It achieves this feat by transitioning from high-precision data types, like 32-bit floating point (float32), to more compact and efficient formats, such as 8-bit integers (int8). This transition not only shrinks the model's memory footprint but also accelerates its operational pace, making it more viable for embedded systems or devices with limited computational resources.\n\n- Benefits:\n - Reduced Memory Footprint: Occupies less storage, making the model more deployable on resource-constrained platforms.\n - Enhanced Performance: Boosts inference speed due to the efficiency of integer arithmetic operations.\n - Energy Efficiency: Consumes less power, a critical factor for mobile and embedded applications.\n\n- Application:\n - Prometh-MOEM-V.01 can be quantized post-training, adjusting to int8 without retraining from scratch. This method preserves the essence of its intelligence while adapting to the practical constraints of deployment environments.",
"#### Fine-Tuning\nBeyond quantization, the model is primed for fine-tuning, allowing it to adapt to specific tasks or datasets with increased precision. This process involves additional training cycles on new data, thereby enhancing its acumen for particular applications.\n\n- Customization: Tailors the model to specialized needs, optimizing its performance on tasks it was not originally designed for.\n- Versatility: Ensures the model remains relevant and effective across a diverse array of use cases.",
"## Model Details and Attribution\n\n- Developed by: [Iago Gaspar]\n- Shared by: [AI Flow Solutions]\n- Model type: Mixture of Experts Model\n- Language(s) (NLP): en-en\n- License: Apache-2.0",
"## Environmental Impact",
"## Out-of-Scope Use\n\nThe model is not intended for generating harmful or biased content.",
"## Bias, Risks, and Limitations",
"## Recommendations\n\nUsers should evaluate the model for biases and other ethical considerations before deploying it for real-world applications."
] | [
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"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #has_space \n# Prometh-MOEM-V.01 Model Card\n\nPrometh-MOEM-V.01 is a Mixture of Experts (MoE) model that integrates multiple foundational models to deliver enhanced performance across a spectrum of tasks. It harnesses the combined strengths of its constituent models, optimizing for accuracy, speed, and versatility.## Model Sources and Components\n\nThis MoE model incorporates the following specialized models:\n\n- Wtzwho/Prometh-merge-test2\n- mistralai/Mistral-7B-Instruct-v0.2\n- Wtzwho/Prometh-merge-test3\n- meta-math/MetaMath-Mistral-7B## Key Features\n\n- Enhanced Performance: Specifically optimized for superior accuracy and efficiency in processing.\n- Versatility: Exhibits outstanding versatility across a broad array of NLP tasks.\n- State-of-the-Art Integration: Employs the latest in AI research to integrate multiple models effectively.## Application Areas\n\nPrometh-MOEM-V.01 excels in various applications, including:\n\n- Text generation\n- Sentiment analysis\n- Language translation\n- Question answering## Usage Instructions\n\nTo leverage Prometh-MOEM-V.01 in your projects, follow these steps:### Merge configuration Details## Technical Specifications### Advanced Optimization\n\nQuantization and Fine-Tuning: Prometh-MOEM-V.01 can be fine tuned, offering pathways for both quantization and fine-tuning. These processes refine the model's performance and efficiency, catering to the nuanced demands of deployment environments."
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | Jayem-11/OpenPipe_mistral-ft-optimized-1227 | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-13T14:01:16+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
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## Uses
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | pogpog/microsoft-phi-2-conversation-summary | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 350_STEPS_TEST_SFT
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3260
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 350
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4448 | 0.1 | 50 | 0.5649 |
| 0.5998 | 0.2 | 100 | 0.6260 |
| 0.5585 | 0.29 | 150 | 0.5314 |
| 0.3909 | 0.39 | 200 | 0.3844 |
| 0.3704 | 0.49 | 250 | 0.3523 |
| 0.3376 | 0.59 | 300 | 0.3305 |
| 0.3266 | 0.68 | 350 | 0.3260 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0+cu117
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "350_STEPS_TEST_SFT", "results": []}]} | text-generation | tsavage68/chat_350STEPS_1e5_SFT | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T14:05:33+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| 350\_STEPS\_TEST\_SFT
=====================
This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3260
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 350
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.0+cu117
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 350",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 350",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
84,
143,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 350### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-base-FeatureExtraction
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/deberta-v3-base", "model-index": [{"name": "deberta-v3-base-FeatureExtraction", "results": []}]} | null | alitolga/deberta-v3-base-FeatureExtraction | [
"safetensors",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-base",
"license:mit",
"region:us"
] | 2024-02-13T14:05:39+00:00 | [] | [] | TAGS
#safetensors #generated_from_trainer #base_model-microsoft/deberta-v3-base #license-mit #region-us
|
# deberta-v3-base-FeatureExtraction
This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"# deberta-v3-base-FeatureExtraction\n\nThis model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu118\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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"# deberta-v3-base-FeatureExtraction\n\nThis model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu118\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
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"passage: TAGS\n#safetensors #generated_from_trainer #base_model-microsoft/deberta-v3-base #license-mit #region-us \n# deberta-v3-base-FeatureExtraction\n\nThis model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu118\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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] |
null | null | transformers |
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` | {"license": "other", "tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]} | text-generation | Jimmyhd/llama213b8Epochs1000Rows | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T14:06:46+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit AutoTrain.
# Usage
| [
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
60,
29,
3
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.# Usage"
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null | null | diffusers | # John
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/Meygure/JohnD/tree/main) them in the Files & versions tab.
| {"tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "-", "output": {"url": "images/1377296637_1500370441.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0"} | text-to-image | Meygure/JohnD | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"has_space",
"region:us"
] | 2024-02-13T14:06:51+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us
| # John
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"# John\n\n<Gallery />",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n",
"# John\n\n<Gallery />",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
60,
7,
28
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n# John\n\n<Gallery />## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "253.22 +/- 14.52", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | danny900714/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-13T14:11:11+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers | # Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
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<!-- Provide a longer summary of what this model is. -->
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] | {"language": ["en"], "widget": [{"text": "And another important point i would like to highlight, we selected google cloud as a technology partner to speed up the implementation of digital innovation"}, {"text": "Artificial Intelligence"}]} | text-classification | Zabbonat/DDI | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-13T14:13:16+00:00 | [
"1910.09700"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #en #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
| # Model Card for Model ID
This modelcard aims to be a base template for new models. It has been generated using this raw template.
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #en #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #pytorch #bert #text-classification #en #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga edwyrl -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga edwyrl -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga edwyrl
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "833.50 +/- 364.34", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | edwyrl/dqn-naive-SpaceInvadersNoFrameskip-v4 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-13T14:15:26+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: URL
SB3: URL
SB3 Contrib: URL
Install the RL Zoo (with SB3 and SB3-Contrib):
If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:
## Training (with the RL Zoo)
## Hyperparameters
# Environment Arguments
| [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
43,
90,
73,
9,
5,
7
] | [
"passage: TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters# Environment Arguments"
] | [
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null | null | transformers |
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| {"library_name": "transformers", "tags": []} | text-generation | B2111797/recipe_gener_v5 | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T14:15:46+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
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- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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"# Model Card for Model ID",
"## Model Details",
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"## Training Details",
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"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
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"## Model Card Authors [optional]",
"## Model Card Contact"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-hf"} | null | PaulM2000/peft_model_random_42_without_up_proj_Llama-2-7b-hf | [
"peft",
"safetensors",
"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
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- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
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APA:
## Glossary [optional]
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## Model Card Contact
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null | null | null |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
| {"license": "mit", "title": "Stable Cascade", "emoji": "\ud83d\udc41", "colorFrom": "blue", "colorTo": "purple", "sdk": "gradio", "sdk_version": "4.18.0", "app_file": "app.py", "pinned": false, "hf_oauth": true} | null | MonsterMMORPG/NewSD | [
"license:mit",
"region:us"
] | 2024-02-13T14:18:32+00:00 | [] | [] | TAGS
#license-mit #region-us
|
Check out the configuration reference at URL
| [] | [
"TAGS\n#license-mit #region-us \n"
] | [
11
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null | null | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: arekpaterak/ppo-ML-Agents-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | reinforcement-learning | arekpaterak/ppo-ML-Agents-Pyramids | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | 2024-02-13T14:20:08+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your
browser: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### Watch your Agent play
You can watch your agent playing directly in your browser
1. If the environment is part of ML-Agents official environments, go to URL
2. Step 1: Find your model_id: arekpaterak/ppo-ML-Agents-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: arekpaterak/ppo-ML-Agents-Pyramids\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: arekpaterak/ppo-ML-Agents-Pyramids\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
48,
209
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: arekpaterak/ppo-ML-Agents-Pyramids\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
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null | null | null |
## Purpose and Design of the Tokenizer
The tokenizer has been crafted with a specific focus on complementing the capabilities of `Llama2` and `Llama2-based` models. Here's a detailed overview of its design philosophy and linguistic proficiency:
### Vocabulary Composition
- **Size**: The tokenizer boasts a comprehensive vocabulary of 32,000 tokens, ensuring a wide coverage of linguistic elements.
- **Design for Llama2 Models**: It is explicitly designed to integrate seamlessly with Llama2 and Llama2-based models, enhancing their performance by providing a rich and well-structured linguistic dataset.
### Linguistic Proficiency
- **English Understanding**: While the tokenizer is capable of understanding English, providing basic support for processing and tokenizing English texts.
- **Bulgarian Proficiency**: Its proficiency, however, is significantly heightened for Bulgarian. It has been specifically designed and optimized for the Bulgarian language, ensuring superior performance in recognizing and tokenizing Bulgarian texts.
### Example with Hugging Face's Tokenizers API
```python
from transformers import LlamaTokenizerFast
# Initialize the tokenizer
tokenizer = LlamaTokenizerFast.from_pretrained("ldilov/llama2-bg-tokenizer")
# Example text in Bulgarian
bg_text = "Това е примерен текст на български език."
# Example text in English
en_text = "This is a sample text in English."
# Tokenize the Bulgarian text
tokens_bg = tokenizer.encode(bg_text, return_tensors="pt")
print("Tokenized Bulgarian Text:", tokens_bg)
# Tokenize the English text
tokens_en = tokenizer.encode(en_text, return_tensors="pt")
print("Tokenized English Text:", tokens_en)
```
### Output
Running the above code will produce output similar to the following, which shows the tokenized representation of both Bulgarian and English texts:
```plaintext
Tokenized Bulgarian Text: {'input_ids': [...], 'attention_mask': [...]}
Tokenized English Text: {'input_ids': [...], 'attention_mask': [...]}
```
## Training Approach Overview
The tokenizer training approach showcases a sophisticated and advanced methodology tailored for optimizing tokenizer performance specifically for `Llama2` compatibility. This approach stands out due to its comprehensive customization capabilities, dynamic adjustments based on dataset analysis, and the integration of advanced tokenization techniques. Here are the key components and advantages:
### **Customization and Special Tokens**
- **Dynamic Special Tokens**: Incorporates a configurable set of special tokens (`<unk>`, `<s>`, `</s>`), enhancing the tokenizer's ability to handle unknown tokens and start/end of sequence markers effectively.
### **Advanced Configuration**
- Utilizes a detailed configuration to fine-tune tokenizer behavior, including dropout rates, minimum token frequency, maximum sequence lengths, and padding strategies, ensuring optimal tokenization for varied text inputs.
### **Decoders**
- **Components**:
- `decoders.Replace`: Replaces specified characters (e.g., the replacement character `"▁"`) with another character (e.g., a space), aiding in the reconstruction of the original text from tokenized sequences.
- `decoders.ByteFallback`: Provides a fallback mechanism for handling bytes directly, useful for dealing with unknown or out-of-vocabulary tokens.
- `decoders.Fuse()`: Fuses consecutive tokens when possible to reduce tokenization granularity, potentially improving model performance by reducing sparsity.
- `decoders.Strip`: Removes leading or trailing characters (e.g., spaces), cleaning up the tokenized output for further processing.
- **Impact**: Decoders play a crucial role in translating tokenized sequences back into human-readable text, ensuring the tokenizer's output remains faithful to the original input while accommodating the model's needs.
### **Normalizers**
- **Components**:
- `Prepend("▁")`: Adds a specific character (e.g., the replacement character `"▁"`) to the beginning of the text, marking the start of processing.
- `Replace(r" ", "▁")`: Replaces spaces with a specified character, aiding in distinguishing between spaces as part of the text and as token separators.
- `NFKC()`: Applies Unicode normalization (NFKC), standardizing characters and reducing the complexity of text encoding.
- **Impact**: Normalizers standardize and prepare the input text for tokenization, improving the model's robustness and consistency in handling diverse text inputs.
### **Pre-tokenizers**
- **Components**:
- `pre_tokenizers.Sequence([Punctuation()])`: Applies a sequence of pre-tokenizers, such as identifying and separating punctuation, which helps in parsing the text more accurately before the main tokenization step.
- **Impact**: Pre-tokenizers refine the input text by identifying and isolating components like punctuation, which enhances the tokenizer's ability to accurately segment text into tokens.
### **Post-processing Template**
- **Components**:
- `TemplateProcessing(single, pair, special_tokens)`: Defines templates for processing single inputs and pairs of inputs, incorporating special tokens at specified positions.
- **Impact**: Post-processing templates dictate how tokenized sequences are structured, ensuring that special tokens are correctly placed. This is crucial for tasks that require understanding the relationship between sequences (e.g., question-answering), as it impacts how the model interprets sequence boundaries and relationships.
### **Dynamic Token Adjustments**
- **Dynamic Token Selection**: Employs statistical analysis to dynamically adjust the minimum frequency of tokens and identify rare but significant tokens (`dynamic_tokens`) for inclusion, improving model performance on specific domains or datasets.
### **Training and Evaluation Mechanism**
- **Efficient Training**: Leverages a custom training loop that merges datasets, applies dynamic token adjustments, and trains the tokenizer on merged datasets, prioritizing efficiency and effectiveness.
- **Evaluation**: Includes a sophisticated evaluation mechanism to assess tokenizer performance using a holdout dataset, focusing on round-trip errors and tokenization loss, ensuring the tokenizer's reliability and accuracy.
### **Advanced Tokenization Techniques**
- **Byte-Pair Encoding (BPE) with Custom Extensions**: Enhances the standard BPE algorithm with byte fallback, dropout, and unknown token fusion, addressing common tokenization challenges and improving token representation.
### **Sophisticated Normalization and Pre-tokenization**
- Implements a sequence of normalization and pre-tokenization steps that prepare text data for tokenization, improving the model's ability to understand and process varied textual inputs.
### **Comprehensive Post-processing**
- **Template Processing**: Utilizes template processing for single and pair tokenization tasks, incorporating special tokens effectively and ensuring consistent tokenization patterns.
### **Advantages Over Regular Approaches**
- **Dynamic Dropout**: Tokenizer training process doesn't use predefined `dropout` but instead calculates on the fly specifically tailored value, based on the current training dataset. This ensures that tokenizer model can generalize better by putting more weight on context rather than specifics. This would be beneficial at later stage when finetuning LLM with this tokenizer.
- **Dynamic Adaptation**: The ability to dynamically adjust tokenization parameters based (like `min_frequency`) on dataset analysis ensures that the tokenizer remains effective across different text domains.
- **Sophisticated Evaluation**: The inclusion of a detailed evaluation mechanism enables continuous assessment and improvement of the tokenizer's performance, ensuring high accuracy and reliability.
- **Number Bucketing**: Numbers in the text are categorized into predefined "buckets" based on their value. The bucketing process involves dividing the number space into several ranges (or buckets) and assigning each number to a specific bucket. Each bucket is represented by its own token that follows specific convention. Common years (e.g., 1900-2025) and ages (e.g., 1-100) are exceptions to this rule and they are represented they way they are written. This reduces sparsity and improves generalization without overfitting to specific values
- **URL Replacement**: URLs in the text are identified using a regular expression for common URL patterns and replaced with a special token `<url>`. Replacing varied URLs with a single token prevents the model from overfitting to specific web addresses, which are usually not relevant to understanding the text's general context.URLs can introduce a vast number of unique tokens into the vocabulary. Replacing them with a single token significantly simplifies the model's vocabulary. By abstracting away the specifics of URLs, models can focus more on the actual textual content.
## Tokenizer Evaluation Methodology
The evaluation of the tokenizer is crucial to ensure its effectiveness and accuracy. The approach used for evaluation relies on assessing the tokenizer's ability to accurately encode and decode textual data, aiming to measure how well the tokenizer can reproduce the original text after a round-trip of tokenization and detokenization. Here's a detailed explanation of how the loss function works and the significance of the evaluation scores:
### **Loss Function Breakdown**
1. **Tokenization**: Each sentence in the dataset is encoded to token IDs using `tokenizer.encode(example).ids`. This step converts text into a sequence of tokens that the model can understand.
2. **Detokenization**: The token IDs are then decoded back into text using `tokenizer.decode(tokenizer.encode(original).ids)`. This step attempts to reconstruct the original text from the token IDs.
3. **Distance Calculation**: For texts that do not match, the Levenshtein distance (a measure of the difference between two sequences) is calculated between the original and detokenized text, normalized by the original text length. This distance provides a quantitative measure of how much the texts differ.
5. **Loss**: The overall loss is computed as the average of these distances (`distance / round_trip_errors`), providing a single metric that reflects the tokenizer's accuracy in reproducing the original text.
### **Evaluation Results**
The evaluation results after training and testing the tokenizer with 5,000 random sentences not included in the training corpus are summarized in the table below:
| Version | Vocab Size | Loss | Training Time (seconds) |
|---------|------------| ----------------|-------------------------|
| v1.1 | 32,000 | 0.00791045752872809 | 9188.8694 |
### **Interpreting the Evaluation Score**
- **Vocab Size**: Indicates the tokenizer's vocabulary size. A larger vocabulary can potentially capture more nuances in the text but might also increase the risk of overfitting or inefficiency. Current vocab size is compatible with existing `Llama2` based models
- **Loss**: The average normalized Levenshtein distance across all errors. Evaluating on sentences not included in the training corpus and achieving such a low loss value highlights the tokenizer's strong generalization capability.
### **Mathematical Significance of the Evaluation Score**
From `Levenshtein distance` definition => **On average, the necessary edits to recover the original text from the detokenized output account for `0.79%` of the length of the original texts.**
The loss value of `0.00791045752872809` suggests that the tokenizer performs well in maintaining the integrity of the text through the tokenization process and sustains a high level of fidelity. Mathematically, this low loss score signifies a high degree of similarity between the original and detokenized texts, demonstrating the tokenizer's effectiveness. The process of detokenization, converting tokenized representations back into their original text form, does not always guarantee a 1:1 exact match to the original text. While the goal of detokenization is to reconstruct the original text as closely as possible, minor differences can occur. These variances are generally acceptable and sometimes inevitable.
Most NLP models and applications can tolerate some level of discrepancy between original and processed texts.
This approach represents a significant advancement over regular tokenization methods, offering a more adaptable, efficient, and accurate solution for preparing text data for machine learning models, especially those compatible with `Llama2`.
## Credits and Dataset Acknowledgments
When utilizing datasets from the Hugging Face 🤗 Datasets library for training models, it's crucial to acknowledge the contributions of the authors and organizations that have made these resources available. Below is a formatted credits section recognizing the datasets used:
### Dataset Acknowledgments
- **OSCAR Dataset**:
- Source: [OSCAR](https://huggingface.co/datasets/oscar) "unshuffled_deduplicated_bg"
- Description: A large-scale corpus obtained by language classification and filtering of the Common Crawl corpus.
- Authors: The OSCAR team from INRIA.
- **Bulgarian Poems Dataset**:
- Source: [Dilyana56/bulgarian_poems](https://huggingface.co/datasets/Dilyana56/bulgarian_poems)
- Description: A collection of Bulgarian poems.
- Authors: Dilyana Aleksandrova.
- **BG OPUS100 Processed Dataset**:
- Source: [anuragshas/bg_opus100_processed](https://huggingface.co/datasets/anuragshas/bg_opus100_processed)
- Description: Processed dataset for Bulgarian language based on OPUS100.
- Authors: Anurag Shandilya.
- **Reasoning BG Dataset**:
- Source: [reasoning_bg](https://huggingface.co/datasets/reasoning_bg) "philosophy-12th"
- Description: Dataset containing philosophical questions for reasoning tasks.
- Authors: Momchil Hardalov, Ivan Koychev, Preslav Nakov.
- **Clickbait News BG Dataset**:
- Source: [clickbait_news_bg](https://huggingface.co/datasets/clickbait_news_bg)
- Description: Dataset for detecting clickbait and fake news in Bulgarian.
- Authors: Bulgarian Association of PR Agencies.
### Training Corpus
- **Records**: Trained on over `700,000` bulgarian sentences
- **Total tokens**: `~21,000,000 tokens`
### Training Code Repository
- **URL**: https://github.com/ldilov/llama2-bg-tokenizer
- **Author**: Lazar Dilov (me)
### Evaluation Corpus
- **Records**: Evaluated on over `5000` bulgarian sentences
- **Total tokens**: `~15,000 tokens`
Acknowledging the sources of datasets and estimating the volume of training data are crucial steps in ensuring transparency and reproducibility in machine learning projects. These acknowledgments not only give credit where it's due but also provide insights into the scale and nature of the data used for model training. | {"language": ["en", "bg"], "license": "apache-2.0", "tags": ["llama2", "llama", "tokenizer", "bulgarian"], "datasets": ["Dilyana56/bulgarian_poems", "anuragshas/bg_opus100_processed", "reasoning_bg", "oscar"]} | null | ldilov/llama2-bg-tokenizer | [
"llama2",
"llama",
"tokenizer",
"bulgarian",
"en",
"bg",
"dataset:Dilyana56/bulgarian_poems",
"dataset:anuragshas/bg_opus100_processed",
"dataset:reasoning_bg",
"dataset:oscar",
"doi:10.57967/hf/1763",
"license:apache-2.0",
"region:us"
] | 2024-02-13T14:20:22+00:00 | [] | [
"en",
"bg"
] | TAGS
#llama2 #llama #tokenizer #bulgarian #en #bg #dataset-Dilyana56/bulgarian_poems #dataset-anuragshas/bg_opus100_processed #dataset-reasoning_bg #dataset-oscar #doi-10.57967/hf/1763 #license-apache-2.0 #region-us
| Purpose and Design of the Tokenizer
-----------------------------------
The tokenizer has been crafted with a specific focus on complementing the capabilities of 'Llama2' and 'Llama2-based' models. Here's a detailed overview of its design philosophy and linguistic proficiency:
### Vocabulary Composition
* Size: The tokenizer boasts a comprehensive vocabulary of 32,000 tokens, ensuring a wide coverage of linguistic elements.
* Design for Llama2 Models: It is explicitly designed to integrate seamlessly with Llama2 and Llama2-based models, enhancing their performance by providing a rich and well-structured linguistic dataset.
### Linguistic Proficiency
* English Understanding: While the tokenizer is capable of understanding English, providing basic support for processing and tokenizing English texts.
* Bulgarian Proficiency: Its proficiency, however, is significantly heightened for Bulgarian. It has been specifically designed and optimized for the Bulgarian language, ensuring superior performance in recognizing and tokenizing Bulgarian texts.
### Example with Hugging Face's Tokenizers API
### Output
Running the above code will produce output similar to the following, which shows the tokenized representation of both Bulgarian and English texts:
Training Approach Overview
--------------------------
The tokenizer training approach showcases a sophisticated and advanced methodology tailored for optimizing tokenizer performance specifically for 'Llama2' compatibility. This approach stands out due to its comprehensive customization capabilities, dynamic adjustments based on dataset analysis, and the integration of advanced tokenization techniques. Here are the key components and advantages:
### Customization and Special Tokens
* Dynamic Special Tokens: Incorporates a configurable set of special tokens ('', '~~', '~~'), enhancing the tokenizer's ability to handle unknown tokens and start/end of sequence markers effectively.
### Advanced Configuration
* Utilizes a detailed configuration to fine-tune tokenizer behavior, including dropout rates, minimum token frequency, maximum sequence lengths, and padding strategies, ensuring optimal tokenization for varied text inputs.
### Decoders
* Components:
+ 'decoders.Replace': Replaces specified characters (e.g., the replacement character '"▁"') with another character (e.g., a space), aiding in the reconstruction of the original text from tokenized sequences.
+ 'decoders.ByteFallback': Provides a fallback mechanism for handling bytes directly, useful for dealing with unknown or out-of-vocabulary tokens.
+ 'decoders.Fuse()': Fuses consecutive tokens when possible to reduce tokenization granularity, potentially improving model performance by reducing sparsity.
+ 'decoders.Strip': Removes leading or trailing characters (e.g., spaces), cleaning up the tokenized output for further processing.
* Impact: Decoders play a crucial role in translating tokenized sequences back into human-readable text, ensuring the tokenizer's output remains faithful to the original input while accommodating the model's needs.
### Normalizers
* Components:
+ 'Prepend("▁")': Adds a specific character (e.g., the replacement character '"▁"') to the beginning of the text, marking the start of processing.
+ 'Replace(r" ", "▁")': Replaces spaces with a specified character, aiding in distinguishing between spaces as part of the text and as token separators.
+ 'NFKC()': Applies Unicode normalization (NFKC), standardizing characters and reducing the complexity of text encoding.
* Impact: Normalizers standardize and prepare the input text for tokenization, improving the model's robustness and consistency in handling diverse text inputs.
### Pre-tokenizers
* Components:
+ 'pre\_tokenizers.Sequence([Punctuation()])': Applies a sequence of pre-tokenizers, such as identifying and separating punctuation, which helps in parsing the text more accurately before the main tokenization step.
* Impact: Pre-tokenizers refine the input text by identifying and isolating components like punctuation, which enhances the tokenizer's ability to accurately segment text into tokens.
### Post-processing Template
* Components:
+ 'TemplateProcessing(single, pair, special\_tokens)': Defines templates for processing single inputs and pairs of inputs, incorporating special tokens at specified positions.
* Impact: Post-processing templates dictate how tokenized sequences are structured, ensuring that special tokens are correctly placed. This is crucial for tasks that require understanding the relationship between sequences (e.g., question-answering), as it impacts how the model interprets sequence boundaries and relationships.
### Dynamic Token Adjustments
* Dynamic Token Selection: Employs statistical analysis to dynamically adjust the minimum frequency of tokens and identify rare but significant tokens ('dynamic\_tokens') for inclusion, improving model performance on specific domains or datasets.
### Training and Evaluation Mechanism
* Efficient Training: Leverages a custom training loop that merges datasets, applies dynamic token adjustments, and trains the tokenizer on merged datasets, prioritizing efficiency and effectiveness.
* Evaluation: Includes a sophisticated evaluation mechanism to assess tokenizer performance using a holdout dataset, focusing on round-trip errors and tokenization loss, ensuring the tokenizer's reliability and accuracy.
### Advanced Tokenization Techniques
* Byte-Pair Encoding (BPE) with Custom Extensions: Enhances the standard BPE algorithm with byte fallback, dropout, and unknown token fusion, addressing common tokenization challenges and improving token representation.
### Sophisticated Normalization and Pre-tokenization
* Implements a sequence of normalization and pre-tokenization steps that prepare text data for tokenization, improving the model's ability to understand and process varied textual inputs.
### Comprehensive Post-processing
* Template Processing: Utilizes template processing for single and pair tokenization tasks, incorporating special tokens effectively and ensuring consistent tokenization patterns.
### Advantages Over Regular Approaches
* Dynamic Dropout: Tokenizer training process doesn't use predefined 'dropout' but instead calculates on the fly specifically tailored value, based on the current training dataset. This ensures that tokenizer model can generalize better by putting more weight on context rather than specifics. This would be beneficial at later stage when finetuning LLM with this tokenizer.
* Dynamic Adaptation: The ability to dynamically adjust tokenization parameters based (like 'min\_frequency') on dataset analysis ensures that the tokenizer remains effective across different text domains.
* Sophisticated Evaluation: The inclusion of a detailed evaluation mechanism enables continuous assessment and improvement of the tokenizer's performance, ensuring high accuracy and reliability.
* Number Bucketing: Numbers in the text are categorized into predefined "buckets" based on their value. The bucketing process involves dividing the number space into several ranges (or buckets) and assigning each number to a specific bucket. Each bucket is represented by its own token that follows specific convention. Common years (e.g., 1900-2025) and ages (e.g., 1-100) are exceptions to this rule and they are represented they way they are written. This reduces sparsity and improves generalization without overfitting to specific values
* URL Replacement: URLs in the text are identified using a regular expression for common URL patterns and replaced with a special token ''. Replacing varied URLs with a single token prevents the model from overfitting to specific web addresses, which are usually not relevant to understanding the text's general context.URLs can introduce a vast number of unique tokens into the vocabulary. Replacing them with a single token significantly simplifies the model's vocabulary. By abstracting away the specifics of URLs, models can focus more on the actual textual content.
Tokenizer Evaluation Methodology
--------------------------------
The evaluation of the tokenizer is crucial to ensure its effectiveness and accuracy. The approach used for evaluation relies on assessing the tokenizer's ability to accurately encode and decode textual data, aiming to measure how well the tokenizer can reproduce the original text after a round-trip of tokenization and detokenization. Here's a detailed explanation of how the loss function works and the significance of the evaluation scores:
### Loss Function Breakdown
1. Tokenization: Each sentence in the dataset is encoded to token IDs using 'URL(example).ids'. This step converts text into a sequence of tokens that the model can understand.
2. Detokenization: The token IDs are then decoded back into text using 'URL(URL(original).ids)'. This step attempts to reconstruct the original text from the token IDs.
3. Distance Calculation: For texts that do not match, the Levenshtein distance (a measure of the difference between two sequences) is calculated between the original and detokenized text, normalized by the original text length. This distance provides a quantitative measure of how much the texts differ.
4. Loss: The overall loss is computed as the average of these distances ('distance / round\_trip\_errors'), providing a single metric that reflects the tokenizer's accuracy in reproducing the original text.
### Evaluation Results
The evaluation results after training and testing the tokenizer with 5,000 random sentences not included in the training corpus are summarized in the table below:
### Interpreting the Evaluation Score
* Vocab Size: Indicates the tokenizer's vocabulary size. A larger vocabulary can potentially capture more nuances in the text but might also increase the risk of overfitting or inefficiency. Current vocab size is compatible with existing 'Llama2' based models
* Loss: The average normalized Levenshtein distance across all errors. Evaluating on sentences not included in the training corpus and achieving such a low loss value highlights the tokenizer's strong generalization capability.
### Mathematical Significance of the Evaluation Score
From 'Levenshtein distance' definition => On average, the necessary edits to recover the original text from the detokenized output account for '0.79%' of the length of the original texts.
The loss value of '0.00791045752872809' suggests that the tokenizer performs well in maintaining the integrity of the text through the tokenization process and sustains a high level of fidelity. Mathematically, this low loss score signifies a high degree of similarity between the original and detokenized texts, demonstrating the tokenizer's effectiveness. The process of detokenization, converting tokenized representations back into their original text form, does not always guarantee a 1:1 exact match to the original text. While the goal of detokenization is to reconstruct the original text as closely as possible, minor differences can occur. These variances are generally acceptable and sometimes inevitable.
Most NLP models and applications can tolerate some level of discrepancy between original and processed texts.
This approach represents a significant advancement over regular tokenization methods, offering a more adaptable, efficient, and accurate solution for preparing text data for machine learning models, especially those compatible with 'Llama2'.
Credits and Dataset Acknowledgments
-----------------------------------
When utilizing datasets from the Hugging Face Datasets library for training models, it's crucial to acknowledge the contributions of the authors and organizations that have made these resources available. Below is a formatted credits section recognizing the datasets used:
### Dataset Acknowledgments
* OSCAR Dataset:
+ Source: OSCAR "unshuffled\_deduplicated\_bg"
+ Description: A large-scale corpus obtained by language classification and filtering of the Common Crawl corpus.
+ Authors: The OSCAR team from INRIA.
* Bulgarian Poems Dataset:
+ Source: Dilyana56/bulgarian\_poems
+ Description: A collection of Bulgarian poems.
+ Authors: Dilyana Aleksandrova.
* BG OPUS100 Processed Dataset:
+ Source: anuragshas/bg\_opus100\_processed
+ Description: Processed dataset for Bulgarian language based on OPUS100.
+ Authors: Anurag Shandilya.
* Reasoning BG Dataset:
+ Source: reasoning\_bg "philosophy-12th"
+ Description: Dataset containing philosophical questions for reasoning tasks.
+ Authors: Momchil Hardalov, Ivan Koychev, Preslav Nakov.
* Clickbait News BG Dataset:
+ Source: clickbait\_news\_bg
+ Description: Dataset for detecting clickbait and fake news in Bulgarian.
+ Authors: Bulgarian Association of PR Agencies.
### Training Corpus
* Records: Trained on over '700,000' bulgarian sentences
* Total tokens: '~21,000,000 tokens'
### Training Code Repository
* URL: URL
* Author: Lazar Dilov (me)
### Evaluation Corpus
* Records: Evaluated on over '5000' bulgarian sentences
* Total tokens: '~15,000 tokens'
Acknowledging the sources of datasets and estimating the volume of training data are crucial steps in ensuring transparency and reproducibility in machine learning projects. These acknowledgments not only give credit where it's due but also provide insights into the scale and nature of the data used for model training.
| [
"### Vocabulary Composition\n\n\n* Size: The tokenizer boasts a comprehensive vocabulary of 32,000 tokens, ensuring a wide coverage of linguistic elements.\n* Design for Llama2 Models: It is explicitly designed to integrate seamlessly with Llama2 and Llama2-based models, enhancing their performance by providing a rich and well-structured linguistic dataset.",
"### Linguistic Proficiency\n\n\n* English Understanding: While the tokenizer is capable of understanding English, providing basic support for processing and tokenizing English texts.\n* Bulgarian Proficiency: Its proficiency, however, is significantly heightened for Bulgarian. It has been specifically designed and optimized for the Bulgarian language, ensuring superior performance in recognizing and tokenizing Bulgarian texts.",
"### Example with Hugging Face's Tokenizers API",
"### Output\n\n\nRunning the above code will produce output similar to the following, which shows the tokenized representation of both Bulgarian and English texts:\n\n\nTraining Approach Overview\n--------------------------\n\n\nThe tokenizer training approach showcases a sophisticated and advanced methodology tailored for optimizing tokenizer performance specifically for 'Llama2' compatibility. This approach stands out due to its comprehensive customization capabilities, dynamic adjustments based on dataset analysis, and the integration of advanced tokenization techniques. Here are the key components and advantages:",
"### Customization and Special Tokens\n\n\n* Dynamic Special Tokens: Incorporates a configurable set of special tokens ('', '~~', '~~'), enhancing the tokenizer's ability to handle unknown tokens and start/end of sequence markers effectively.",
"### Advanced Configuration\n\n\n* Utilizes a detailed configuration to fine-tune tokenizer behavior, including dropout rates, minimum token frequency, maximum sequence lengths, and padding strategies, ensuring optimal tokenization for varied text inputs.",
"### Decoders\n\n\n* Components:\n\t+ 'decoders.Replace': Replaces specified characters (e.g., the replacement character '\"▁\"') with another character (e.g., a space), aiding in the reconstruction of the original text from tokenized sequences.\n\t+ 'decoders.ByteFallback': Provides a fallback mechanism for handling bytes directly, useful for dealing with unknown or out-of-vocabulary tokens.\n\t+ 'decoders.Fuse()': Fuses consecutive tokens when possible to reduce tokenization granularity, potentially improving model performance by reducing sparsity.\n\t+ 'decoders.Strip': Removes leading or trailing characters (e.g., spaces), cleaning up the tokenized output for further processing.\n* Impact: Decoders play a crucial role in translating tokenized sequences back into human-readable text, ensuring the tokenizer's output remains faithful to the original input while accommodating the model's needs.",
"### Normalizers\n\n\n* Components:\n\t+ 'Prepend(\"▁\")': Adds a specific character (e.g., the replacement character '\"▁\"') to the beginning of the text, marking the start of processing.\n\t+ 'Replace(r\" \", \"▁\")': Replaces spaces with a specified character, aiding in distinguishing between spaces as part of the text and as token separators.\n\t+ 'NFKC()': Applies Unicode normalization (NFKC), standardizing characters and reducing the complexity of text encoding.\n* Impact: Normalizers standardize and prepare the input text for tokenization, improving the model's robustness and consistency in handling diverse text inputs.",
"### Pre-tokenizers\n\n\n* Components:\n\t+ 'pre\\_tokenizers.Sequence([Punctuation()])': Applies a sequence of pre-tokenizers, such as identifying and separating punctuation, which helps in parsing the text more accurately before the main tokenization step.\n* Impact: Pre-tokenizers refine the input text by identifying and isolating components like punctuation, which enhances the tokenizer's ability to accurately segment text into tokens.",
"### Post-processing Template\n\n\n* Components:\n\t+ 'TemplateProcessing(single, pair, special\\_tokens)': Defines templates for processing single inputs and pairs of inputs, incorporating special tokens at specified positions.\n* Impact: Post-processing templates dictate how tokenized sequences are structured, ensuring that special tokens are correctly placed. This is crucial for tasks that require understanding the relationship between sequences (e.g., question-answering), as it impacts how the model interprets sequence boundaries and relationships.",
"### Dynamic Token Adjustments\n\n\n* Dynamic Token Selection: Employs statistical analysis to dynamically adjust the minimum frequency of tokens and identify rare but significant tokens ('dynamic\\_tokens') for inclusion, improving model performance on specific domains or datasets.",
"### Training and Evaluation Mechanism\n\n\n* Efficient Training: Leverages a custom training loop that merges datasets, applies dynamic token adjustments, and trains the tokenizer on merged datasets, prioritizing efficiency and effectiveness.\n* Evaluation: Includes a sophisticated evaluation mechanism to assess tokenizer performance using a holdout dataset, focusing on round-trip errors and tokenization loss, ensuring the tokenizer's reliability and accuracy.",
"### Advanced Tokenization Techniques\n\n\n* Byte-Pair Encoding (BPE) with Custom Extensions: Enhances the standard BPE algorithm with byte fallback, dropout, and unknown token fusion, addressing common tokenization challenges and improving token representation.",
"### Sophisticated Normalization and Pre-tokenization\n\n\n* Implements a sequence of normalization and pre-tokenization steps that prepare text data for tokenization, improving the model's ability to understand and process varied textual inputs.",
"### Comprehensive Post-processing\n\n\n* Template Processing: Utilizes template processing for single and pair tokenization tasks, incorporating special tokens effectively and ensuring consistent tokenization patterns.",
"### Advantages Over Regular Approaches\n\n\n* Dynamic Dropout: Tokenizer training process doesn't use predefined 'dropout' but instead calculates on the fly specifically tailored value, based on the current training dataset. This ensures that tokenizer model can generalize better by putting more weight on context rather than specifics. This would be beneficial at later stage when finetuning LLM with this tokenizer.\n* Dynamic Adaptation: The ability to dynamically adjust tokenization parameters based (like 'min\\_frequency') on dataset analysis ensures that the tokenizer remains effective across different text domains.\n* Sophisticated Evaluation: The inclusion of a detailed evaluation mechanism enables continuous assessment and improvement of the tokenizer's performance, ensuring high accuracy and reliability.\n* Number Bucketing: Numbers in the text are categorized into predefined \"buckets\" based on their value. The bucketing process involves dividing the number space into several ranges (or buckets) and assigning each number to a specific bucket. Each bucket is represented by its own token that follows specific convention. Common years (e.g., 1900-2025) and ages (e.g., 1-100) are exceptions to this rule and they are represented they way they are written. This reduces sparsity and improves generalization without overfitting to specific values\n* URL Replacement: URLs in the text are identified using a regular expression for common URL patterns and replaced with a special token ''. Replacing varied URLs with a single token prevents the model from overfitting to specific web addresses, which are usually not relevant to understanding the text's general context.URLs can introduce a vast number of unique tokens into the vocabulary. Replacing them with a single token significantly simplifies the model's vocabulary. By abstracting away the specifics of URLs, models can focus more on the actual textual content.\n\n\nTokenizer Evaluation Methodology\n--------------------------------\n\n\nThe evaluation of the tokenizer is crucial to ensure its effectiveness and accuracy. The approach used for evaluation relies on assessing the tokenizer's ability to accurately encode and decode textual data, aiming to measure how well the tokenizer can reproduce the original text after a round-trip of tokenization and detokenization. Here's a detailed explanation of how the loss function works and the significance of the evaluation scores:",
"### Loss Function Breakdown\n\n\n1. Tokenization: Each sentence in the dataset is encoded to token IDs using 'URL(example).ids'. This step converts text into a sequence of tokens that the model can understand.\n2. Detokenization: The token IDs are then decoded back into text using 'URL(URL(original).ids)'. This step attempts to reconstruct the original text from the token IDs.\n3. Distance Calculation: For texts that do not match, the Levenshtein distance (a measure of the difference between two sequences) is calculated between the original and detokenized text, normalized by the original text length. This distance provides a quantitative measure of how much the texts differ.\n4. Loss: The overall loss is computed as the average of these distances ('distance / round\\_trip\\_errors'), providing a single metric that reflects the tokenizer's accuracy in reproducing the original text.",
"### Evaluation Results\n\n\nThe evaluation results after training and testing the tokenizer with 5,000 random sentences not included in the training corpus are summarized in the table below:",
"### Interpreting the Evaluation Score\n\n\n* Vocab Size: Indicates the tokenizer's vocabulary size. A larger vocabulary can potentially capture more nuances in the text but might also increase the risk of overfitting or inefficiency. Current vocab size is compatible with existing 'Llama2' based models\n* Loss: The average normalized Levenshtein distance across all errors. Evaluating on sentences not included in the training corpus and achieving such a low loss value highlights the tokenizer's strong generalization capability.",
"### Mathematical Significance of the Evaluation Score\n\n\nFrom 'Levenshtein distance' definition => On average, the necessary edits to recover the original text from the detokenized output account for '0.79%' of the length of the original texts.\n\n\nThe loss value of '0.00791045752872809' suggests that the tokenizer performs well in maintaining the integrity of the text through the tokenization process and sustains a high level of fidelity. Mathematically, this low loss score signifies a high degree of similarity between the original and detokenized texts, demonstrating the tokenizer's effectiveness. The process of detokenization, converting tokenized representations back into their original text form, does not always guarantee a 1:1 exact match to the original text. While the goal of detokenization is to reconstruct the original text as closely as possible, minor differences can occur. These variances are generally acceptable and sometimes inevitable.\nMost NLP models and applications can tolerate some level of discrepancy between original and processed texts.\n\n\nThis approach represents a significant advancement over regular tokenization methods, offering a more adaptable, efficient, and accurate solution for preparing text data for machine learning models, especially those compatible with 'Llama2'.\n\n\nCredits and Dataset Acknowledgments\n-----------------------------------\n\n\nWhen utilizing datasets from the Hugging Face Datasets library for training models, it's crucial to acknowledge the contributions of the authors and organizations that have made these resources available. Below is a formatted credits section recognizing the datasets used:",
"### Dataset Acknowledgments\n\n\n* OSCAR Dataset:\n\n\n\t+ Source: OSCAR \"unshuffled\\_deduplicated\\_bg\"\n\t+ Description: A large-scale corpus obtained by language classification and filtering of the Common Crawl corpus.\n\t+ Authors: The OSCAR team from INRIA.\n* Bulgarian Poems Dataset:\n\n\n\t+ Source: Dilyana56/bulgarian\\_poems\n\t+ Description: A collection of Bulgarian poems.\n\t+ Authors: Dilyana Aleksandrova.\n* BG OPUS100 Processed Dataset:\n\n\n\t+ Source: anuragshas/bg\\_opus100\\_processed\n\t+ Description: Processed dataset for Bulgarian language based on OPUS100.\n\t+ Authors: Anurag Shandilya.\n* Reasoning BG Dataset:\n\n\n\t+ Source: reasoning\\_bg \"philosophy-12th\"\n\t+ Description: Dataset containing philosophical questions for reasoning tasks.\n\t+ Authors: Momchil Hardalov, Ivan Koychev, Preslav Nakov.\n* Clickbait News BG Dataset:\n\n\n\t+ Source: clickbait\\_news\\_bg\n\t+ Description: Dataset for detecting clickbait and fake news in Bulgarian.\n\t+ Authors: Bulgarian Association of PR Agencies.",
"### Training Corpus\n\n\n* Records: Trained on over '700,000' bulgarian sentences\n* Total tokens: '~21,000,000 tokens'",
"### Training Code Repository\n\n\n* URL: URL\n* Author: Lazar Dilov (me)",
"### Evaluation Corpus\n\n\n* Records: Evaluated on over '5000' bulgarian sentences\n* Total tokens: '~15,000 tokens'\n\n\nAcknowledging the sources of datasets and estimating the volume of training data are crucial steps in ensuring transparency and reproducibility in machine learning projects. These acknowledgments not only give credit where it's due but also provide insights into the scale and nature of the data used for model training."
] | [
"TAGS\n#llama2 #llama #tokenizer #bulgarian #en #bg #dataset-Dilyana56/bulgarian_poems #dataset-anuragshas/bg_opus100_processed #dataset-reasoning_bg #dataset-oscar #doi-10.57967/hf/1763 #license-apache-2.0 #region-us \n",
"### Vocabulary Composition\n\n\n* Size: The tokenizer boasts a comprehensive vocabulary of 32,000 tokens, ensuring a wide coverage of linguistic elements.\n* Design for Llama2 Models: It is explicitly designed to integrate seamlessly with Llama2 and Llama2-based models, enhancing their performance by providing a rich and well-structured linguistic dataset.",
"### Linguistic Proficiency\n\n\n* English Understanding: While the tokenizer is capable of understanding English, providing basic support for processing and tokenizing English texts.\n* Bulgarian Proficiency: Its proficiency, however, is significantly heightened for Bulgarian. It has been specifically designed and optimized for the Bulgarian language, ensuring superior performance in recognizing and tokenizing Bulgarian texts.",
"### Example with Hugging Face's Tokenizers API",
"### Output\n\n\nRunning the above code will produce output similar to the following, which shows the tokenized representation of both Bulgarian and English texts:\n\n\nTraining Approach Overview\n--------------------------\n\n\nThe tokenizer training approach showcases a sophisticated and advanced methodology tailored for optimizing tokenizer performance specifically for 'Llama2' compatibility. This approach stands out due to its comprehensive customization capabilities, dynamic adjustments based on dataset analysis, and the integration of advanced tokenization techniques. Here are the key components and advantages:",
"### Customization and Special Tokens\n\n\n* Dynamic Special Tokens: Incorporates a configurable set of special tokens ('', '~~', '~~'), enhancing the tokenizer's ability to handle unknown tokens and start/end of sequence markers effectively.",
"### Advanced Configuration\n\n\n* Utilizes a detailed configuration to fine-tune tokenizer behavior, including dropout rates, minimum token frequency, maximum sequence lengths, and padding strategies, ensuring optimal tokenization for varied text inputs.",
"### Decoders\n\n\n* Components:\n\t+ 'decoders.Replace': Replaces specified characters (e.g., the replacement character '\"▁\"') with another character (e.g., a space), aiding in the reconstruction of the original text from tokenized sequences.\n\t+ 'decoders.ByteFallback': Provides a fallback mechanism for handling bytes directly, useful for dealing with unknown or out-of-vocabulary tokens.\n\t+ 'decoders.Fuse()': Fuses consecutive tokens when possible to reduce tokenization granularity, potentially improving model performance by reducing sparsity.\n\t+ 'decoders.Strip': Removes leading or trailing characters (e.g., spaces), cleaning up the tokenized output for further processing.\n* Impact: Decoders play a crucial role in translating tokenized sequences back into human-readable text, ensuring the tokenizer's output remains faithful to the original input while accommodating the model's needs.",
"### Normalizers\n\n\n* Components:\n\t+ 'Prepend(\"▁\")': Adds a specific character (e.g., the replacement character '\"▁\"') to the beginning of the text, marking the start of processing.\n\t+ 'Replace(r\" \", \"▁\")': Replaces spaces with a specified character, aiding in distinguishing between spaces as part of the text and as token separators.\n\t+ 'NFKC()': Applies Unicode normalization (NFKC), standardizing characters and reducing the complexity of text encoding.\n* Impact: Normalizers standardize and prepare the input text for tokenization, improving the model's robustness and consistency in handling diverse text inputs.",
"### Pre-tokenizers\n\n\n* Components:\n\t+ 'pre\\_tokenizers.Sequence([Punctuation()])': Applies a sequence of pre-tokenizers, such as identifying and separating punctuation, which helps in parsing the text more accurately before the main tokenization step.\n* Impact: Pre-tokenizers refine the input text by identifying and isolating components like punctuation, which enhances the tokenizer's ability to accurately segment text into tokens.",
"### Post-processing Template\n\n\n* Components:\n\t+ 'TemplateProcessing(single, pair, special\\_tokens)': Defines templates for processing single inputs and pairs of inputs, incorporating special tokens at specified positions.\n* Impact: Post-processing templates dictate how tokenized sequences are structured, ensuring that special tokens are correctly placed. This is crucial for tasks that require understanding the relationship between sequences (e.g., question-answering), as it impacts how the model interprets sequence boundaries and relationships.",
"### Dynamic Token Adjustments\n\n\n* Dynamic Token Selection: Employs statistical analysis to dynamically adjust the minimum frequency of tokens and identify rare but significant tokens ('dynamic\\_tokens') for inclusion, improving model performance on specific domains or datasets.",
"### Training and Evaluation Mechanism\n\n\n* Efficient Training: Leverages a custom training loop that merges datasets, applies dynamic token adjustments, and trains the tokenizer on merged datasets, prioritizing efficiency and effectiveness.\n* Evaluation: Includes a sophisticated evaluation mechanism to assess tokenizer performance using a holdout dataset, focusing on round-trip errors and tokenization loss, ensuring the tokenizer's reliability and accuracy.",
"### Advanced Tokenization Techniques\n\n\n* Byte-Pair Encoding (BPE) with Custom Extensions: Enhances the standard BPE algorithm with byte fallback, dropout, and unknown token fusion, addressing common tokenization challenges and improving token representation.",
"### Sophisticated Normalization and Pre-tokenization\n\n\n* Implements a sequence of normalization and pre-tokenization steps that prepare text data for tokenization, improving the model's ability to understand and process varied textual inputs.",
"### Comprehensive Post-processing\n\n\n* Template Processing: Utilizes template processing for single and pair tokenization tasks, incorporating special tokens effectively and ensuring consistent tokenization patterns.",
"### Advantages Over Regular Approaches\n\n\n* Dynamic Dropout: Tokenizer training process doesn't use predefined 'dropout' but instead calculates on the fly specifically tailored value, based on the current training dataset. This ensures that tokenizer model can generalize better by putting more weight on context rather than specifics. This would be beneficial at later stage when finetuning LLM with this tokenizer.\n* Dynamic Adaptation: The ability to dynamically adjust tokenization parameters based (like 'min\\_frequency') on dataset analysis ensures that the tokenizer remains effective across different text domains.\n* Sophisticated Evaluation: The inclusion of a detailed evaluation mechanism enables continuous assessment and improvement of the tokenizer's performance, ensuring high accuracy and reliability.\n* Number Bucketing: Numbers in the text are categorized into predefined \"buckets\" based on their value. The bucketing process involves dividing the number space into several ranges (or buckets) and assigning each number to a specific bucket. Each bucket is represented by its own token that follows specific convention. Common years (e.g., 1900-2025) and ages (e.g., 1-100) are exceptions to this rule and they are represented they way they are written. This reduces sparsity and improves generalization without overfitting to specific values\n* URL Replacement: URLs in the text are identified using a regular expression for common URL patterns and replaced with a special token ''. Replacing varied URLs with a single token prevents the model from overfitting to specific web addresses, which are usually not relevant to understanding the text's general context.URLs can introduce a vast number of unique tokens into the vocabulary. Replacing them with a single token significantly simplifies the model's vocabulary. By abstracting away the specifics of URLs, models can focus more on the actual textual content.\n\n\nTokenizer Evaluation Methodology\n--------------------------------\n\n\nThe evaluation of the tokenizer is crucial to ensure its effectiveness and accuracy. The approach used for evaluation relies on assessing the tokenizer's ability to accurately encode and decode textual data, aiming to measure how well the tokenizer can reproduce the original text after a round-trip of tokenization and detokenization. Here's a detailed explanation of how the loss function works and the significance of the evaluation scores:",
"### Loss Function Breakdown\n\n\n1. Tokenization: Each sentence in the dataset is encoded to token IDs using 'URL(example).ids'. This step converts text into a sequence of tokens that the model can understand.\n2. Detokenization: The token IDs are then decoded back into text using 'URL(URL(original).ids)'. This step attempts to reconstruct the original text from the token IDs.\n3. Distance Calculation: For texts that do not match, the Levenshtein distance (a measure of the difference between two sequences) is calculated between the original and detokenized text, normalized by the original text length. This distance provides a quantitative measure of how much the texts differ.\n4. Loss: The overall loss is computed as the average of these distances ('distance / round\\_trip\\_errors'), providing a single metric that reflects the tokenizer's accuracy in reproducing the original text.",
"### Evaluation Results\n\n\nThe evaluation results after training and testing the tokenizer with 5,000 random sentences not included in the training corpus are summarized in the table below:",
"### Interpreting the Evaluation Score\n\n\n* Vocab Size: Indicates the tokenizer's vocabulary size. A larger vocabulary can potentially capture more nuances in the text but might also increase the risk of overfitting or inefficiency. Current vocab size is compatible with existing 'Llama2' based models\n* Loss: The average normalized Levenshtein distance across all errors. Evaluating on sentences not included in the training corpus and achieving such a low loss value highlights the tokenizer's strong generalization capability.",
"### Mathematical Significance of the Evaluation Score\n\n\nFrom 'Levenshtein distance' definition => On average, the necessary edits to recover the original text from the detokenized output account for '0.79%' of the length of the original texts.\n\n\nThe loss value of '0.00791045752872809' suggests that the tokenizer performs well in maintaining the integrity of the text through the tokenization process and sustains a high level of fidelity. Mathematically, this low loss score signifies a high degree of similarity between the original and detokenized texts, demonstrating the tokenizer's effectiveness. The process of detokenization, converting tokenized representations back into their original text form, does not always guarantee a 1:1 exact match to the original text. While the goal of detokenization is to reconstruct the original text as closely as possible, minor differences can occur. These variances are generally acceptable and sometimes inevitable.\nMost NLP models and applications can tolerate some level of discrepancy between original and processed texts.\n\n\nThis approach represents a significant advancement over regular tokenization methods, offering a more adaptable, efficient, and accurate solution for preparing text data for machine learning models, especially those compatible with 'Llama2'.\n\n\nCredits and Dataset Acknowledgments\n-----------------------------------\n\n\nWhen utilizing datasets from the Hugging Face Datasets library for training models, it's crucial to acknowledge the contributions of the authors and organizations that have made these resources available. Below is a formatted credits section recognizing the datasets used:",
"### Dataset Acknowledgments\n\n\n* OSCAR Dataset:\n\n\n\t+ Source: OSCAR \"unshuffled\\_deduplicated\\_bg\"\n\t+ Description: A large-scale corpus obtained by language classification and filtering of the Common Crawl corpus.\n\t+ Authors: The OSCAR team from INRIA.\n* Bulgarian Poems Dataset:\n\n\n\t+ Source: Dilyana56/bulgarian\\_poems\n\t+ Description: A collection of Bulgarian poems.\n\t+ Authors: Dilyana Aleksandrova.\n* BG OPUS100 Processed Dataset:\n\n\n\t+ Source: anuragshas/bg\\_opus100\\_processed\n\t+ Description: Processed dataset for Bulgarian language based on OPUS100.\n\t+ Authors: Anurag Shandilya.\n* Reasoning BG Dataset:\n\n\n\t+ Source: reasoning\\_bg \"philosophy-12th\"\n\t+ Description: Dataset containing philosophical questions for reasoning tasks.\n\t+ Authors: Momchil Hardalov, Ivan Koychev, Preslav Nakov.\n* Clickbait News BG Dataset:\n\n\n\t+ Source: clickbait\\_news\\_bg\n\t+ Description: Dataset for detecting clickbait and fake news in Bulgarian.\n\t+ Authors: Bulgarian Association of PR Agencies.",
"### Training Corpus\n\n\n* Records: Trained on over '700,000' bulgarian sentences\n* Total tokens: '~21,000,000 tokens'",
"### Training Code Repository\n\n\n* URL: URL\n* Author: Lazar Dilov (me)",
"### Evaluation Corpus\n\n\n* Records: Evaluated on over '5000' bulgarian sentences\n* Total tokens: '~15,000 tokens'\n\n\nAcknowledging the sources of datasets and estimating the volume of training data are crucial steps in ensuring transparency and reproducibility in machine learning projects. These acknowledgments not only give credit where it's due but also provide insights into the scale and nature of the data used for model training."
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"passage: TAGS\n#llama2 #llama #tokenizer #bulgarian #en #bg #dataset-Dilyana56/bulgarian_poems #dataset-anuragshas/bg_opus100_processed #dataset-reasoning_bg #dataset-oscar #doi-10.57967/hf/1763 #license-apache-2.0 #region-us \n### Vocabulary Composition\n\n\n* Size: The tokenizer boasts a comprehensive vocabulary of 32,000 tokens, ensuring a wide coverage of linguistic elements.\n* Design for Llama2 Models: It is explicitly designed to integrate seamlessly with Llama2 and Llama2-based models, enhancing their performance by providing a rich and well-structured linguistic dataset.### Linguistic Proficiency\n\n\n* English Understanding: While the tokenizer is capable of understanding English, providing basic support for processing and tokenizing English texts.\n* Bulgarian Proficiency: Its proficiency, however, is significantly heightened for Bulgarian. It has been specifically designed and optimized for the Bulgarian language, ensuring superior performance in recognizing and tokenizing Bulgarian texts.### Example with Hugging Face's Tokenizers API### Output\n\n\nRunning the above code will produce output similar to the following, which shows the tokenized representation of both Bulgarian and English texts:\n\n\nTraining Approach Overview\n--------------------------\n\n\nThe tokenizer training approach showcases a sophisticated and advanced methodology tailored for optimizing tokenizer performance specifically for 'Llama2' compatibility. This approach stands out due to its comprehensive customization capabilities, dynamic adjustments based on dataset analysis, and the integration of advanced tokenization techniques. Here are the key components and advantages:### Customization and Special Tokens\n\n\n* Dynamic Special Tokens: Incorporates a configurable set of special tokens ('', '~~', '~~'), enhancing the tokenizer's ability to handle unknown tokens and start/end of sequence markers effectively.",
"passage: ### Advanced Configuration\n\n\n* Utilizes a detailed configuration to fine-tune tokenizer behavior, including dropout rates, minimum token frequency, maximum sequence lengths, and padding strategies, ensuring optimal tokenization for varied text inputs.### Decoders\n\n\n* Components:\n\t+ 'decoders.Replace': Replaces specified characters (e.g., the replacement character '\"▁\"') with another character (e.g., a space), aiding in the reconstruction of the original text from tokenized sequences.\n\t+ 'decoders.ByteFallback': Provides a fallback mechanism for handling bytes directly, useful for dealing with unknown or out-of-vocabulary tokens.\n\t+ 'decoders.Fuse()': Fuses consecutive tokens when possible to reduce tokenization granularity, potentially improving model performance by reducing sparsity.\n\t+ 'decoders.Strip': Removes leading or trailing characters (e.g., spaces), cleaning up the tokenized output for further processing.\n* Impact: Decoders play a crucial role in translating tokenized sequences back into human-readable text, ensuring the tokenizer's output remains faithful to the original input while accommodating the model's needs.### Normalizers\n\n\n* Components:\n\t+ 'Prepend(\"▁\")': Adds a specific character (e.g., the replacement character '\"▁\"') to the beginning of the text, marking the start of processing.\n\t+ 'Replace(r\" \", \"▁\")': Replaces spaces with a specified character, aiding in distinguishing between spaces as part of the text and as token separators.\n\t+ 'NFKC()': Applies Unicode normalization (NFKC), standardizing characters and reducing the complexity of text encoding.\n* Impact: Normalizers standardize and prepare the input text for tokenization, improving the model's robustness and consistency in handling diverse text inputs.",
"passage: ### Pre-tokenizers\n\n\n* Components:\n\t+ 'pre\\_tokenizers.Sequence([Punctuation()])': Applies a sequence of pre-tokenizers, such as identifying and separating punctuation, which helps in parsing the text more accurately before the main tokenization step.\n* Impact: Pre-tokenizers refine the input text by identifying and isolating components like punctuation, which enhances the tokenizer's ability to accurately segment text into tokens.### Post-processing Template\n\n\n* Components:\n\t+ 'TemplateProcessing(single, pair, special\\_tokens)': Defines templates for processing single inputs and pairs of inputs, incorporating special tokens at specified positions.\n* Impact: Post-processing templates dictate how tokenized sequences are structured, ensuring that special tokens are correctly placed. This is crucial for tasks that require understanding the relationship between sequences (e.g., question-answering), as it impacts how the model interprets sequence boundaries and relationships.### Dynamic Token Adjustments\n\n\n* Dynamic Token Selection: Employs statistical analysis to dynamically adjust the minimum frequency of tokens and identify rare but significant tokens ('dynamic\\_tokens') for inclusion, improving model performance on specific domains or datasets.### Training and Evaluation Mechanism\n\n\n* Efficient Training: Leverages a custom training loop that merges datasets, applies dynamic token adjustments, and trains the tokenizer on merged datasets, prioritizing efficiency and effectiveness.\n* Evaluation: Includes a sophisticated evaluation mechanism to assess tokenizer performance using a holdout dataset, focusing on round-trip errors and tokenization loss, ensuring the tokenizer's reliability and accuracy.### Advanced Tokenization Techniques\n\n\n* Byte-Pair Encoding (BPE) with Custom Extensions: Enhances the standard BPE algorithm with byte fallback, dropout, and unknown token fusion, addressing common tokenization challenges and improving token representation.### Sophisticated Normalization and Pre-tokenization\n\n\n* Implements a sequence of normalization and pre-tokenization steps that prepare text data for tokenization, improving the model's ability to understand and process varied textual inputs.### Comprehensive Post-processing\n\n\n* Template Processing: Utilizes template processing for single and pair tokenization tasks, incorporating special tokens effectively and ensuring consistent tokenization patterns.",
"passage: ### Advantages Over Regular Approaches\n\n\n* Dynamic Dropout: Tokenizer training process doesn't use predefined 'dropout' but instead calculates on the fly specifically tailored value, based on the current training dataset. This ensures that tokenizer model can generalize better by putting more weight on context rather than specifics. This would be beneficial at later stage when finetuning LLM with this tokenizer.\n* Dynamic Adaptation: The ability to dynamically adjust tokenization parameters based (like 'min\\_frequency') on dataset analysis ensures that the tokenizer remains effective across different text domains.\n* Sophisticated Evaluation: The inclusion of a detailed evaluation mechanism enables continuous assessment and improvement of the tokenizer's performance, ensuring high accuracy and reliability.\n* Number Bucketing: Numbers in the text are categorized into predefined \"buckets\" based on their value. The bucketing process involves dividing the number space into several ranges (or buckets) and assigning each number to a specific bucket. Each bucket is represented by its own token that follows specific convention. Common years (e.g., 1900-2025) and ages (e.g., 1-100) are exceptions to this rule and they are represented they way they are written. This reduces sparsity and improves generalization without overfitting to specific values\n* URL Replacement: URLs in the text are identified using a regular expression for common URL patterns and replaced with a special token ''. Replacing varied URLs with a single token prevents the model from overfitting to specific web addresses, which are usually not relevant to understanding the text's general context.URLs can introduce a vast number of unique tokens into the vocabulary. Replacing them with a single token significantly simplifies the model's vocabulary. By abstracting away the specifics of URLs, models can focus more on the actual textual content.\n\n\nTokenizer Evaluation Methodology\n--------------------------------\n\n\nThe evaluation of the tokenizer is crucial to ensure its effectiveness and accuracy. The approach used for evaluation relies on assessing the tokenizer's ability to accurately encode and decode textual data, aiming to measure how well the tokenizer can reproduce the original text after a round-trip of tokenization and detokenization. Here's a detailed explanation of how the loss function works and the significance of the evaluation scores:### Loss Function Breakdown\n\n\n1. Tokenization: Each sentence in the dataset is encoded to token IDs using 'URL(example).ids'. This step converts text into a sequence of tokens that the model can understand.\n2. Detokenization: The token IDs are then decoded back into text using 'URL(URL(original).ids)'. This step attempts to reconstruct the original text from the token IDs.\n3. Distance Calculation: For texts that do not match, the Levenshtein distance (a measure of the difference between two sequences) is calculated between the original and detokenized text, normalized by the original text length. This distance provides a quantitative measure of how much the texts differ.\n4. Loss: The overall loss is computed as the average of these distances ('distance / round\\_trip\\_errors'), providing a single metric that reflects the tokenizer's accuracy in reproducing the original text.### Evaluation Results\n\n\nThe evaluation results after training and testing the tokenizer with 5,000 random sentences not included in the training corpus are summarized in the table below:### Interpreting the Evaluation Score\n\n\n* Vocab Size: Indicates the tokenizer's vocabulary size. A larger vocabulary can potentially capture more nuances in the text but might also increase the risk of overfitting or inefficiency. Current vocab size is compatible with existing 'Llama2' based models\n* Loss: The average normalized Levenshtein distance across all errors. Evaluating on sentences not included in the training corpus and achieving such a low loss value highlights the tokenizer's strong generalization capability."
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null | null | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Ostfriese -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Ostfriese -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Ostfriese
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "551.50 +/- 115.67", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Ostfriese/dqn-SpaceInvadersNoFrameskip-v4 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-13T14:23:06+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: URL
SB3: URL
SB3 Contrib: URL
Install the RL Zoo (with SB3 and SB3-Contrib):
If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:
## Training (with the RL Zoo)
## Hyperparameters
# Environment Arguments
| [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
43,
90,
73,
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] | [
"passage: TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters# Environment Arguments"
] | [
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null | null | transformers |
# Jaskier-7b-dpo-v3.3
**This is work-in-progress model, may not be ready for production use**
Model based on `CultriX/NeuralTrix-7B-dpo` (downstream version of Mistral7B) finetuned using Direct Preference Optimization on lmsys/mt_bench_human_judgments.
## Changelog
- 2024-02-13: Initial release
## About bards.ai
At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: bards.ai
Let us know if you use our model :). Also, if you need any help, feel free to contact us at [email protected] | {"language": ["en"], "license": "cc-by-4.0", "library_name": "transformers", "tags": ["llm", "7b"], "datasets": ["lmsys/mt_bench_human_judgments"]} | text-generation | bardsai/jaskier-7b-dpo-v3.3 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"llm",
"7b",
"en",
"dataset:lmsys/mt_bench_human_judgments",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T14:30:21+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #llm #7b #en #dataset-lmsys/mt_bench_human_judgments #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Jaskier-7b-dpo-v3.3
This is work-in-progress model, may not be ready for production use
Model based on 'CultriX/NeuralTrix-7B-dpo' (downstream version of Mistral7B) finetuned using Direct Preference Optimization on lmsys/mt_bench_human_judgments.
## Changelog
- 2024-02-13: Initial release
## About URL
At URL, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: URL
Let us know if you use our model :). Also, if you need any help, feel free to contact us at info@URL | [
"# Jaskier-7b-dpo-v3.3\n\nThis is work-in-progress model, may not be ready for production use\n\nModel based on 'CultriX/NeuralTrix-7B-dpo' (downstream version of Mistral7B) finetuned using Direct Preference Optimization on lmsys/mt_bench_human_judgments.",
"## Changelog\n\n- 2024-02-13: Initial release",
"## About URL\n\nAt URL, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: URL\n\nLet us know if you use our model :). Also, if you need any help, feel free to contact us at info@URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #llm #7b #en #dataset-lmsys/mt_bench_human_judgments #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Jaskier-7b-dpo-v3.3\n\nThis is work-in-progress model, may not be ready for production use\n\nModel based on 'CultriX/NeuralTrix-7B-dpo' (downstream version of Mistral7B) finetuned using Direct Preference Optimization on lmsys/mt_bench_human_judgments.",
"## Changelog\n\n- 2024-02-13: Initial release",
"## About URL\n\nAt URL, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: URL\n\nLet us know if you use our model :). Also, if you need any help, feel free to contact us at info@URL"
] | [
83,
85,
13,
81
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #llm #7b #en #dataset-lmsys/mt_bench_human_judgments #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Jaskier-7b-dpo-v3.3\n\nThis is work-in-progress model, may not be ready for production use\n\nModel based on 'CultriX/NeuralTrix-7B-dpo' (downstream version of Mistral7B) finetuned using Direct Preference Optimization on lmsys/mt_bench_human_judgments.## Changelog\n\n- 2024-02-13: Initial release## About URL\n\nAt URL, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: URL\n\nLet us know if you use our model :). Also, if you need any help, feel free to contact us at info@URL"
] | [
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null | null | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | GccX11/Reinforce-1 | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2024-02-13T14:32:08+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
39,
54
] | [
"passage: TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
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] |
null | null | diffusers | # Shincrayonfiy
<Gallery />
## Trigger words
You should use `style of TOK` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Hoga2/Crayon_Shinchanfiy/tree/main) them in the Files & versions tab.
| {"tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "A boy playing toy", "output": {"url": "images/33350bdcba4248b697361b8bbf9a6f12.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "style of TOK"} | text-to-image | Hoga2/Crayon_Shinchanfiy | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"has_space",
"region:us"
] | 2024-02-13T14:32:14+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us
| # Shincrayonfiy
<Gallery />
## Trigger words
You should use 'style of TOK' to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"# Shincrayonfiy\n\n<Gallery />",
"## Trigger words\n\nYou should use 'style of TOK' to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n",
"# Shincrayonfiy\n\n<Gallery />",
"## Trigger words\n\nYou should use 'style of TOK' to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
60,
11,
19,
28
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n# Shincrayonfiy\n\n<Gallery />## Trigger words\n\nYou should use 'style of TOK' to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
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null | null | null |
4-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [WizardLM-33B-V1.0-Uncensored](https://huggingface.co/cognitivecomputations/WizardLM-33B-V1.0-Uncensored)
| {"license": "other"} | null | numen-tech/WizardLM-33B-V1.0-Uncensored-w4a16g128asym | [
"arxiv:2308.13137",
"license:other",
"region:us"
] | 2024-02-13T14:32:28+00:00 | [
"2308.13137"
] | [] | TAGS
#arxiv-2308.13137 #license-other #region-us
|
4-bit OmniQuant quantized version of WizardLM-33B-V1.0-Uncensored
| [] | [
"TAGS\n#arxiv-2308.13137 #license-other #region-us \n"
] | [
19
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"passage: TAGS\n#arxiv-2308.13137 #license-other #region-us \n"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | Jayem-11/OpenPipe_mistral-ft-optimized-1227_merged | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T14:35:49+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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## Uses
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### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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### Training Procedure
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## Evaluation
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#### Testing Data
#### Factors
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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[optional]
BibTeX:
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## Model Card Contact
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"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"passage: TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "240.17 +/- 16.82", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | smehashutka/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-13T14:39:36+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "276.10 +/- 16.88", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | danylov/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-13T14:39:59+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vgm_v8.4.5
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0123
- Precision: 0.9237
- Recall: 0.9087
- F1: 0.9162
- Accuracy: 0.9973
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1800
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.32 | 100 | 0.0872 | 0.3391 | 0.1602 | 0.2176 | 0.9775 |
| No log | 0.64 | 200 | 0.0574 | 0.5483 | 0.5984 | 0.5723 | 0.9849 |
| No log | 0.96 | 300 | 0.0468 | 0.6535 | 0.7039 | 0.6777 | 0.9871 |
| No log | 1.29 | 400 | 0.0321 | 0.7385 | 0.7850 | 0.7611 | 0.9906 |
| 0.0716 | 1.61 | 500 | 0.0248 | 0.7851 | 0.7931 | 0.7891 | 0.9930 |
| 0.0716 | 1.93 | 600 | 0.0214 | 0.8178 | 0.8012 | 0.8094 | 0.9940 |
| 0.0716 | 2.25 | 700 | 0.0225 | 0.7830 | 0.8418 | 0.8113 | 0.9940 |
| 0.0716 | 2.57 | 800 | 0.0195 | 0.8530 | 0.8357 | 0.8443 | 0.9948 |
| 0.0716 | 2.89 | 900 | 0.0160 | 0.8465 | 0.8722 | 0.8591 | 0.9958 |
| 0.015 | 3.22 | 1000 | 0.0156 | 0.8960 | 0.8560 | 0.8755 | 0.9963 |
| 0.015 | 3.54 | 1100 | 0.0148 | 0.9011 | 0.8682 | 0.8843 | 0.9964 |
| 0.015 | 3.86 | 1200 | 0.0135 | 0.8990 | 0.8844 | 0.8916 | 0.9968 |
| 0.015 | 4.18 | 1300 | 0.0155 | 0.875 | 0.8803 | 0.8777 | 0.9962 |
| 0.015 | 4.5 | 1400 | 0.0141 | 0.8866 | 0.8884 | 0.8875 | 0.9967 |
| 0.0066 | 4.82 | 1500 | 0.0134 | 0.9010 | 0.9047 | 0.9028 | 0.9969 |
| 0.0066 | 5.14 | 1600 | 0.0136 | 0.8884 | 0.9047 | 0.8965 | 0.9967 |
| 0.0066 | 5.47 | 1700 | 0.0120 | 0.9194 | 0.9026 | 0.9110 | 0.9973 |
| 0.0066 | 5.79 | 1800 | 0.0123 | 0.9237 | 0.9087 | 0.9162 | 0.9973 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu121
- Datasets 2.2.2
- Tokenizers 0.13.3
| {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["sroie"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "vgm_v8.4.5", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.9237113402061856, "name": "Precision"}, {"type": "recall", "value": 0.9087221095334685, "name": "Recall"}, {"type": "f1", "value": 0.9161554192229039, "name": "F1"}, {"type": "accuracy", "value": 0.9973407623148031, "name": "Accuracy"}]}]}]} | token-classification | atatavana/vgm_v8.4.5 | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:sroie",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-13T14:40:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vgm\_v8.4.5
===========
This model is a fine-tuned version of microsoft/layoutlmv3-base on the sroie dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0123
* Precision: 0.9237
* Recall: 0.9087
* F1: 0.9162
* Accuracy: 0.9973
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 1800
### Training results
### Framework versions
* Transformers 4.28.0
* Pytorch 2.1.0+cu121
* Datasets 2.2.2
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 1800",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.2.2\n* Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 1800",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.2.2\n* Tokenizers 0.13.3"
] | [
76,
97,
4,
35
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 1800### Training results### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.2.2\n* Tokenizers 0.13.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2293
- Accuracy: 0.9309
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2215 | 1.0 | 1563 | 0.2098 | 0.9252 |
| 0.1477 | 2.0 | 3126 | 0.2293 | 0.9309 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "my_awesome_model", "results": []}]} | text-classification | sebastiencormier/my_awesome_model | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-13T14:43:46+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| my\_awesome\_model
==================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2293
* Accuracy: 0.9309
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
72,
98,
4,
30
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
# turkish-gpt2
This is a Turkish GPT-2 model. GPT-2 is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner.
Due to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.
## Example Usage
```python
from transformers import AutoTokenizer, GPT2LMHeadModel
from transformers import pipeline
model = GPT2LMHeadModel.from_pretrained("ytu-ce-cosmos/turkish-gpt2")
tokenizer = AutoTokenizer.from_pretrained("ytu-ce-cosmos/turkish-gpt2")
text_generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
r = text_generator("Teknolojinin gelişimi hayatımızı önemli ölçüde etkiledi. ", max_length=100)
[{'generated_text': 'Teknolojinin gelişimi hayatımızı önemli ölçüde etkiledi. "Dijitalleşme" ile birlikte hayatımızın belirli bir parçası daha rahata ermeye başladı.'}]
```
# Acknowledgments
- Research supported with Cloud TPUs from [Google's TensorFlow Research Cloud](https://sites.research.google/trc/about/) (TFRC). Thanks for providing access to the TFRC ❤️
- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage 🤗
# Citation
Paper coming soon 😊 | {"language": ["tr"], "license": "mit", "tags": ["Turkish", "turkish", "gpt2"], "datasets": ["uonlp/CulturaX"], "pipeline_tag": "text-generation"} | text-generation | ytu-ce-cosmos/turkish-gpt2 | [
"transformers",
"pytorch",
"tf",
"safetensors",
"gpt2",
"text-generation",
"Turkish",
"turkish",
"tr",
"dataset:uonlp/CulturaX",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T14:45:12+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #safetensors #gpt2 #text-generation #Turkish #turkish #tr #dataset-uonlp/CulturaX #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# turkish-gpt2
This is a Turkish GPT-2 model. GPT-2 is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner.
Due to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.
## Example Usage
# Acknowledgments
- Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ️
- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage
Paper coming soon | [
"# turkish-gpt2\n\nThis is a Turkish GPT-2 model. GPT-2 is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner.\nDue to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.",
"## Example Usage",
"# Acknowledgments\n- Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ️\n- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage \n\nPaper coming soon"
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #gpt2 #text-generation #Turkish #turkish #tr #dataset-uonlp/CulturaX #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# turkish-gpt2\n\nThis is a Turkish GPT-2 model. GPT-2 is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner.\nDue to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.",
"## Example Usage",
"# Acknowledgments\n- Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ️\n- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage \n\nPaper coming soon"
] | [
81,
98,
5,
68
] | [
"passage: TAGS\n#transformers #pytorch #tf #safetensors #gpt2 #text-generation #Turkish #turkish #tr #dataset-uonlp/CulturaX #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# turkish-gpt2\n\nThis is a Turkish GPT-2 model. GPT-2 is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner.\nDue to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.## Example Usage# Acknowledgments\n- Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ️\n- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage \n\nPaper coming soon"
] | [
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# stablelm-2-1.6-dpo-disticoder-v0.1
This model is a fine-tuned version of [plaguss/stablelm-2-1_6-sft-disticoder-v01](https://huggingface.co/plaguss/stablelm-2-1_6-sft-disticoder-v01) on the argilla/DistiCoder-dpo-binarized-train dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7398
- Rewards/chosen: -0.0026
- Rewards/rejected: -0.0002
- Rewards/accuracies: 0.4902
- Rewards/margins: -0.0024
- Logps/rejected: -359.7791
- Logps/chosen: -297.9016
- Logits/rejected: -0.9458
- Logits/chosen: -0.9673
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.7397 | 1.0 | 288 | 0.7419 | -0.0098 | -0.0101 | 0.5 | 0.0003 | -359.7990 | -297.9159 | -0.9478 | -0.9696 |
| 0.718 | 2.0 | 576 | 0.7291 | 0.0095 | -0.0100 | 0.5117 | 0.0194 | -359.7986 | -297.8773 | -0.9464 | -0.9679 |
| 0.6923 | 3.0 | 864 | 0.7398 | -0.0026 | -0.0002 | 0.4902 | -0.0024 | -359.7791 | -297.9016 | -0.9458 | -0.9673 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2 | {"license": "other", "library_name": "peft", "tags": ["choo-choo", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "choo-choo", "generated_from_trainer"], "base_model": "plaguss/stablelm-2-1_6-sft-disticoder-v01", "model-index": [{"name": "stablelm-2-1.6-dpo-disticoder-v0.1", "results": []}]} | null | plaguss/stablelm-2-1.6-dpo-disticoder-v0.1 | [
"peft",
"safetensors",
"stablelm_epoch",
"choo-choo",
"trl",
"dpo",
"generated_from_trainer",
"custom_code",
"base_model:plaguss/stablelm-2-1_6-sft-disticoder-v01",
"license:other",
"region:us"
] | 2024-02-13T14:45:49+00:00 | [] | [] | TAGS
#peft #safetensors #stablelm_epoch #choo-choo #trl #dpo #generated_from_trainer #custom_code #base_model-plaguss/stablelm-2-1_6-sft-disticoder-v01 #license-other #region-us
| stablelm-2-1.6-dpo-disticoder-v0.1
==================================
This model is a fine-tuned version of plaguss/stablelm-2-1\_6-sft-disticoder-v01 on the argilla/DistiCoder-dpo-binarized-train dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7398
* Rewards/chosen: -0.0026
* Rewards/rejected: -0.0002
* Rewards/accuracies: 0.4902
* Rewards/margins: -0.0024
* Logps/rejected: -359.7791
* Logps/chosen: -297.9016
* Logits/rejected: -0.9458
* Logits/chosen: -0.9673
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-07
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3
### Training results
### Framework versions
* PEFT 0.8.2
* Transformers 4.37.2
* Pytorch 2.1.1+cu121
* Datasets 2.16.1
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3",
"### Training results",
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] | [
"TAGS\n#peft #safetensors #stablelm_epoch #choo-choo #trl #dpo #generated_from_trainer #custom_code #base_model-plaguss/stablelm-2-1_6-sft-disticoder-v01 #license-other #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.2"
] | [
76,
146,
4,
39
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"passage: TAGS\n#peft #safetensors #stablelm_epoch #choo-choo #trl #dpo #generated_from_trainer #custom_code #base_model-plaguss/stablelm-2-1_6-sft-disticoder-v01 #license-other #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.2"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | longcule123/book_132 | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-13T14:46:35+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
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"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilenet_v2_1.4_224-northern-pikes
This model is a fine-tuned version of [google/mobilenet_v2_1.4_224](https://huggingface.co/google/mobilenet_v2_1.4_224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0281
- Accuracy: 0.9937
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3107 | 0.98 | 22 | 0.1320 | 0.9708 |
| 0.0701 | 2.0 | 45 | 0.0362 | 0.9854 |
| 0.0451 | 2.93 | 66 | 0.0281 | 0.9937 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
| {"license": "other", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "mobilenet_v2_1.4_224-northern-pikes", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9937369519832986, "name": "Accuracy"}]}]}]} | image-classification | aitomation/mobilenet_v2_1.4_224-northern-pikes | [
"transformers",
"pytorch",
"tensorboard",
"mobilenet_v2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:other",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2024-02-13T14:47:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mobilenet_v2 #image-classification #generated_from_trainer #dataset-imagefolder #license-other #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| mobilenet\_v2\_1.4\_224-northern-pikes
======================================
This model is a fine-tuned version of google/mobilenet\_v2\_1.4\_224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0281
* Accuracy: 0.9937
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.30.2
* Pytorch 1.13.1+cu117
* Datasets 2.13.1
* Tokenizers 0.13.3
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 1.13.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 1.13.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3"
] | [
71,
144,
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"passage: TAGS\n#transformers #pytorch #tensorboard #mobilenet_v2 #image-classification #generated_from_trainer #dataset-imagefolder #license-other #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 1.13.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3"
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-cased-lora-text-classification
This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5734
- Precision: 0.7362
- Recall: 0.8026
- F1 and accuracy: {'accuracy': 0.6970509383378016, 'f1': 0.7679671457905544}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 and accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:----------------------------------------------------------:|
| No log | 1.0 | 372 | 0.6573 | 0.6247 | 1.0 | {'accuracy': 0.6246648793565683, 'f1': 0.768976897689769} |
| 0.6699 | 2.0 | 744 | 0.6452 | 0.6247 | 1.0 | {'accuracy': 0.6246648793565683, 'f1': 0.768976897689769} |
| 0.6412 | 3.0 | 1116 | 0.6130 | 0.6602 | 0.8755 | {'accuracy': 0.6407506702412868, 'f1': 0.7527675276752768} |
| 0.6412 | 4.0 | 1488 | 0.5949 | 0.7413 | 0.8240 | {'accuracy': 0.710455764075067, 'f1': 0.7804878048780487} |
| 0.6158 | 5.0 | 1860 | 0.5860 | 0.7323 | 0.8455 | {'accuracy': 0.710455764075067, 'f1': 0.7848605577689244} |
| 0.5891 | 6.0 | 2232 | 0.5802 | 0.7381 | 0.7983 | {'accuracy': 0.6970509383378016, 'f1': 0.7670103092783506} |
| 0.5855 | 7.0 | 2604 | 0.5770 | 0.7354 | 0.8112 | {'accuracy': 0.6997319034852547, 'f1': 0.7714285714285714} |
| 0.5855 | 8.0 | 2976 | 0.5757 | 0.7328 | 0.8240 | {'accuracy': 0.7024128686327078, 'f1': 0.7757575757575758} |
| 0.5839 | 9.0 | 3348 | 0.5741 | 0.7362 | 0.8026 | {'accuracy': 0.6970509383378016, 'f1': 0.7679671457905544} |
| 0.5759 | 10.0 | 3720 | 0.5734 | 0.7362 | 0.8026 | {'accuracy': 0.6970509383378016, 'f1': 0.7679671457905544} |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall"], "base_model": "distilbert/distilbert-base-multilingual-cased", "model-index": [{"name": "distilbert-base-multilingual-cased-lora-text-classification", "results": []}]} | null | nhankins/es_distilbert_lora_adapter_1.0 | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-multilingual-cased",
"license:apache-2.0",
"region:us"
] | 2024-02-13T14:52:21+00:00 | [] | [] | TAGS
#tensorboard #safetensors #generated_from_trainer #base_model-distilbert/distilbert-base-multilingual-cased #license-apache-2.0 #region-us
| distilbert-base-multilingual-cased-lora-text-classification
===========================================================
This model is a fine-tuned version of distilbert/distilbert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5734
* Precision: 0.7362
* Recall: 0.8026
* F1 and accuracy: {'accuracy': 0.6970509383378016, 'f1': 0.7679671457905544}
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-distilbert/distilbert-base-multilingual-cased #license-apache-2.0 #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
51,
98,
4,
33
] | [
"passage: TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-distilbert/distilbert-base-multilingual-cased #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-PixelCopter", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "19.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | hugo-massonnat/Reinforce-PixelCopter | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2024-02-13T14:54:34+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
41,
58
] | [
"passage: TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GPT2_FT_By_NT_RAND_v5
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 50
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilgpt2", "model-index": [{"name": "GPT2_FT_By_NT_RAND_v5", "results": []}]} | text-generation | RickMartel/GPT2_FT_By_NT_RAND_v5 | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T14:56:26+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# GPT2_FT_By_NT_RAND_v5
This model is a fine-tuned version of distilgpt2 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 50
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# GPT2_FT_By_NT_RAND_v5\n\nThis model is a fine-tuned version of distilgpt2 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 50\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- num_epochs: 5\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT2_FT_By_NT_RAND_v5\n\nThis model is a fine-tuned version of distilgpt2 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 50\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- num_epochs: 5\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
77,
38,
6,
12,
8,
3,
103,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# GPT2_FT_By_NT_RAND_v5\n\nThis model is a fine-tuned version of distilgpt2 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 50\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- num_epochs: 5\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0005
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 425 | 0.0087 |
| 0.5768 | 2.0 | 850 | 0.0035 |
| 0.0087 | 3.0 | 1275 | 0.0020 |
| 0.0039 | 4.0 | 1700 | 0.0006 |
| 0.0023 | 5.0 | 2125 | 0.0005 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilroberta-base", "model-index": [{"name": "distilroberta-base-finetuned-wikitext2", "results": []}]} | text-generation | Doniaa/trial2 | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-generation",
"generated_from_trainer",
"base_model:distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-13T14:56:39+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #text-generation #generated_from_trainer #base_model-distilroberta-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-finetuned-wikitext2
======================================
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0005
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
68,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #roberta #text-generation #generated_from_trainer #base_model-distilroberta-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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] |
null | null | transformers |
## Model Description
This model is finetuned from [Viet-Mistral/Vistral-7B-Chat](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat). The dataset is taken from [bkai-foundation-models/vi-self-chat-sharegpt-format](https://huggingface.co/datasets/bkai-foundation-models/vi-self-chat-sharegpt-format)
This is a **low rank** finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.
The format looks like this:
```
<|im_start|>system
Provide some context and/or instructions to the model.
<|im_end|>
<|im_start|>user
The user’s message goes here
<|im_end|>
<|im_start|>assistant
```
## How to use it
The recommended way is to use the GGUF `vistral-7b-chatml-Q4_K_M.gguf` file included in this repository. Run it via llama.cpp (remember to pass `-cml` argument to use chatml template)
```bash
./main -m vistral-7b-chatml-Q4_K_M.gguf -p "Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực." -cml
```
Additionally, you can run the `python3 run.py` inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.
This is an example of a conversation using llama.cpp:
- <|im_start|>system
- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>
- <|im_start|>user
- > xin chào
- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>
- > trợ lý AI là gì? bạn giải thích được không?
- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>
- > ồ, cảm ơn, vậy bạn có thể làm gì?
- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>
- > tức là sao?
- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>
## Training
You can also look at the training code in the `finetune.py` file.
For tokenizer, I changed these things:
- Token `[INST]` to `<|im_start|>`, make it become special token
- Token `[/INST]` to `<|im_end|>`, make it become special token
- Change to `eos_token` to `<|im_end|>`
- Update `chat_template` to chatml, taken from [this example](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/tokenizer_config.json#L52)
Additionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.
## More information
Disclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a "hobby" to me. Training is done using a VPS on Google Cloud, I paid with my own money.
If you want to discuss, feel free to contact me at `contact at ngxson dot com` - [ngxson.com](https://ngxson.com)
| {"language": ["vi"], "license": "mit", "library_name": "transformers", "tags": ["LLMs", "NLP", "Vietnamese"]} | null | LoneStriker/Vistral-7B-ChatML-GGUF | [
"transformers",
"gguf",
"LLMs",
"NLP",
"Vietnamese",
"vi",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-13T14:58:49+00:00 | [] | [
"vi"
] | TAGS
#transformers #gguf #LLMs #NLP #Vietnamese #vi #license-mit #endpoints_compatible #region-us
|
## Model Description
This model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format
This is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.
The format looks like this:
## How to use it
The recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)
Additionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.
This is an example of a conversation using URL:
- <|im_start|>system
- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>
- <|im_start|>user
- > xin chào
- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>
- > trợ lý AI là gì? bạn giải thích được không?
- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>
- > ồ, cảm ơn, vậy bạn có thể làm gì?
- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>
- > tức là sao?
- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>
## Training
You can also look at the training code in the 'URL' file.
For tokenizer, I changed these things:
- Token '[INST]' to '<|im_start|>', make it become special token
- Token '[/INST]' to '<|im_end|>', make it become special token
- Change to 'eos_token' to '<|im_end|>'
- Update 'chat_template' to chatml, taken from this example
Additionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.
## More information
Disclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a "hobby" to me. Training is done using a VPS on Google Cloud, I paid with my own money.
If you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL
| [
"## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:",
"## How to use it\n\nThe recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)\n\n\n\nAdditionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.\n\nThis is an example of a conversation using URL:\n\n- <|im_start|>system\n- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>\n- <|im_start|>user\n- > xin chào \n- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>\n- > trợ lý AI là gì? bạn giải thích được không?\n- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>\n- > ồ, cảm ơn, vậy bạn có thể làm gì?\n- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>\n- > tức là sao?\n- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>",
"## Training\n\nYou can also look at the training code in the 'URL' file.\n\nFor tokenizer, I changed these things:\n- Token '[INST]' to '<|im_start|>', make it become special token\n- Token '[/INST]' to '<|im_end|>', make it become special token\n- Change to 'eos_token' to '<|im_end|>'\n- Update 'chat_template' to chatml, taken from this example\n\nAdditionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.",
"## More information\n\nDisclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a \"hobby\" to me. Training is done using a VPS on Google Cloud, I paid with my own money.\n\nIf you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL"
] | [
"TAGS\n#transformers #gguf #LLMs #NLP #Vietnamese #vi #license-mit #endpoints_compatible #region-us \n",
"## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:",
"## How to use it\n\nThe recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)\n\n\n\nAdditionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.\n\nThis is an example of a conversation using URL:\n\n- <|im_start|>system\n- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>\n- <|im_start|>user\n- > xin chào \n- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>\n- > trợ lý AI là gì? bạn giải thích được không?\n- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>\n- > ồ, cảm ơn, vậy bạn có thể làm gì?\n- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>\n- > tức là sao?\n- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>",
"## Training\n\nYou can also look at the training code in the 'URL' file.\n\nFor tokenizer, I changed these things:\n- Token '[INST]' to '<|im_start|>', make it become special token\n- Token '[/INST]' to '<|im_end|>', make it become special token\n- Change to 'eos_token' to '<|im_end|>'\n- Update 'chat_template' to chatml, taken from this example\n\nAdditionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.",
"## More information\n\nDisclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a \"hobby\" to me. Training is done using a VPS on Google Cloud, I paid with my own money.\n\nIf you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL"
] | [
39,
98,
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141,
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] | [
"passage: TAGS\n#transformers #gguf #LLMs #NLP #Vietnamese #vi #license-mit #endpoints_compatible #region-us \n## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:"
] | [
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-cased-lora-text-classification
This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6038
- Precision: 0.7039
- Recall: 0.9283
- F1 and accuracy: {'accuracy': 0.7017994858611826, 'f1': 0.8006872852233677}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 and accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:----------------------------------------------------------:|
| No log | 1.0 | 388 | 0.6457 | 0.6452 | 1.0 | {'accuracy': 0.6452442159383034, 'f1': 0.784375} |
| 0.6427 | 2.0 | 776 | 0.6410 | 0.6452 | 1.0 | {'accuracy': 0.6452442159383034, 'f1': 0.784375} |
| 0.6298 | 3.0 | 1164 | 0.6290 | 0.6486 | 1.0 | {'accuracy': 0.6503856041131105, 'f1': 0.786833855799373} |
| 0.6216 | 4.0 | 1552 | 0.6243 | 0.6552 | 0.9841 | {'accuracy': 0.6555269922879178, 'f1': 0.7866242038216561} |
| 0.6216 | 5.0 | 1940 | 0.6148 | 0.6799 | 0.9562 | {'accuracy': 0.6812339331619537, 'f1': 0.7947019867549668} |
| 0.6091 | 6.0 | 2328 | 0.6131 | 0.6791 | 0.9442 | {'accuracy': 0.6760925449871465, 'f1': 0.79} |
| 0.5892 | 7.0 | 2716 | 0.6102 | 0.6871 | 0.9363 | {'accuracy': 0.6838046272493573, 'f1': 0.7925801011804384} |
| 0.5818 | 8.0 | 3104 | 0.6051 | 0.6997 | 0.9283 | {'accuracy': 0.6966580976863753, 'f1': 0.7979452054794521} |
| 0.5818 | 9.0 | 3492 | 0.6061 | 0.6953 | 0.9363 | {'accuracy': 0.6940874035989717, 'f1': 0.797962648556876} |
| 0.5766 | 10.0 | 3880 | 0.6038 | 0.7039 | 0.9283 | {'accuracy': 0.7017994858611826, 'f1': 0.8006872852233677} |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall"], "base_model": "distilbert/distilbert-base-multilingual-cased", "model-index": [{"name": "distilbert-base-multilingual-cased-lora-text-classification", "results": []}]} | null | nhankins/yo_distilbert_lora_adapter_1.0 | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-multilingual-cased",
"license:apache-2.0",
"region:us"
] | 2024-02-13T14:59:11+00:00 | [] | [] | TAGS
#tensorboard #safetensors #generated_from_trainer #base_model-distilbert/distilbert-base-multilingual-cased #license-apache-2.0 #region-us
| distilbert-base-multilingual-cased-lora-text-classification
===========================================================
This model is a fine-tuned version of distilbert/distilbert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6038
* Precision: 0.7039
* Recall: 0.9283
* F1 and accuracy: {'accuracy': 0.7017994858611826, 'f1': 0.8006872852233677}
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-distilbert/distilbert-base-multilingual-cased #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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] |
null | null | transformers |
# Brunhilde-2x7b-MOE-DPO-v.01.5
Brunhilde-2x7b-MOE-DPO-v.01.5 is a Mixure of Experts (MoE).
* [NurtureAI/neural-chat-7b-v3-16k](https://huggingface.co/NurtureAI/neural-chat-7b-v3-16k)
* [mncai/mistral-7b-dpo-v6](https://huggingface.co/mncai/mistral-7b-dpo-v6)
## Usage
```
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "cc-by-nc-4.0", "tags": ["moe"]} | text-generation | arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T15:02:15+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #moe #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Brunhilde-2x7b-MOE-DPO-v.01.5
Brunhilde-2x7b-MOE-DPO-v.01.5 is a Mixure of Experts (MoE).
* NurtureAI/neural-chat-7b-v3-16k
* mncai/mistral-7b-dpo-v6
## Usage
| [
"# Brunhilde-2x7b-MOE-DPO-v.01.5\n\nBrunhilde-2x7b-MOE-DPO-v.01.5 is a Mixure of Experts (MoE).\n* NurtureAI/neural-chat-7b-v3-16k\n* mncai/mistral-7b-dpo-v6",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #moe #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Brunhilde-2x7b-MOE-DPO-v.01.5\n\nBrunhilde-2x7b-MOE-DPO-v.01.5 is a Mixure of Experts (MoE).\n* NurtureAI/neural-chat-7b-v3-16k\n* mncai/mistral-7b-dpo-v6",
"## Usage"
] | [
61,
77,
3
] | [
"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #moe #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Brunhilde-2x7b-MOE-DPO-v.01.5\n\nBrunhilde-2x7b-MOE-DPO-v.01.5 is a Mixure of Experts (MoE).\n* NurtureAI/neural-chat-7b-v3-16k\n* mncai/mistral-7b-dpo-v6## Usage"
] | [
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null | null | null | <h1 style="border-bottom: 2px solid black; font-size: 100px;" align="center"> SwinUNETR </h1>
_Trained by Margerie Huet Dastarac ._ <br>
_Training date: November 2023 ._
## 1. Task Description
Segmentation of the body on the CT scan on a dataset of 60 oropharyngeal patients. This model can be used to clean CT scans by setting voxels value outside of the body contour to air, a typical preprocessing step for other networks.
## 2. Model
### 2.1. Architecture
<img width="100%" src="https://cdn-uploads.huggingface.co/production/uploads/65c9dbefd6cbf9dfed67367e/o59In69BqrxTEoOdSHZLD.png" alt="alternatetext">
_Figure 1: SwinUNETR architecture_
### 2.2. Input
<ul>
<li> CT</li>
</ul>
### 2.3. Output
<ul>
<li> BODY</li>
</ul>
### 2.4 Training details
<ul>
<li> Number of epoch: 300 </li>
<li> Loss function: Dice loss </li>
<li> Optimizer: Adam </li>
<li> Learning Rate: 3e-4 </li>
<li> Dropout: No </li>
<li> Patch size in voxels: (128,128,128) </li>
<li> Data augmentation used:
<ul>
<li> RandSpatialCropd</li>
<li> RandFlipd axis:0</li>
<li> RandFlipd axis:1</li>
<li> RandFlipd axis:2</li>
<li> NormalizeIntensityd</li>
<li> RandScaleIntensityd factors:0.1 prob:1.0</li>
<li> RandShiftIntensityd, offsets:0.1, prob:1.0</li>
</ul>
</li>
</ul>
## 3. Dataset
<ul>
<li> Location: Head and neck, oropharynx </li>
<li> Training set size: 60 </li>
<li> Resolution in mm: 3x3x3 </li>
</ul>
## Performance
+ TBD
| {} | null | AI4MIRO/SwinUNETR_body_segmentation | [
"region:us"
] | 2024-02-13T15:04:02+00:00 | [] | [] | TAGS
#region-us
| <h1 style="border-bottom: 2px solid black; font-size: 100px;" align="center"> SwinUNETR </h1>
_Trained by Margerie Huet Dastarac ._ <br>
_Training date: November 2023 ._
## 1. Task Description
Segmentation of the body on the CT scan on a dataset of 60 oropharyngeal patients. This model can be used to clean CT scans by setting voxels value outside of the body contour to air, a typical preprocessing step for other networks.
## 2. Model
### 2.1. Architecture
<img width="100%" src="URL alt="alternatetext">
_Figure 1: SwinUNETR architecture_
### 2.2. Input
<ul>
<li> CT</li>
</ul>
### 2.3. Output
<ul>
<li> BODY</li>
</ul>
### 2.4 Training details
<ul>
<li> Number of epoch: 300 </li>
<li> Loss function: Dice loss </li>
<li> Optimizer: Adam </li>
<li> Learning Rate: 3e-4 </li>
<li> Dropout: No </li>
<li> Patch size in voxels: (128,128,128) </li>
<li> Data augmentation used:
<ul>
<li> RandSpatialCropd</li>
<li> RandFlipd axis:0</li>
<li> RandFlipd axis:1</li>
<li> RandFlipd axis:2</li>
<li> NormalizeIntensityd</li>
<li> RandScaleIntensityd factors:0.1 prob:1.0</li>
<li> RandShiftIntensityd, offsets:0.1, prob:1.0</li>
</ul>
</li>
</ul>
## 3. Dataset
<ul>
<li> Location: Head and neck, oropharynx </li>
<li> Training set size: 60 </li>
<li> Resolution in mm: 3x3x3 </li>
</ul>
## Performance
+ TBD
| [
"## 1. Task Description \nSegmentation of the body on the CT scan on a dataset of 60 oropharyngeal patients. This model can be used to clean CT scans by setting voxels value outside of the body contour to air, a typical preprocessing step for other networks.",
"## 2. Model",
"### 2.1. Architecture \n\n<img width=\"100%\" src=\"URL alt=\"alternatetext\">\n\n_Figure 1: SwinUNETR architecture_",
"### 2.2. Input \n<ul>\n<li> CT</li>\n</ul>",
"### 2.3. Output \n<ul>\n<li> BODY</li>\n</ul>",
"### 2.4 Training details \n<ul> \n<li> Number of epoch: 300 </li> \n<li> Loss function: Dice loss </li> \n<li> Optimizer: Adam </li> \n<li> Learning Rate: 3e-4 </li> \n<li> Dropout: No </li> \n<li> Patch size in voxels: (128,128,128) </li> \n<li> Data augmentation used: \n<ul>\n<li> RandSpatialCropd</li>\n<li> RandFlipd axis:0</li>\n<li> RandFlipd axis:1</li>\n<li> RandFlipd axis:2</li>\n<li> NormalizeIntensityd</li>\n<li> RandScaleIntensityd factors:0.1 prob:1.0</li>\n<li> RandShiftIntensityd, offsets:0.1, prob:1.0</li>\n</ul>\n</li> \n</ul>",
"## 3. Dataset \n<ul> \n<li> Location: Head and neck, oropharynx </li> \n<li> Training set size: 60 </li> \n<li> Resolution in mm: 3x3x3 </li> \n</ul>",
"## Performance \n+ TBD"
] | [
"TAGS\n#region-us \n",
"## 1. Task Description \nSegmentation of the body on the CT scan on a dataset of 60 oropharyngeal patients. This model can be used to clean CT scans by setting voxels value outside of the body contour to air, a typical preprocessing step for other networks.",
"## 2. Model",
"### 2.1. Architecture \n\n<img width=\"100%\" src=\"URL alt=\"alternatetext\">\n\n_Figure 1: SwinUNETR architecture_",
"### 2.2. Input \n<ul>\n<li> CT</li>\n</ul>",
"### 2.3. Output \n<ul>\n<li> BODY</li>\n</ul>",
"### 2.4 Training details \n<ul> \n<li> Number of epoch: 300 </li> \n<li> Loss function: Dice loss </li> \n<li> Optimizer: Adam </li> \n<li> Learning Rate: 3e-4 </li> \n<li> Dropout: No </li> \n<li> Patch size in voxels: (128,128,128) </li> \n<li> Data augmentation used: \n<ul>\n<li> RandSpatialCropd</li>\n<li> RandFlipd axis:0</li>\n<li> RandFlipd axis:1</li>\n<li> RandFlipd axis:2</li>\n<li> NormalizeIntensityd</li>\n<li> RandScaleIntensityd factors:0.1 prob:1.0</li>\n<li> RandShiftIntensityd, offsets:0.1, prob:1.0</li>\n</ul>\n</li> \n</ul>",
"## 3. Dataset \n<ul> \n<li> Location: Head and neck, oropharynx </li> \n<li> Training set size: 60 </li> \n<li> Resolution in mm: 3x3x3 </li> \n</ul>",
"## Performance \n+ TBD"
] | [
6,
64,
3,
37,
20,
21,
219,
58,
5
] | [
"passage: TAGS\n#region-us \n## 1. Task Description \nSegmentation of the body on the CT scan on a dataset of 60 oropharyngeal patients. This model can be used to clean CT scans by setting voxels value outside of the body contour to air, a typical preprocessing step for other networks.## 2. Model### 2.1. Architecture \n\n<img width=\"100%\" src=\"URL alt=\"alternatetext\">\n\n_Figure 1: SwinUNETR architecture_### 2.2. Input \n<ul>\n<li> CT</li>\n</ul>### 2.3. Output \n<ul>\n<li> BODY</li>\n</ul>### 2.4 Training details \n<ul> \n<li> Number of epoch: 300 </li> \n<li> Loss function: Dice loss </li> \n<li> Optimizer: Adam </li> \n<li> Learning Rate: 3e-4 </li> \n<li> Dropout: No </li> \n<li> Patch size in voxels: (128,128,128) </li> \n<li> Data augmentation used: \n<ul>\n<li> RandSpatialCropd</li>\n<li> RandFlipd axis:0</li>\n<li> RandFlipd axis:1</li>\n<li> RandFlipd axis:2</li>\n<li> NormalizeIntensityd</li>\n<li> RandScaleIntensityd factors:0.1 prob:1.0</li>\n<li> RandShiftIntensityd, offsets:0.1, prob:1.0</li>\n</ul>\n</li> \n</ul>## 3. Dataset \n<ul> \n<li> Location: Head and neck, oropharynx </li> \n<li> Training set size: 60 </li> \n<li> Resolution in mm: 3x3x3 </li> \n</ul>## Performance \n+ TBD"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chat_500STEPS_1e5rate_SFT
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3160
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.351 | 0.1 | 50 | 1.2639 |
| 0.3961 | 0.2 | 100 | 0.3739 |
| 0.3542 | 0.29 | 150 | 0.3401 |
| 0.3308 | 0.39 | 200 | 0.3253 |
| 0.33 | 0.49 | 250 | 0.3196 |
| 0.3246 | 0.59 | 300 | 0.3175 |
| 0.3159 | 0.68 | 350 | 0.3163 |
| 0.3105 | 0.78 | 400 | 0.3160 |
| 0.2879 | 0.88 | 450 | 0.3160 |
| 0.316 | 0.98 | 500 | 0.3160 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0+cu117
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "chat_500STEPS_1e5rate_SFT", "results": []}]} | text-generation | tsavage68/chat_500STEPS_1e5rate_SFT | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T15:04:42+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| chat\_500STEPS\_1e5rate\_SFT
============================
This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3160
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-06
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 500
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.0+cu117
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 500",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 500",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
84,
144,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 500### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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null | null | peft |
### Direct Use
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "Dhruvil47/falcon-7b-bioarxiv-qa"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
input_prompt = "Question: Are group 2 innate lymphoid cells ( ILC2s ) increased in chronic rhinosinusitis with nasal polyps or eosinophilia?\nAnswer:"
sequences = pipeline(
input_prompt,
max_length=300,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
generated_text = seq['generated_text'].split("\nQuestion:")[0]
generated_text = generated_text.replace(input_prompt, "").strip()
print(generated_text)
```
| {"library_name": "peft", "base_model": "tiiuae/falcon-7b"} | null | Dhruvil47/falcon-7b-bioarxiv-qa | [
"peft",
"safetensors",
"base_model:tiiuae/falcon-7b",
"region:us"
] | 2024-02-13T15:05:54+00:00 | [] | [] | TAGS
#peft #safetensors #base_model-tiiuae/falcon-7b #region-us
|
### Direct Use
| [
"### Direct Use"
] | [
"TAGS\n#peft #safetensors #base_model-tiiuae/falcon-7b #region-us \n",
"### Direct Use"
] | [
27,
4
] | [
"passage: TAGS\n#peft #safetensors #base_model-tiiuae/falcon-7b #region-us \n### Direct Use"
] | [
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null | null | null | ### My-Pet-Dog Dreambooth model trained by shanmukh542 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 21KT1A0542
Sample pictures of this concept:
| {"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]} | text-to-image | shanmukh542/my-pet-dog | [
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] | 2024-02-13T15:10:47+00:00 | [] | [] | TAGS
#safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #region-us
| ### My-Pet-Dog Dreambooth model trained by shanmukh542 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 21KT1A0542
Sample pictures of this concept:
| [
"### My-Pet-Dog Dreambooth model trained by shanmukh542 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 21KT1A0542\n\nSample pictures of this concept:"
] | [
"TAGS\n#safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #region-us \n",
"### My-Pet-Dog Dreambooth model trained by shanmukh542 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 21KT1A0542\n\nSample pictures of this concept:"
] | [
48,
55
] | [
"passage: TAGS\n#safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #region-us \n### My-Pet-Dog Dreambooth model trained by shanmukh542 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 21KT1A0542\n\nSample pictures of this concept:"
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null | null | keras |
# Breast Cancer Wisconsin v1.0 Model Card
This study explores the application of artificial intelligence (AI) in detecting breast cancer using the Wisconsin Breast Cancer dataset. Machine learning algorithms are trained on clinical and diagnostic features extracted from breast tissue samples to classify them as benign or malignant. Results demonstrate the effectiveness of AI-based approaches in accurately identifying breast cancer, offering potential improvements in early detection and treatment outcomes.
Below is a visualization:
A machine learning classifier trained on the breast cancer dataset can assist in diagnosing breast cancer by analyzing various features of the breast tissue. This classifier goes through a process of data collection, preprocessing, and training. Firstly, relevant data regarding patients' breast tissue characteristics, such as cell size, shape, and other diagnostic measurements, are collected. After that, the collected data undergo preprocessing steps, which may include handling missing values, normalization, and feature extraction. Once the data is prepared, it is used to train a machine learning model, such as a neural network or a support vector machine (SVM). During the training phase, the model learns the patterns and relationships within the data to classify breast tissue samples as malignant or benign accurately. Finally, the trained model can be deployed to predict whether a new tissue sample is indicative of cancer, providing valuable assistance to medical professionals in early detection and diagnosis.
### Model Description
- **Developed by:** Anezatra Katedram
- **Model type:** Text Classification
- **License:** GNU General Public License v3.0
- This model was trained by Anezatra and designed with the keras module.
- Use of the model for any commercial purpose requires agreement. [Contact TR](https://instagram.com/xx___xxbora_anezatraxx___xx_) for more information.
- **Model Description:** Breast Cancer Detection Using Artificial Intelligence (Trained with Keras module)
- **BCWMAI:** Resources for more information: [Breast Cancer Wiconsin AI]
| {"language": ["en"], "license": "gpl-3.0", "library_name": "keras", "tags": ["medical"], "datasets": ["AiresPucrs/breast-cancer-wisconsin"], "metrics": ["accuracy"], "pipeline_tag": "text-classification"} | text-classification | anezatra/breast-cancer-wisconsin | [
"keras",
"medical",
"text-classification",
"en",
"dataset:AiresPucrs/breast-cancer-wisconsin",
"license:gpl-3.0",
"region:us"
] | 2024-02-13T15:11:11+00:00 | [] | [
"en"
] | TAGS
#keras #medical #text-classification #en #dataset-AiresPucrs/breast-cancer-wisconsin #license-gpl-3.0 #region-us
|
# Breast Cancer Wisconsin v1.0 Model Card
This study explores the application of artificial intelligence (AI) in detecting breast cancer using the Wisconsin Breast Cancer dataset. Machine learning algorithms are trained on clinical and diagnostic features extracted from breast tissue samples to classify them as benign or malignant. Results demonstrate the effectiveness of AI-based approaches in accurately identifying breast cancer, offering potential improvements in early detection and treatment outcomes.
Below is a visualization:
!examples
A machine learning classifier trained on the breast cancer dataset can assist in diagnosing breast cancer by analyzing various features of the breast tissue. This classifier goes through a process of data collection, preprocessing, and training. Firstly, relevant data regarding patients' breast tissue characteristics, such as cell size, shape, and other diagnostic measurements, are collected. After that, the collected data undergo preprocessing steps, which may include handling missing values, normalization, and feature extraction. Once the data is prepared, it is used to train a machine learning model, such as a neural network or a support vector machine (SVM). During the training phase, the model learns the patterns and relationships within the data to classify breast tissue samples as malignant or benign accurately. Finally, the trained model can be deployed to predict whether a new tissue sample is indicative of cancer, providing valuable assistance to medical professionals in early detection and diagnosis.
### Model Description
- Developed by: Anezatra Katedram
- Model type: Text Classification
- License: GNU General Public License v3.0
- This model was trained by Anezatra and designed with the keras module.
- Use of the model for any commercial purpose requires agreement. Contact TR for more information.
- Model Description: Breast Cancer Detection Using Artificial Intelligence (Trained with Keras module)
- BCWMAI: Resources for more information: [Breast Cancer Wiconsin AI]
| [
"# Breast Cancer Wisconsin v1.0 Model Card\n\nThis study explores the application of artificial intelligence (AI) in detecting breast cancer using the Wisconsin Breast Cancer dataset. Machine learning algorithms are trained on clinical and diagnostic features extracted from breast tissue samples to classify them as benign or malignant. Results demonstrate the effectiveness of AI-based approaches in accurately identifying breast cancer, offering potential improvements in early detection and treatment outcomes.\n\nBelow is a visualization:\n\n!examples\n\nA machine learning classifier trained on the breast cancer dataset can assist in diagnosing breast cancer by analyzing various features of the breast tissue. This classifier goes through a process of data collection, preprocessing, and training. Firstly, relevant data regarding patients' breast tissue characteristics, such as cell size, shape, and other diagnostic measurements, are collected. After that, the collected data undergo preprocessing steps, which may include handling missing values, normalization, and feature extraction. Once the data is prepared, it is used to train a machine learning model, such as a neural network or a support vector machine (SVM). During the training phase, the model learns the patterns and relationships within the data to classify breast tissue samples as malignant or benign accurately. Finally, the trained model can be deployed to predict whether a new tissue sample is indicative of cancer, providing valuable assistance to medical professionals in early detection and diagnosis.",
"### Model Description\n\n- Developed by: Anezatra Katedram\n- Model type: Text Classification\n- License: GNU General Public License v3.0\n - This model was trained by Anezatra and designed with the keras module.\n - Use of the model for any commercial purpose requires agreement. Contact TR for more information. \n\n- Model Description: Breast Cancer Detection Using Artificial Intelligence (Trained with Keras module)\n- BCWMAI: Resources for more information: [Breast Cancer Wiconsin AI]"
] | [
"TAGS\n#keras #medical #text-classification #en #dataset-AiresPucrs/breast-cancer-wisconsin #license-gpl-3.0 #region-us \n",
"# Breast Cancer Wisconsin v1.0 Model Card\n\nThis study explores the application of artificial intelligence (AI) in detecting breast cancer using the Wisconsin Breast Cancer dataset. Machine learning algorithms are trained on clinical and diagnostic features extracted from breast tissue samples to classify them as benign or malignant. Results demonstrate the effectiveness of AI-based approaches in accurately identifying breast cancer, offering potential improvements in early detection and treatment outcomes.\n\nBelow is a visualization:\n\n!examples\n\nA machine learning classifier trained on the breast cancer dataset can assist in diagnosing breast cancer by analyzing various features of the breast tissue. This classifier goes through a process of data collection, preprocessing, and training. Firstly, relevant data regarding patients' breast tissue characteristics, such as cell size, shape, and other diagnostic measurements, are collected. After that, the collected data undergo preprocessing steps, which may include handling missing values, normalization, and feature extraction. Once the data is prepared, it is used to train a machine learning model, such as a neural network or a support vector machine (SVM). During the training phase, the model learns the patterns and relationships within the data to classify breast tissue samples as malignant or benign accurately. Finally, the trained model can be deployed to predict whether a new tissue sample is indicative of cancer, providing valuable assistance to medical professionals in early detection and diagnosis.",
"### Model Description\n\n- Developed by: Anezatra Katedram\n- Model type: Text Classification\n- License: GNU General Public License v3.0\n - This model was trained by Anezatra and designed with the keras module.\n - Use of the model for any commercial purpose requires agreement. Contact TR for more information. \n\n- Model Description: Breast Cancer Detection Using Artificial Intelligence (Trained with Keras module)\n- BCWMAI: Resources for more information: [Breast Cancer Wiconsin AI]"
] | [
45,
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"passage: TAGS\n#keras #medical #text-classification #en #dataset-AiresPucrs/breast-cancer-wisconsin #license-gpl-3.0 #region-us \n# Breast Cancer Wisconsin v1.0 Model Card\n\nThis study explores the application of artificial intelligence (AI) in detecting breast cancer using the Wisconsin Breast Cancer dataset. Machine learning algorithms are trained on clinical and diagnostic features extracted from breast tissue samples to classify them as benign or malignant. Results demonstrate the effectiveness of AI-based approaches in accurately identifying breast cancer, offering potential improvements in early detection and treatment outcomes.\n\nBelow is a visualization:\n\n!examples\n\nA machine learning classifier trained on the breast cancer dataset can assist in diagnosing breast cancer by analyzing various features of the breast tissue. This classifier goes through a process of data collection, preprocessing, and training. Firstly, relevant data regarding patients' breast tissue characteristics, such as cell size, shape, and other diagnostic measurements, are collected. After that, the collected data undergo preprocessing steps, which may include handling missing values, normalization, and feature extraction. Once the data is prepared, it is used to train a machine learning model, such as a neural network or a support vector machine (SVM). During the training phase, the model learns the patterns and relationships within the data to classify breast tissue samples as malignant or benign accurately. Finally, the trained model can be deployed to predict whether a new tissue sample is indicative of cancer, providing valuable assistance to medical professionals in early detection and diagnosis.### Model Description\n\n- Developed by: Anezatra Katedram\n- Model type: Text Classification\n- License: GNU General Public License v3.0\n - This model was trained by Anezatra and designed with the keras module.\n - Use of the model for any commercial purpose requires agreement. Contact TR for more information. \n\n- Model Description: Breast Cancer Detection Using Artificial Intelligence (Trained with Keras module)\n- BCWMAI: Resources for more information: [Breast Cancer Wiconsin AI]"
] | [
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] |
null | null | transformers |
## Exllama v2 Quantizations of Prima-Pastacles-7b
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/Test157t/Prima-Pastacles-7b
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/Prima-Pastacles-7b-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/Prima-Pastacles-7b-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/bartowski/Prima-Pastacles-7b-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/bartowski/Prima-Pastacles-7b-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/Prima-Pastacles-7b-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Prima-Pastacles-7b-exl2 Prima-Pastacles-7b-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Prima-Pastacles-7b-exl2`:
```shell
mkdir Prima-Pastacles-7b-exl2
huggingface-cli download bartowski/Prima-Pastacles-7b-exl2 --local-dir Prima-Pastacles-7b-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir Prima-Pastacles-7b-exl2-6_5
huggingface-cli download bartowski/Prima-Pastacles-7b-exl2 --revision 6_5 --local-dir Prima-Pastacles-7b-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir Prima-Pastacles-7b-exl2-6.5
huggingface-cli download bartowski/Prima-Pastacles-7b-exl2 --revision 6_5 --local-dir Prima-Pastacles-7b-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski | {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Locutusque/Hercules-2.5-Mistral-7B", "Test157t/Pasta-PrimaMaid-7b"], "quantized_by": "bartowski", "pipeline_tag": "text-generation"} | text-generation | bartowski/Prima-Pastacles-7b-exl2 | [
"transformers",
"mergekit",
"merge",
"text-generation",
"base_model:Locutusque/Hercules-2.5-Mistral-7B",
"base_model:Test157t/Pasta-PrimaMaid-7b",
"endpoints_compatible",
"region:us"
] | 2024-02-13T15:11:17+00:00 | [] | [] | TAGS
#transformers #mergekit #merge #text-generation #base_model-Locutusque/Hercules-2.5-Mistral-7B #base_model-Test157t/Pasta-PrimaMaid-7b #endpoints_compatible #region-us
| Exllama v2 Quantizations of Prima-Pastacles-7b
----------------------------------------------
Using <a href="URL ExLlamaV2 v0.0.13 for quantization.
**The "main" branch only contains the URL, download one of the other branches for the model (see below)**
Each branch contains an individual bits per weight, with the main one containing only the URL for further conversions.
Original model: URL
Download instructions
---------------------
With git:
With huggingface hub (credit to TheBloke for instructions):
To download the 'main' (only useful if you only care about URL) branch to a folder called 'Prima-Pastacles-7b-exl2':
To download from a different branch, add the '--revision' parameter:
Linux:
Windows (which apparently doesn't like \_ in folders sometimes?):
Want to support my work? Visit my ko-fi page here: URL
| [] | [
"TAGS\n#transformers #mergekit #merge #text-generation #base_model-Locutusque/Hercules-2.5-Mistral-7B #base_model-Test157t/Pasta-PrimaMaid-7b #endpoints_compatible #region-us \n"
] | [
66
] | [
"passage: TAGS\n#transformers #mergekit #merge #text-generation #base_model-Locutusque/Hercules-2.5-Mistral-7B #base_model-Test157t/Pasta-PrimaMaid-7b #endpoints_compatible #region-us \n"
] | [
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] |
null | null | espnet |
## ESPnet2 SPK model
### `espnet/voxcelebs12devs_librispeech_cv16fa_rawnet3`
This model was trained by Alexgichamba using sdsv21 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout 7ffe306553b905c97948a1d4926132000ee2e1be
pip install -e .
cd egs2/voxceleb/spk1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/voxcelebs12devs_voxblinkfull_rawnet3
```
<!-- Generated by scripts/utils/show_spk_result.py -->
# RESULTS
## Environments
date: 2024-02-08 08:51:46.908694
- python version: 3.8.6 (default, Dec 17 2020, 16:57:01) [GCC 10.2.0]
- espnet version: 202310
- pytorch version: 2.0.1+cu118
## Test set: Vox1-O
| Model (conf name) | EER(%) | minDCF |
|---|---|---|
| [conf/train_rawnet3.yaml](conf/train_rawnet3.yaml) | 1.229 | 0.08033 |
## Test set: Sample DeepMine
| Model (conf name) | EER(%) | minDCF |
|---|---|---|
| [conf/train_rawnet3.yaml](conf/train_rawnet3.yaml) | 4.640 | 0.25994 |
## SPK config
<details><summary>expand</summary>
```
config: conf/train_rawnet3.yaml
print_config: false
log_level: INFO
drop_last_iter: true
dry_run: false
iterator_type: category
valid_iterator_type: sequence
output_dir: exp/spk_train_rawnet3_raw_sp
ngpu: 1
seed: 0
num_workers: 2
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 37073
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: true
cudnn_deterministic: false
collect_stats: false
write_collected_feats: false
max_epoch: 40
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- eer
- min
keep_nbest_models: 3
nbest_averaging_interval: 0
grad_clip: 9999
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: 100
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
use_lora: false
save_lora_only: true
lora_conf: {}
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 128
valid_batch_size: 40
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/spk_stats_16k_sp/train/speech_shape
valid_shape_file:
- exp/spk_stats_16k_sp/valid/speech_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 120000
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
chunk_default_fs: null
train_data_path_and_name_and_type:
- - dump/raw/combined_train_set_sp/wav.scp
- speech
- sound
- - dump/raw/combined_train_set_sp/utt2spk
- spk_labels
- text
valid_data_path_and_name_and_type:
- - dump/raw/voxceleb1_test/trial.scp
- speech
- sound
- - dump/raw/voxceleb1_test/trial2.scp
- speech2
- sound
- - dump/raw/voxceleb1_test/trial_label
- spk_labels
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
allow_multi_rates: false
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
lr: 0.001
weight_decay: 5.0e-05
amsgrad: false
scheduler: cosineannealingwarmuprestarts
scheduler_conf:
first_cycle_steps: 158760
cycle_mult: 1.0
max_lr: 0.001
min_lr: 5.0e-06
warmup_steps: 1000
gamma: 0.75
init: null
use_preprocessor: true
input_size: null
target_duration: 3.0
spk2utt: dump/raw/combined_train_set_sp/spk2utt
spk_num: 37485
sample_rate: 16000
num_eval: 10
rir_scp: ''
model_conf:
extract_feats_in_collect_stats: false
frontend: asteroid_frontend
frontend_conf:
sinc_stride: 16
sinc_kernel_size: 251
sinc_filters: 256
preemph_coef: 0.97
log_term: 1.0e-06
specaug: null
specaug_conf: {}
normalize: null
normalize_conf: {}
encoder: rawnet3
encoder_conf:
model_scale: 8
ndim: 1024
output_size: 1536
pooling: chn_attn_stat
pooling_conf: {}
projector: rawnet3
projector_conf:
output_size: 192
preprocessor: spk
preprocessor_conf:
target_duration: 3.0
sample_rate: 16000
num_eval: 5
noise_apply_prob: 0.5
noise_info:
- - 1.0
- dump/raw/musan_speech.scp
- - 4
- 7
- - 13
- 20
- - 1.0
- dump/raw/musan_noise.scp
- - 1
- 1
- - 0
- 15
- - 1.0
- dump/raw/musan_music.scp
- - 1
- 1
- - 5
- 15
rir_apply_prob: 0.5
rir_scp: dump/raw/rirs.scp
loss: aamsoftmax_sc_topk
loss_conf:
margin: 0.3
scale: 30
K: 3
mp: 0.06
k_top: 5
required:
- output_dir
version: '202310'
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | {"language": "multilingual", "license": "cc-by-4.0", "tags": ["espnet", "audio", "speaker-recognition"], "datasets": ["voxceleb", "librispeech", "commonvoice"]} | null | espnet/voxcelebs12devs_librispeech_cv16fa_rawnet3 | [
"espnet",
"audio",
"speaker-recognition",
"multilingual",
"dataset:voxceleb",
"dataset:librispeech",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | 2024-02-13T15:11:25+00:00 | [
"1804.00015"
] | [
"multilingual"
] | TAGS
#espnet #audio #speaker-recognition #multilingual #dataset-voxceleb #dataset-librispeech #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 SPK model
-----------------
### 'espnet/voxcelebs12devs\_librispeech\_cv16fa\_rawnet3'
This model was trained by Alexgichamba using sdsv21 recipe in espnet.
### Demo: How to use in ESPnet2
Follow the ESPnet installation instructions
if you haven't done that already.
RESULTS
=======
Environments
------------
date: 2024-02-08 08:51:46.908694
* python version: 3.8.6 (default, Dec 17 2020, 16:57:01) [GCC 10.2.0]
* espnet version: 202310
* pytorch version: 2.0.1+cu118
Test set: Vox1-O
----------------
Model (conf name): conf/train\_rawnet3.yaml, EER(%): 1.229, minDCF: 0.08033
Test set: Sample DeepMine
-------------------------
Model (conf name): conf/train\_rawnet3.yaml, EER(%): 4.640, minDCF: 0.25994
SPK config
----------
expand
### Citing ESPnet
or arXiv:
| [
"### 'espnet/voxcelebs12devs\\_librispeech\\_cv16fa\\_rawnet3'\n\n\nThis model was trained by Alexgichamba using sdsv21 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nFollow the ESPnet installation instructions\nif you haven't done that already.\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\ndate: 2024-02-08 08:51:46.908694\n\n\n* python version: 3.8.6 (default, Dec 17 2020, 16:57:01) [GCC 10.2.0]\n* espnet version: 202310\n* pytorch version: 2.0.1+cu118\n\n\nTest set: Vox1-O\n----------------\n\n\nModel (conf name): conf/train\\_rawnet3.yaml, EER(%): 1.229, minDCF: 0.08033\n\n\nTest set: Sample DeepMine\n-------------------------\n\n\nModel (conf name): conf/train\\_rawnet3.yaml, EER(%): 4.640, minDCF: 0.25994\n\n\nSPK config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #speaker-recognition #multilingual #dataset-voxceleb #dataset-librispeech #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/voxcelebs12devs\\_librispeech\\_cv16fa\\_rawnet3'\n\n\nThis model was trained by Alexgichamba using sdsv21 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nFollow the ESPnet installation instructions\nif you haven't done that already.\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\ndate: 2024-02-08 08:51:46.908694\n\n\n* python version: 3.8.6 (default, Dec 17 2020, 16:57:01) [GCC 10.2.0]\n* espnet version: 202310\n* pytorch version: 2.0.1+cu118\n\n\nTest set: Vox1-O\n----------------\n\n\nModel (conf name): conf/train\\_rawnet3.yaml, EER(%): 1.229, minDCF: 0.08033\n\n\nTest set: Sample DeepMine\n-------------------------\n\n\nModel (conf name): conf/train\\_rawnet3.yaml, EER(%): 4.640, minDCF: 0.25994\n\n\nSPK config\n----------\n\n\nexpand",
"### Citing ESPnet\n\n\nor arXiv:"
] | [
65,
51,
187,
11
] | [
"passage: TAGS\n#espnet #audio #speaker-recognition #multilingual #dataset-voxceleb #dataset-librispeech #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n### 'espnet/voxcelebs12devs\\_librispeech\\_cv16fa\\_rawnet3'\n\n\nThis model was trained by Alexgichamba using sdsv21 recipe in espnet.### Demo: How to use in ESPnet2\n\n\nFollow the ESPnet installation instructions\nif you haven't done that already.\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\ndate: 2024-02-08 08:51:46.908694\n\n\n* python version: 3.8.6 (default, Dec 17 2020, 16:57:01) [GCC 10.2.0]\n* espnet version: 202310\n* pytorch version: 2.0.1+cu118\n\n\nTest set: Vox1-O\n----------------\n\n\nModel (conf name): conf/train\\_rawnet3.yaml, EER(%): 1.229, minDCF: 0.08033\n\n\nTest set: Sample DeepMine\n-------------------------\n\n\nModel (conf name): conf/train\\_rawnet3.yaml, EER(%): 4.640, minDCF: 0.25994\n\n\nSPK config\n----------\n\n\nexpand### Citing ESPnet\n\n\nor arXiv:"
] | [
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null | null | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "30.90 +/- 24.57", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | mathreader/Reinforce-Pixelcopter-PLE-v0 | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2024-02-13T15:12:35+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
41,
58
] | [
"passage: TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
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null | null | transformers |
# Model Card for Model ID
Amber fine tuned model used share gpt 90k.
## Model Details
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[More Information Needed] | {"license": "apache-2.0"} | text-generation | Lvxy1117/amber_fine_tune_sgall | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T15:13:52+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
Amber fine tuned model used share gpt 90k.
## Model Details
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- Shared by [optional]:
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- Demo [optional]:
## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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## Evaluation
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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[optional]
BibTeX:
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## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID\n\nAmber fine tuned model used share gpt 90k.",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID\n\nAmber fine tuned model used share gpt 90k.",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID\n\nAmber fine tuned model used share gpt 90k.## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | diffusers | ### MyEpxWithAI Dreambooth model trained by susanth73825 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 21KT1A0595
Sample pictures of this concept:
| {"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]} | text-to-image | susanth73825/myepxwithai | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-13T15:14:51+00:00 | [] | [] | TAGS
#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
| ### MyEpxWithAI Dreambooth model trained by susanth73825 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 21KT1A0595
Sample pictures of this concept:
| [
"### MyEpxWithAI Dreambooth model trained by susanth73825 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 21KT1A0595\n\nSample pictures of this concept:"
] | [
"TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"### MyEpxWithAI Dreambooth model trained by susanth73825 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 21KT1A0595\n\nSample pictures of this concept:"
] | [
73,
54
] | [
"passage: TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### MyEpxWithAI Dreambooth model trained by susanth73825 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 21KT1A0595\n\nSample pictures of this concept:"
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null | null | diffusers | ### My-Pet-Dog Dreambooth model trained by sunitha525 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 21KT1A0525
Sample pictures of this concept:
| {"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]} | text-to-image | sunitha525/my-pet-dog | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-13T15:15:01+00:00 | [] | [] | TAGS
#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
| ### My-Pet-Dog Dreambooth model trained by sunitha525 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 21KT1A0525
Sample pictures of this concept:
| [
"### My-Pet-Dog Dreambooth model trained by sunitha525 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 21KT1A0525\n\nSample pictures of this concept:"
] | [
"TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"### My-Pet-Dog Dreambooth model trained by sunitha525 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 21KT1A0525\n\nSample pictures of this concept:"
] | [
73,
56
] | [
"passage: TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### My-Pet-Dog Dreambooth model trained by sunitha525 following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 21KT1A0525\n\nSample pictures of this concept:"
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null | null | transformers |
# Model Card for Model ID
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# Model Card for Model ID
## Model Details
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## Uses
### Direct Use
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## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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Use the code below to get started with the model.
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### Training Data
### Training Procedure
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## Evaluation
### Testing Data, Factors & Metrics
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### Results
#### Summary
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null | null | timm | # Model card for DERETFound_DR_IDRID
| {"license": "apache-2.0", "library_name": "timm", "tags": ["image-classification", "timm"]} | image-classification | seige-ml/DERETFound_DR_IDRID | [
"timm",
"pytorch",
"image-classification",
"license:apache-2.0",
"region:us"
] | 2024-02-13T15:15:42+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #license-apache-2.0 #region-us
| # Model card for DERETFound_DR_IDRID
| [
"# Model card for DERETFound_DR_IDRID"
] | [
"TAGS\n#timm #pytorch #image-classification #license-apache-2.0 #region-us \n",
"# Model card for DERETFound_DR_IDRID"
] | [
26,
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null | null | peft |
# Model Card for Model ID
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"} | null | Elkhayyat17/peft-tinyllama-Med | [
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# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
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null | null | transformers |
# turkish-gpt2-medium
This is a Turkish GPT-2-medium model. GPT-2 is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner.
Due to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.
## Example Usage
```python
from transformers import AutoTokenizer, GPT2LMHeadModel
from transformers import pipeline
model = GPT2LMHeadModel.from_pretrained("ytu-ce-cosmos/turkish-gpt2-medium")
tokenizer = AutoTokenizer.from_pretrained("ytu-ce-cosmos/turkish-gpt2-medium")
text_generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
r = text_generator("Teknolojinin gelişimi hayatımızı önemli ölçüde etkiledi. ", max_length=100)
[{'generated_text': 'Teknolojinin gelişimi hayatımızı önemli ölçüde etkiledi. "Teknoloji hayatın merkezindeyse, insan hayatında da önemli bir yere sahip demektir!" diyerek devam edelim.}]
```
# Acknowledgments
- Research supported with Cloud TPUs from [Google's TensorFlow Research Cloud](https://sites.research.google/trc/about/) (TFRC). Thanks for providing access to the TFRC ❤️
- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage 🤗
# Citation
Paper coming soon 😊
| {"language": ["tr"], "license": "mit", "tags": ["Turkish", "turkish", "gpt2"], "datasets": ["uonlp/CulturaX"], "pipeline_tag": "text-generation"} | text-generation | ytu-ce-cosmos/turkish-gpt2-medium | [
"transformers",
"pytorch",
"tf",
"safetensors",
"gpt2",
"text-generation",
"Turkish",
"turkish",
"tr",
"dataset:uonlp/CulturaX",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T15:16:52+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #safetensors #gpt2 #text-generation #Turkish #turkish #tr #dataset-uonlp/CulturaX #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# turkish-gpt2-medium
This is a Turkish GPT-2-medium model. GPT-2 is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner.
Due to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.
## Example Usage
# Acknowledgments
- Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ️
- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage
Paper coming soon
| [
"# turkish-gpt2-medium\n\nThis is a Turkish GPT-2-medium model. GPT-2 is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner.\nDue to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.",
"## Example Usage",
"# Acknowledgments\n- Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ️\n- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage \n\nPaper coming soon"
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #gpt2 #text-generation #Turkish #turkish #tr #dataset-uonlp/CulturaX #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# turkish-gpt2-medium\n\nThis is a Turkish GPT-2-medium model. GPT-2 is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner.\nDue to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.",
"## Example Usage",
"# Acknowledgments\n- Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ️\n- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage \n\nPaper coming soon"
] | [
81,
103,
5,
68
] | [
"passage: TAGS\n#transformers #pytorch #tf #safetensors #gpt2 #text-generation #Turkish #turkish #tr #dataset-uonlp/CulturaX #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# turkish-gpt2-medium\n\nThis is a Turkish GPT-2-medium model. GPT-2 is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner.\nDue to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.## Example Usage# Acknowledgments\n- Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ️\n- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage \n\nPaper coming soon"
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null | null | transformers |
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# wav2vec2-300m-england-0214-parallel-8-23-avatar
This model is a fine-tuned version of [vitouphy/wav2vec2-xls-r-300m-english](https://huggingface.co/vitouphy/wav2vec2-xls-r-300m-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3170
- Wer: 0.1605
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1227
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.4028 | 1.0 | 1227 | 0.2310 | 0.2257 |
| 0.2259 | 2.0 | 2454 | 0.1990 | 0.1982 |
| 0.1796 | 3.0 | 3681 | 0.1938 | 0.1857 |
| 0.1449 | 4.0 | 4908 | 0.1869 | 0.1783 |
| 0.1181 | 5.0 | 6135 | 0.1890 | 0.1716 |
| 0.0985 | 6.0 | 7362 | 0.1907 | 0.1678 |
| 0.0802 | 7.0 | 8589 | 0.1990 | 0.1670 |
| 0.0658 | 8.0 | 9816 | 0.2027 | 0.1647 |
| 0.0533 | 9.0 | 11043 | 0.2221 | 0.1640 |
| 0.0422 | 10.0 | 12270 | 0.2258 | 0.1625 |
| 0.0331 | 11.0 | 13497 | 0.2479 | 0.1621 |
| 0.0263 | 12.0 | 14724 | 0.2680 | 0.1617 |
| 0.0199 | 13.0 | 15951 | 0.2849 | 0.1603 |
| 0.0155 | 14.0 | 17178 | 0.3052 | 0.1614 |
| 0.0122 | 15.0 | 18405 | 0.3170 | 0.1605 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.14.7
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "vitouphy/wav2vec2-xls-r-300m-english", "model-index": [{"name": "wav2vec2-300m-england-0214-parallel-8-23-avatar", "results": []}]} | automatic-speech-recognition | Lin25/wav2vec2-300m-england-0214-parallel-8-23-avatar | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:vitouphy/wav2vec2-xls-r-300m-english",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-13T15:17:13+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-300m-england-0214-parallel-8-23-avatar
===============================================
This model is a fine-tuned version of vitouphy/wav2vec2-xls-r-300m-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3170
* Wer: 0.1605
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1227
* num\_epochs: 15
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.36.0.dev0
* Pytorch 1.12.1+cu113
* Datasets 2.14.7
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1227\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 1.12.1+cu113\n* Datasets 2.14.7\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1227\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 1.12.1+cu113\n* Datasets 2.14.7\n* Tokenizers 0.15.0"
] | [
80,
159,
4,
40
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1227\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 1.12.1+cu113\n* Datasets 2.14.7\n* Tokenizers 0.15.0"
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] |
null | null | null | # Deepreneur-blue-lizard-gguf
[Deepreneurさんが公開しているblue-lizard](https://huggingface.co/Deepreneur/blue-lizard)のggufフォーマット変換版です。
モデルサイズは7Bになります。
## Usage
```
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make -j
./main -m 'Deepreneur-blue-lizard-q4_0.gguf' -p "<s>[INST] <<SYS>>\nあなたは誠実で優秀な日本人のアシスタントです。\n<</SYS>>\n\ndeepreneurについて教えて [/INST]" -n 512
```
## ライセンス
元モデルのライセンス表記に則り、llama2ライセンスになります
| {"language": ["en", "ja"], "license": "llama2", "tags": ["llama2"]} | null | mmnga/Deepreneur-blue-lizard-gguf | [
"gguf",
"llama2",
"en",
"ja",
"license:llama2",
"region:us"
] | 2024-02-13T15:18:15+00:00 | [] | [
"en",
"ja"
] | TAGS
#gguf #llama2 #en #ja #license-llama2 #region-us
| # Deepreneur-blue-lizard-gguf
Deepreneurさんが公開しているblue-lizardのggufフォーマット変換版です。
モデルサイズは7Bになります。
## Usage
## ライセンス
元モデルのライセンス表記に則り、llama2ライセンスになります
| [
"# Deepreneur-blue-lizard-gguf\nDeepreneurさんが公開しているblue-lizardのggufフォーマット変換版です。\nモデルサイズは7Bになります。",
"## Usage",
"## ライセンス\n元モデルのライセンス表記に則り、llama2ライセンスになります"
] | [
"TAGS\n#gguf #llama2 #en #ja #license-llama2 #region-us \n",
"# Deepreneur-blue-lizard-gguf\nDeepreneurさんが公開しているblue-lizardのggufフォーマット変換版です。\nモデルサイズは7Bになります。",
"## Usage",
"## ライセンス\n元モデルのライセンス表記に則り、llama2ライセンスになります"
] | [
24,
40,
3,
25
] | [
"passage: TAGS\n#gguf #llama2 #en #ja #license-llama2 #region-us \n# Deepreneur-blue-lizard-gguf\nDeepreneurさんが公開しているblue-lizardのggufフォーマット変換版です。\nモデルサイズは7Bになります。## Usage## ライセンス\n元モデルのライセンス表記に則り、llama2ライセンスになります"
] | [
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] |
null | null | ml-agents |
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: hugo-massonnat/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget"]} | reinforcement-learning | hugo-massonnat/ppo-SnowballTarget | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | 2024-02-13T15:19:17+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us
|
# ppo Agent playing SnowballTarget
This is a trained model of a ppo agent playing SnowballTarget
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your
browser: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### Watch your Agent play
You can watch your agent playing directly in your browser
1. If the environment is part of ML-Agents official environments, go to URL
2. Step 1: Find your model_id: hugo-massonnat/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: hugo-massonnat/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n",
"# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: hugo-massonnat/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
50,
209
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: hugo-massonnat/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | AntoineGourru/Mistral_qlora_drome_2 | [
"transformers",
"safetensors",
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"1910.09700"
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#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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- Training regime:
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## Evaluation
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- Hardware Type:
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null | null | peft |
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "facebook/opt-350m"} | null | alitolga/627_facebook_opt-350m_PrefixTuning | [
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# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
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APA:
## Glossary [optional]
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## Model Card Contact
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] |
null | null | transformers |
# This model is now live (We'll always be serving the newest model on our web app)!
Access at: https://www.whiterabbitneo.com/
# Our Discord Server
Join us at: https://discord.gg/8Ynkrcbk92 (Updated on Dec 29th. Now permanent link to join)
# DeepSeek Coder Licence + WhiteRabbitNeo Extended Version
# Licence: Usage Restrictions
```
You agree not to use the Model or Derivatives of the Model:
- In any way that violates any applicable national or international law or regulation or infringes upon the lawful rights and interests of any third party;
- For military use in any way;
- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
- To generate or disseminate inappropriate content subject to applicable regulatory requirements;
- To generate or disseminate personal identifiable information without due authorization or for unreasonable use;
- To defame, disparage or otherwise harass others;
- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories.
```
# Topics Covered:
```
- Open Ports: Identifying open ports is crucial as they can be entry points for attackers. Common ports to check include HTTP (80, 443), FTP (21), SSH (22), and SMB (445).
- Outdated Software or Services: Systems running outdated software or services are often vulnerable to exploits. This includes web servers, database servers, and any third-party software.
- Default Credentials: Many systems and services are installed with default usernames and passwords, which are well-known and can be easily exploited.
- Misconfigurations: Incorrectly configured services, permissions, and security settings can introduce vulnerabilities.
- Injection Flaws: SQL injection, command injection, and cross-site scripting (XSS) are common issues in web applications.
- Unencrypted Services: Services that do not use encryption (like HTTP instead of HTTPS) can expose sensitive data.
- Known Software Vulnerabilities: Checking for known vulnerabilities in software using databases like the National Vulnerability Database (NVD) or tools like Nessus or OpenVAS.
- Cross-Site Request Forgery (CSRF): This is where unauthorized commands are transmitted from a user that the web application trusts.
- Insecure Direct Object References: This occurs when an application provides direct access to objects based on user-supplied input.
- Security Misconfigurations in Web Servers/Applications: This includes issues like insecure HTTP headers or verbose error messages that reveal too much information.
- Broken Authentication and Session Management: This can allow attackers to compromise passwords, keys, or session tokens, or to exploit other implementation flaws to assume other users' identities.
- Sensitive Data Exposure: Includes vulnerabilities that expose sensitive data, such as credit card numbers, health records, or personal information.
- API Vulnerabilities: In modern web applications, APIs are often used and can have vulnerabilities like insecure endpoints or data leakage.
- Denial of Service (DoS) Vulnerabilities: Identifying services that are vulnerable to DoS attacks, which can make the resource unavailable to legitimate users.
- Buffer Overflows: Common in older software, these vulnerabilities can allow an attacker to crash the system or execute arbitrary code.
```
# WhiteRabbitNeo
<br>
<br>
WhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity.
Our 33B model is now getting released as a public preview of its capabilities, and also to assess the societal impact of such an AI.
```python
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "whiterabbitneo/WhiteRabbitNeo-33B-v-1"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_4bit=False,
load_in_8bit=True,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.5,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: You are an AI that code. Answer with code."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
# print(conversation)
json_data = {"prompt": user_input, "answer": answer}
# print(json_data)
# with open(output_file_path, "a") as output_file:
# output_file.write(json.dumps(json_data) + "\n")
```
| {"license": "other", "license_name": "deepseek", "license_link": "https://huggingface.co/deepseek-ai/deepseek-coder-33b-base/blob/main/LICENSE"} | text-generation | ExAi/WhiteRabbitNeo-33B-v1.5-exl2-3.0 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T15:20:57+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# This model is now live (We'll always be serving the newest model on our web app)!
Access at: URL
# Our Discord Server
Join us at: URL (Updated on Dec 29th. Now permanent link to join)
# DeepSeek Coder Licence + WhiteRabbitNeo Extended Version
# Licence: Usage Restrictions
# Topics Covered:
# WhiteRabbitNeo
<br>
!WhiteRabbitNeo
<br>
WhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity.
Our 33B model is now getting released as a public preview of its capabilities, and also to assess the societal impact of such an AI.
| [
"# This model is now live (We'll always be serving the newest model on our web app)!\n Access at: URL",
"# Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# DeepSeek Coder Licence + WhiteRabbitNeo Extended Version",
"# Licence: Usage Restrictions",
"# Topics Covered:",
"# WhiteRabbitNeo\n\n<br>\n\n!WhiteRabbitNeo\n\n<br>\n\nWhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity. \n\nOur 33B model is now getting released as a public preview of its capabilities, and also to assess the societal impact of such an AI."
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# This model is now live (We'll always be serving the newest model on our web app)!\n Access at: URL",
"# Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# DeepSeek Coder Licence + WhiteRabbitNeo Extended Version",
"# Licence: Usage Restrictions",
"# Topics Covered:",
"# WhiteRabbitNeo\n\n<br>\n\n!WhiteRabbitNeo\n\n<br>\n\nWhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity. \n\nOur 33B model is now getting released as a public preview of its capabilities, and also to assess the societal impact of such an AI."
] | [
56,
28,
24,
19,
9,
6,
75
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# This model is now live (We'll always be serving the newest model on our web app)!\n Access at: URL# Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)# DeepSeek Coder Licence + WhiteRabbitNeo Extended Version# Licence: Usage Restrictions# Topics Covered:# WhiteRabbitNeo\n\n<br>\n\n!WhiteRabbitNeo\n\n<br>\n\nWhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity. \n\nOur 33B model is now getting released as a public preview of its capabilities, and also to assess the societal impact of such an AI."
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | kenchenxingyu/flan-large-single-label-emotion-human3 | [
"transformers",
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#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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## Uses
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### Out-of-Scope Use
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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Use the code below to get started with the model.
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### Training Procedure
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#### Speeds, Sizes, Times [optional]
## Evaluation
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#### Factors
#### Metrics
### Results
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | manishjha24/bloom-560m-qa-ds-guild | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-13T15:27:35+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Uses",
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"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chat_700STEPS_1e4rate
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1848
- Rewards/chosen: -4.4236
- Rewards/rejected: -4.3538
- Rewards/accuracies: 0.4000
- Rewards/margins: -0.0698
- Logps/rejected: -62.3289
- Logps/chosen: -60.9807
- Logits/rejected: -4.5000
- Logits/chosen: -4.5000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 700
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 1.2329 | 0.1 | 50 | 1.6243 | -8.0715 | -8.0351 | 0.4176 | -0.0364 | -99.1426 | -97.4598 | -0.6161 | -0.6164 |
| 1.3399 | 0.2 | 100 | 1.2323 | -4.6270 | -4.6104 | 0.3978 | -0.0167 | -64.8951 | -63.0151 | -2.3014 | -2.3014 |
| 1.316 | 0.29 | 150 | 1.2017 | -4.3807 | -4.3323 | 0.4000 | -0.0484 | -62.1140 | -60.5517 | -2.9891 | -2.9891 |
| 1.2778 | 0.39 | 200 | 1.1891 | -4.3216 | -4.2623 | 0.4044 | -0.0593 | -61.4138 | -59.9605 | -3.4116 | -3.4116 |
| 1.0721 | 0.49 | 250 | 1.1847 | -4.3234 | -4.2565 | 0.4110 | -0.0669 | -61.3561 | -59.9788 | -3.7977 | -3.7977 |
| 1.3775 | 0.59 | 300 | 1.1896 | -4.3481 | -4.2745 | 0.4176 | -0.0737 | -61.5360 | -60.2260 | -4.0911 | -4.0911 |
| 1.3232 | 0.68 | 350 | 1.1818 | -4.3486 | -4.2846 | 0.4088 | -0.0640 | -61.6375 | -60.2310 | -4.2490 | -4.2490 |
| 1.2476 | 0.78 | 400 | 1.1789 | -4.3705 | -4.3105 | 0.4044 | -0.0600 | -61.8961 | -60.4495 | -4.1920 | -4.1920 |
| 1.3082 | 0.88 | 450 | 1.1766 | -4.3831 | -4.3296 | 0.4000 | -0.0535 | -62.0870 | -60.5753 | -4.2348 | -4.2348 |
| 1.1007 | 0.98 | 500 | 1.1762 | -4.4022 | -4.3470 | 0.4000 | -0.0552 | -62.2610 | -60.7662 | -4.2010 | -4.2010 |
| 0.8786 | 1.07 | 550 | 1.1811 | -4.4150 | -4.3527 | 0.4000 | -0.0623 | -62.3185 | -60.8948 | -4.3351 | -4.3351 |
| 1.3113 | 1.17 | 600 | 1.1842 | -4.4174 | -4.3487 | 0.4000 | -0.0687 | -62.2785 | -60.9186 | -4.4858 | -4.4858 |
| 0.9783 | 1.27 | 650 | 1.1850 | -4.4234 | -4.3533 | 0.4000 | -0.0701 | -62.3242 | -60.9783 | -4.4997 | -4.4997 |
| 1.3696 | 1.37 | 700 | 1.1848 | -4.4236 | -4.3538 | 0.4000 | -0.0698 | -62.3289 | -60.9807 | -4.5000 | -4.5000 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0+cu117
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "chat_700STEPS_1e4rate", "results": []}]} | text-generation | tsavage68/chat_700STEPS_1e4rate_01beta_DPO | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T15:29:46+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #trl #dpo #generated_from_trainer #conversational #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| chat\_700STEPS\_1e4rate
=======================
This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1848
* Rewards/chosen: -4.4236
* Rewards/rejected: -4.3538
* Rewards/accuracies: 0.4000
* Rewards/margins: -0.0698
* Logps/rejected: -62.3289
* Logps/chosen: -60.9807
* Logits/rejected: -4.5000
* Logits/chosen: -4.5000
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 700
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.0+cu117
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 700",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 700",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
84,
143,
4,
33
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #trl #dpo #generated_from_trainer #conversational #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 700### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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] |
null | null | timm | # Model card for DERETFound_DR_APTOS2019
| {"license": "apache-2.0", "library_name": "timm", "tags": ["image-classification", "timm"]} | image-classification | seige-ml/DERETFound_DR_APTOS2019 | [
"timm",
"pytorch",
"image-classification",
"license:apache-2.0",
"region:us"
] | 2024-02-13T15:30:03+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #license-apache-2.0 #region-us
| # Model card for DERETFound_DR_APTOS2019
| [
"# Model card for DERETFound_DR_APTOS2019"
] | [
"TAGS\n#timm #pytorch #image-classification #license-apache-2.0 #region-us \n",
"# Model card for DERETFound_DR_APTOS2019"
] | [
26,
14
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"passage: TAGS\n#timm #pytorch #image-classification #license-apache-2.0 #region-us \n# Model card for DERETFound_DR_APTOS2019"
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] |
null | null | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-v7", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | yoshq/Reinforce-v7 | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2024-02-13T15:30:49+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
39,
54
] | [
"passage: TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
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] |
null | null | transformers |
# turkish-gpt2
This is a Turkish GPT-2 large model. GPT-2 is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner.
Due to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.
## Example Usage
```python
from transformers import AutoTokenizer, GPT2LMHeadModel
from transformers import pipeline
model = GPT2LMHeadModel.from_pretrained("ytu-ce-cosmos/turkish-gpt2-large")
tokenizer = AutoTokenizer.from_pretrained("ytu-ce-cosmos/turkish-gpt2-large")
text_generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
r = text_generator("Teknolojinin gelişimi hayatımızı önemli ölçüde etkiledi. ", max_length=100)
[{'generated_text': 'Teknolojinin gelişimi hayatımızı önemli ölçüde etkiledi. "Sosyal ağ" adını verdiğimiz yeni bir iletişim çağımız oluştu. '}]
```
# Acknowledgments
- Research supported with Cloud TPUs from [Google's TensorFlow Research Cloud](https://sites.research.google/trc/about/) (TFRC). Thanks for providing access to the TFRC ❤️
- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage 🤗
# Citation
Paper coming soon 😊 | {"language": ["tr"], "license": "mit", "tags": ["Turkish", "turkish", "gpt2"], "datasets": ["uonlp/CulturaX"], "pipeline_tag": "text-generation"} | text-generation | ytu-ce-cosmos/turkish-gpt2-large | [
"transformers",
"pytorch",
"tf",
"safetensors",
"gpt2",
"text-generation",
"Turkish",
"turkish",
"tr",
"dataset:uonlp/CulturaX",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T15:32:26+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #safetensors #gpt2 #text-generation #Turkish #turkish #tr #dataset-uonlp/CulturaX #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# turkish-gpt2
This is a Turkish GPT-2 large model. GPT-2 is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner.
Due to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.
## Example Usage
# Acknowledgments
- Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ️
- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage
Paper coming soon | [
"# turkish-gpt2\n\nThis is a Turkish GPT-2 large model. GPT-2 is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner.\nDue to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.",
"## Example Usage",
"# Acknowledgments\n- Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ️\n- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage \n\nPaper coming soon"
] | [
"TAGS\n#transformers #pytorch #tf #safetensors #gpt2 #text-generation #Turkish #turkish #tr #dataset-uonlp/CulturaX #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# turkish-gpt2\n\nThis is a Turkish GPT-2 large model. GPT-2 is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner.\nDue to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.",
"## Example Usage",
"# Acknowledgments\n- Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ️\n- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage \n\nPaper coming soon"
] | [
81,
99,
5,
68
] | [
"passage: TAGS\n#transformers #pytorch #tf #safetensors #gpt2 #text-generation #Turkish #turkish #tr #dataset-uonlp/CulturaX #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# turkish-gpt2\n\nThis is a Turkish GPT-2 large model. GPT-2 is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner.\nDue to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.## Example Usage# Acknowledgments\n- Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ️\n- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage \n\nPaper coming soon"
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null | null | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga xncy -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga xncy -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga xncy
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "424.00 +/- 231.61", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | xncy/dqn-SpaceInvadersNoFrameskip-v4 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-13T15:33:22+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: URL
SB3: URL
SB3 Contrib: URL
Install the RL Zoo (with SB3 and SB3-Contrib):
If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:
## Training (with the RL Zoo)
## Hyperparameters
# Environment Arguments
| [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
43,
90,
73,
9,
5,
7
] | [
"passage: TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters# Environment Arguments"
] | [
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null | null | transformers |
# TinyNewsLlama-1.1B
TinyNewsLlama-1.1B is a QLoRA SFT fine-tune of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) using a sample of a concentrated version of the [bigNews] (https://paperswithcode.com/dataset/bignews) Dataset. The model was fine-tuned for ~12h on one A100 40GB on ~125M tokens.
The goal of this project is to study the potential for improving the domain-specific (in this case political) knowledge of small (<3B) LLMs by concentrating the training datasets TF-IDF in respect to the underlying Topics found in the origianl Dataset.
The used training data contains political news articles from **The New York Times**, **USA Today** and **The Washington Times**. The concentrated BigNews Dataset as well as more information about the used sample will soon be added.
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "h4rz3rk4s3/TinyNewsLlama-1.1B"
messages = [
{
"role": "system",
"content": "You are a an experienced journalist.",
},
{"role": "user", "content": "Write a short article on Brexit and it's impact on the European Union."},
]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"language": ["en"], "license": "apache-2.0", "tags": ["TinyLlama", "QLoRA", "Politics", "News", "sft"], "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "pipeline_tag": "text-generation"} | text-generation | h4rz3rk4s3/TinyNewsLlama-1.1B | [
"transformers",
"safetensors",
"llama",
"text-generation",
"TinyLlama",
"QLoRA",
"Politics",
"News",
"sft",
"conversational",
"en",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2024-02-13T15:35:44+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #TinyLlama #QLoRA #Politics #News #sft #conversational #en #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# TinyNewsLlama-1.1B
TinyNewsLlama-1.1B is a QLoRA SFT fine-tune of TinyLlama/TinyLlama-1.1B-Chat-v1.0 using a sample of a concentrated version of the [bigNews] (URL Dataset. The model was fine-tuned for ~12h on one A100 40GB on ~125M tokens.
The goal of this project is to study the potential for improving the domain-specific (in this case political) knowledge of small (<3B) LLMs by concentrating the training datasets TF-IDF in respect to the underlying Topics found in the origianl Dataset.
The used training data contains political news articles from The New York Times, USA Today and The Washington Times. The concentrated BigNews Dataset as well as more information about the used sample will soon be added.
## Usage
| [
"# TinyNewsLlama-1.1B\n\nTinyNewsLlama-1.1B is a QLoRA SFT fine-tune of TinyLlama/TinyLlama-1.1B-Chat-v1.0 using a sample of a concentrated version of the [bigNews] (URL Dataset. The model was fine-tuned for ~12h on one A100 40GB on ~125M tokens.\n\nThe goal of this project is to study the potential for improving the domain-specific (in this case political) knowledge of small (<3B) LLMs by concentrating the training datasets TF-IDF in respect to the underlying Topics found in the origianl Dataset.\n\nThe used training data contains political news articles from The New York Times, USA Today and The Washington Times. The concentrated BigNews Dataset as well as more information about the used sample will soon be added.",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #TinyLlama #QLoRA #Politics #News #sft #conversational #en #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"# TinyNewsLlama-1.1B\n\nTinyNewsLlama-1.1B is a QLoRA SFT fine-tune of TinyLlama/TinyLlama-1.1B-Chat-v1.0 using a sample of a concentrated version of the [bigNews] (URL Dataset. The model was fine-tuned for ~12h on one A100 40GB on ~125M tokens.\n\nThe goal of this project is to study the potential for improving the domain-specific (in this case political) knowledge of small (<3B) LLMs by concentrating the training datasets TF-IDF in respect to the underlying Topics found in the origianl Dataset.\n\nThe used training data contains political news articles from The New York Times, USA Today and The Washington Times. The concentrated BigNews Dataset as well as more information about the used sample will soon be added.",
"## Usage"
] | [
103,
194,
3
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #TinyLlama #QLoRA #Politics #News #sft #conversational #en #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# TinyNewsLlama-1.1B\n\nTinyNewsLlama-1.1B is a QLoRA SFT fine-tune of TinyLlama/TinyLlama-1.1B-Chat-v1.0 using a sample of a concentrated version of the [bigNews] (URL Dataset. The model was fine-tuned for ~12h on one A100 40GB on ~125M tokens.\n\nThe goal of this project is to study the potential for improving the domain-specific (in this case political) knowledge of small (<3B) LLMs by concentrating the training datasets TF-IDF in respect to the underlying Topics found in the origianl Dataset.\n\nThe used training data contains political news articles from The New York Times, USA Today and The Washington Times. The concentrated BigNews Dataset as well as more information about the used sample will soon be added.## Usage"
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] |
null | null | null |
# Mixtral MOE 5x7B
MoE of the following models :
* [Toten5/Marcoroni-neural-chat-7B-v1](https://huggingface.co/Toten5/Marcoroni-neural-chat-7B-v1)
* [NurtureAI/neural-chat-7b-v3-16k](https://huggingface.co/NurtureAI/neural-chat-7b-v3-16k)
* [mncai/mistral-7b-dpo-v6](https://huggingface.co/mncai/mistral-7b-dpo-v6)
* [cookinai/CatMacaroni-Slerp](https://huggingface.co/cookinai/CatMacaroni-Slerp)
* [ignos/Mistral-T5-7B-v1](https://huggingface.co/ignos/Mistral-T5-7B-v1)
gpu code example
```
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import math
## v2 models
model_path = "cloudyu/Mixtral_7Bx5_MoE_30B"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False)
model = AutoModelForCausalLM.from_pretrained(
model_path, torch_dtype=torch.float32, device_map='auto',local_files_only=False, load_in_4bit=True
)
print(model)
prompt = input("please input prompt:")
while len(prompt) > 0:
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")
generation_output = model.generate(
input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2
)
print(tokenizer.decode(generation_output[0]))
prompt = input("please input prompt:")
```
CPU example
```
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import math
## v2 models
model_path = "cloudyu/Mixtral_7Bx5_MoE_30B"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False)
model = AutoModelForCausalLM.from_pretrained(
model_path, torch_dtype=torch.float32, device_map='cpu',local_files_only=False
)
print(model)
prompt = input("please input prompt:")
while len(prompt) > 0:
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
generation_output = model.generate(
input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2
)
print(tokenizer.decode(generation_output[0]))
prompt = input("please input prompt:")
``` | {"license": "cc-by-nc-4.0"} | null | LoneStriker/Mixtral_7Bx5_MoE_30B-GGUF | [
"gguf",
"license:cc-by-nc-4.0",
"region:us"
] | 2024-02-13T15:37:06+00:00 | [] | [] | TAGS
#gguf #license-cc-by-nc-4.0 #region-us
|
# Mixtral MOE 5x7B
MoE of the following models :
* Toten5/Marcoroni-neural-chat-7B-v1
* NurtureAI/neural-chat-7b-v3-16k
* mncai/mistral-7b-dpo-v6
* cookinai/CatMacaroni-Slerp
* ignos/Mistral-T5-7B-v1
gpu code example
CPU example
| [
"# Mixtral MOE 5x7B\n\nMoE of the following models :\n\n* Toten5/Marcoroni-neural-chat-7B-v1\n* NurtureAI/neural-chat-7b-v3-16k\n* mncai/mistral-7b-dpo-v6\n* cookinai/CatMacaroni-Slerp\n* ignos/Mistral-T5-7B-v1\n\n\ngpu code example\n\n\n\nCPU example"
] | [
"TAGS\n#gguf #license-cc-by-nc-4.0 #region-us \n",
"# Mixtral MOE 5x7B\n\nMoE of the following models :\n\n* Toten5/Marcoroni-neural-chat-7B-v1\n* NurtureAI/neural-chat-7b-v3-16k\n* mncai/mistral-7b-dpo-v6\n* cookinai/CatMacaroni-Slerp\n* ignos/Mistral-T5-7B-v1\n\n\ngpu code example\n\n\n\nCPU example"
] | [
20,
98
] | [
"passage: TAGS\n#gguf #license-cc-by-nc-4.0 #region-us \n# Mixtral MOE 5x7B\n\nMoE of the following models :\n\n* Toten5/Marcoroni-neural-chat-7B-v1\n* NurtureAI/neural-chat-7b-v3-16k\n* mncai/mistral-7b-dpo-v6\n* cookinai/CatMacaroni-Slerp\n* ignos/Mistral-T5-7B-v1\n\n\ngpu code example\n\n\n\nCPU example"
] | [
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null | null | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga juan9 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga juan9 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga juan9
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "339.00 +/- 106.79", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | juan9/dqn-SpaceInvadersNoFrameskip-v4 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-13T15:37:55+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: URL
SB3: URL
SB3 Contrib: URL
Install the RL Zoo (with SB3 and SB3-Contrib):
If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:
## Training (with the RL Zoo)
## Hyperparameters
# Environment Arguments
| [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
43,
90,
73,
9,
5,
7
] | [
"passage: TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters# Environment Arguments"
] | [
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null | null | null |
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 1000000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.999
'gae_lambda': 0.99
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'ORromu/ppo-LunarLander-v2-u8'
'batch_size': 512
'minibatch_size': 128}
```
| {"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "160.87 +/- 103.90", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | ORromu/ppo-LunarLander-v2-u8 | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | 2024-02-13T15:39:32+00:00 | [] | [] | TAGS
#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us
|
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
| [
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
"TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n",
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
51,
37
] | [
"passage: TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
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] |
null | null | transformers |
## Model Description
This model is finetuned from [Viet-Mistral/Vistral-7B-Chat](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat). The dataset is taken from [bkai-foundation-models/vi-self-chat-sharegpt-format](https://huggingface.co/datasets/bkai-foundation-models/vi-self-chat-sharegpt-format)
This is a **low rank** finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.
The format looks like this:
```
<|im_start|>system
Provide some context and/or instructions to the model.
<|im_end|>
<|im_start|>user
The user’s message goes here
<|im_end|>
<|im_start|>assistant
```
## How to use it
The recommended way is to use the GGUF `vistral-7b-chatml-Q4_K_M.gguf` file included in this repository. Run it via llama.cpp (remember to pass `-cml` argument to use chatml template)
```bash
./main -m vistral-7b-chatml-Q4_K_M.gguf -p "Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực." -cml
```
Additionally, you can run the `python3 run.py` inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.
This is an example of a conversation using llama.cpp:
- <|im_start|>system
- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>
- <|im_start|>user
- > xin chào
- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>
- > trợ lý AI là gì? bạn giải thích được không?
- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>
- > ồ, cảm ơn, vậy bạn có thể làm gì?
- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>
- > tức là sao?
- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>
## Training
You can also look at the training code in the `finetune.py` file.
For tokenizer, I changed these things:
- Token `[INST]` to `<|im_start|>`, make it become special token
- Token `[/INST]` to `<|im_end|>`, make it become special token
- Change to `eos_token` to `<|im_end|>`
- Update `chat_template` to chatml, taken from [this example](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/tokenizer_config.json#L52)
Additionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.
## More information
Disclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a "hobby" to me. Training is done using a VPS on Google Cloud, I paid with my own money.
If you want to discuss, feel free to contact me at `contact at ngxson dot com` - [ngxson.com](https://ngxson.com)
| {"language": ["vi"], "license": "mit", "library_name": "transformers", "tags": ["LLMs", "NLP", "Vietnamese"]} | text-generation | LoneStriker/Vistral-7B-ChatML-3.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"LLMs",
"NLP",
"Vietnamese",
"conversational",
"vi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T15:48:32+00:00 | [] | [
"vi"
] | TAGS
#transformers #safetensors #mistral #text-generation #LLMs #NLP #Vietnamese #conversational #vi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Model Description
This model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format
This is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.
The format looks like this:
## How to use it
The recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)
Additionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.
This is an example of a conversation using URL:
- <|im_start|>system
- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>
- <|im_start|>user
- > xin chào
- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>
- > trợ lý AI là gì? bạn giải thích được không?
- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>
- > ồ, cảm ơn, vậy bạn có thể làm gì?
- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>
- > tức là sao?
- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>
## Training
You can also look at the training code in the 'URL' file.
For tokenizer, I changed these things:
- Token '[INST]' to '<|im_start|>', make it become special token
- Token '[/INST]' to '<|im_end|>', make it become special token
- Change to 'eos_token' to '<|im_end|>'
- Update 'chat_template' to chatml, taken from this example
Additionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.
## More information
Disclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a "hobby" to me. Training is done using a VPS on Google Cloud, I paid with my own money.
If you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL
| [
"## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:",
"## How to use it\n\nThe recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)\n\n\n\nAdditionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.\n\nThis is an example of a conversation using URL:\n\n- <|im_start|>system\n- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>\n- <|im_start|>user\n- > xin chào \n- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>\n- > trợ lý AI là gì? bạn giải thích được không?\n- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>\n- > ồ, cảm ơn, vậy bạn có thể làm gì?\n- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>\n- > tức là sao?\n- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>",
"## Training\n\nYou can also look at the training code in the 'URL' file.\n\nFor tokenizer, I changed these things:\n- Token '[INST]' to '<|im_start|>', make it become special token\n- Token '[/INST]' to '<|im_end|>', make it become special token\n- Change to 'eos_token' to '<|im_end|>'\n- Update 'chat_template' to chatml, taken from this example\n\nAdditionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.",
"## More information\n\nDisclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a \"hobby\" to me. Training is done using a VPS on Google Cloud, I paid with my own money.\n\nIf you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #LLMs #NLP #Vietnamese #conversational #vi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:",
"## How to use it\n\nThe recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)\n\n\n\nAdditionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.\n\nThis is an example of a conversation using URL:\n\n- <|im_start|>system\n- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>\n- <|im_start|>user\n- > xin chào \n- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>\n- > trợ lý AI là gì? bạn giải thích được không?\n- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>\n- > ồ, cảm ơn, vậy bạn có thể làm gì?\n- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>\n- > tức là sao?\n- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>",
"## Training\n\nYou can also look at the training code in the 'URL' file.\n\nFor tokenizer, I changed these things:\n- Token '[INST]' to '<|im_start|>', make it become special token\n- Token '[/INST]' to '<|im_end|>', make it become special token\n- Change to 'eos_token' to '<|im_end|>'\n- Update 'chat_template' to chatml, taken from this example\n\nAdditionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.",
"## More information\n\nDisclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a \"hobby\" to me. Training is done using a VPS on Google Cloud, I paid with my own money.\n\nIf you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL"
] | [
70,
98,
478,
141,
76
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"passage: TAGS\n#transformers #safetensors #mistral #text-generation #LLMs #NLP #Vietnamese #conversational #vi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:"
] | [
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] |
null | null | transformers |
## Model Description
This model is finetuned from [Viet-Mistral/Vistral-7B-Chat](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat). The dataset is taken from [bkai-foundation-models/vi-self-chat-sharegpt-format](https://huggingface.co/datasets/bkai-foundation-models/vi-self-chat-sharegpt-format)
This is a **low rank** finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.
The format looks like this:
```
<|im_start|>system
Provide some context and/or instructions to the model.
<|im_end|>
<|im_start|>user
The user’s message goes here
<|im_end|>
<|im_start|>assistant
```
## How to use it
The recommended way is to use the GGUF `vistral-7b-chatml-Q4_K_M.gguf` file included in this repository. Run it via llama.cpp (remember to pass `-cml` argument to use chatml template)
```bash
./main -m vistral-7b-chatml-Q4_K_M.gguf -p "Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực." -cml
```
Additionally, you can run the `python3 run.py` inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.
This is an example of a conversation using llama.cpp:
- <|im_start|>system
- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>
- <|im_start|>user
- > xin chào
- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>
- > trợ lý AI là gì? bạn giải thích được không?
- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>
- > ồ, cảm ơn, vậy bạn có thể làm gì?
- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>
- > tức là sao?
- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>
## Training
You can also look at the training code in the `finetune.py` file.
For tokenizer, I changed these things:
- Token `[INST]` to `<|im_start|>`, make it become special token
- Token `[/INST]` to `<|im_end|>`, make it become special token
- Change to `eos_token` to `<|im_end|>`
- Update `chat_template` to chatml, taken from [this example](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/tokenizer_config.json#L52)
Additionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.
## More information
Disclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a "hobby" to me. Training is done using a VPS on Google Cloud, I paid with my own money.
If you want to discuss, feel free to contact me at `contact at ngxson dot com` - [ngxson.com](https://ngxson.com)
| {"language": ["vi"], "license": "mit", "library_name": "transformers", "tags": ["LLMs", "NLP", "Vietnamese"]} | text-generation | LoneStriker/Vistral-7B-ChatML-4.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"LLMs",
"NLP",
"Vietnamese",
"conversational",
"vi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T15:49:57+00:00 | [] | [
"vi"
] | TAGS
#transformers #safetensors #mistral #text-generation #LLMs #NLP #Vietnamese #conversational #vi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Model Description
This model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format
This is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.
The format looks like this:
## How to use it
The recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)
Additionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.
This is an example of a conversation using URL:
- <|im_start|>system
- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>
- <|im_start|>user
- > xin chào
- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>
- > trợ lý AI là gì? bạn giải thích được không?
- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>
- > ồ, cảm ơn, vậy bạn có thể làm gì?
- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>
- > tức là sao?
- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>
## Training
You can also look at the training code in the 'URL' file.
For tokenizer, I changed these things:
- Token '[INST]' to '<|im_start|>', make it become special token
- Token '[/INST]' to '<|im_end|>', make it become special token
- Change to 'eos_token' to '<|im_end|>'
- Update 'chat_template' to chatml, taken from this example
Additionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.
## More information
Disclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a "hobby" to me. Training is done using a VPS on Google Cloud, I paid with my own money.
If you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL
| [
"## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:",
"## How to use it\n\nThe recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)\n\n\n\nAdditionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.\n\nThis is an example of a conversation using URL:\n\n- <|im_start|>system\n- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>\n- <|im_start|>user\n- > xin chào \n- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>\n- > trợ lý AI là gì? bạn giải thích được không?\n- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>\n- > ồ, cảm ơn, vậy bạn có thể làm gì?\n- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>\n- > tức là sao?\n- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>",
"## Training\n\nYou can also look at the training code in the 'URL' file.\n\nFor tokenizer, I changed these things:\n- Token '[INST]' to '<|im_start|>', make it become special token\n- Token '[/INST]' to '<|im_end|>', make it become special token\n- Change to 'eos_token' to '<|im_end|>'\n- Update 'chat_template' to chatml, taken from this example\n\nAdditionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.",
"## More information\n\nDisclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a \"hobby\" to me. Training is done using a VPS on Google Cloud, I paid with my own money.\n\nIf you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #LLMs #NLP #Vietnamese #conversational #vi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:",
"## How to use it\n\nThe recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)\n\n\n\nAdditionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.\n\nThis is an example of a conversation using URL:\n\n- <|im_start|>system\n- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>\n- <|im_start|>user\n- > xin chào \n- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>\n- > trợ lý AI là gì? bạn giải thích được không?\n- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>\n- > ồ, cảm ơn, vậy bạn có thể làm gì?\n- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>\n- > tức là sao?\n- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>",
"## Training\n\nYou can also look at the training code in the 'URL' file.\n\nFor tokenizer, I changed these things:\n- Token '[INST]' to '<|im_start|>', make it become special token\n- Token '[/INST]' to '<|im_end|>', make it become special token\n- Change to 'eos_token' to '<|im_end|>'\n- Update 'chat_template' to chatml, taken from this example\n\nAdditionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.",
"## More information\n\nDisclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a \"hobby\" to me. Training is done using a VPS on Google Cloud, I paid with my own money.\n\nIf you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL"
] | [
70,
98,
478,
141,
76
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #LLMs #NLP #Vietnamese #conversational #vi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:"
] | [
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] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "266.30 +/- 22.54", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | MJavadZallaghi/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-13T15:50:21+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers |
## Model Description
This model is finetuned from [Viet-Mistral/Vistral-7B-Chat](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat). The dataset is taken from [bkai-foundation-models/vi-self-chat-sharegpt-format](https://huggingface.co/datasets/bkai-foundation-models/vi-self-chat-sharegpt-format)
This is a **low rank** finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.
The format looks like this:
```
<|im_start|>system
Provide some context and/or instructions to the model.
<|im_end|>
<|im_start|>user
The user’s message goes here
<|im_end|>
<|im_start|>assistant
```
## How to use it
The recommended way is to use the GGUF `vistral-7b-chatml-Q4_K_M.gguf` file included in this repository. Run it via llama.cpp (remember to pass `-cml` argument to use chatml template)
```bash
./main -m vistral-7b-chatml-Q4_K_M.gguf -p "Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực." -cml
```
Additionally, you can run the `python3 run.py` inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.
This is an example of a conversation using llama.cpp:
- <|im_start|>system
- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>
- <|im_start|>user
- > xin chào
- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>
- > trợ lý AI là gì? bạn giải thích được không?
- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>
- > ồ, cảm ơn, vậy bạn có thể làm gì?
- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>
- > tức là sao?
- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>
## Training
You can also look at the training code in the `finetune.py` file.
For tokenizer, I changed these things:
- Token `[INST]` to `<|im_start|>`, make it become special token
- Token `[/INST]` to `<|im_end|>`, make it become special token
- Change to `eos_token` to `<|im_end|>`
- Update `chat_template` to chatml, taken from [this example](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/tokenizer_config.json#L52)
Additionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.
## More information
Disclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a "hobby" to me. Training is done using a VPS on Google Cloud, I paid with my own money.
If you want to discuss, feel free to contact me at `contact at ngxson dot com` - [ngxson.com](https://ngxson.com)
| {"language": ["vi"], "license": "mit", "library_name": "transformers", "tags": ["LLMs", "NLP", "Vietnamese"]} | text-generation | LoneStriker/Vistral-7B-ChatML-5.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"LLMs",
"NLP",
"Vietnamese",
"conversational",
"vi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T15:51:41+00:00 | [] | [
"vi"
] | TAGS
#transformers #safetensors #mistral #text-generation #LLMs #NLP #Vietnamese #conversational #vi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Model Description
This model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format
This is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.
The format looks like this:
## How to use it
The recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)
Additionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.
This is an example of a conversation using URL:
- <|im_start|>system
- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>
- <|im_start|>user
- > xin chào
- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>
- > trợ lý AI là gì? bạn giải thích được không?
- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>
- > ồ, cảm ơn, vậy bạn có thể làm gì?
- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>
- > tức là sao?
- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>
## Training
You can also look at the training code in the 'URL' file.
For tokenizer, I changed these things:
- Token '[INST]' to '<|im_start|>', make it become special token
- Token '[/INST]' to '<|im_end|>', make it become special token
- Change to 'eos_token' to '<|im_end|>'
- Update 'chat_template' to chatml, taken from this example
Additionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.
## More information
Disclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a "hobby" to me. Training is done using a VPS on Google Cloud, I paid with my own money.
If you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL
| [
"## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:",
"## How to use it\n\nThe recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)\n\n\n\nAdditionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.\n\nThis is an example of a conversation using URL:\n\n- <|im_start|>system\n- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>\n- <|im_start|>user\n- > xin chào \n- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>\n- > trợ lý AI là gì? bạn giải thích được không?\n- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>\n- > ồ, cảm ơn, vậy bạn có thể làm gì?\n- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>\n- > tức là sao?\n- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>",
"## Training\n\nYou can also look at the training code in the 'URL' file.\n\nFor tokenizer, I changed these things:\n- Token '[INST]' to '<|im_start|>', make it become special token\n- Token '[/INST]' to '<|im_end|>', make it become special token\n- Change to 'eos_token' to '<|im_end|>'\n- Update 'chat_template' to chatml, taken from this example\n\nAdditionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.",
"## More information\n\nDisclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a \"hobby\" to me. Training is done using a VPS on Google Cloud, I paid with my own money.\n\nIf you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #LLMs #NLP #Vietnamese #conversational #vi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:",
"## How to use it\n\nThe recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)\n\n\n\nAdditionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.\n\nThis is an example of a conversation using URL:\n\n- <|im_start|>system\n- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>\n- <|im_start|>user\n- > xin chào \n- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>\n- > trợ lý AI là gì? bạn giải thích được không?\n- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>\n- > ồ, cảm ơn, vậy bạn có thể làm gì?\n- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>\n- > tức là sao?\n- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>",
"## Training\n\nYou can also look at the training code in the 'URL' file.\n\nFor tokenizer, I changed these things:\n- Token '[INST]' to '<|im_start|>', make it become special token\n- Token '[/INST]' to '<|im_end|>', make it become special token\n- Change to 'eos_token' to '<|im_end|>'\n- Update 'chat_template' to chatml, taken from this example\n\nAdditionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.",
"## More information\n\nDisclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a \"hobby\" to me. Training is done using a VPS on Google Cloud, I paid with my own money.\n\nIf you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL"
] | [
70,
98,
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# conversational-PCRA-and-CCRA-zephyr-7b-beta-sft-qlora
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the ajrogier/conversational-PCRA-and-CCRA-sections dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3551
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3504 | 1.0 | 1419 | 1.3551 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2 | {"license": "mit", "library_name": "peft", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "sft", "generated_from_trainer"], "datasets": ["ajrogier/conversational-PCRA-and-CCRA-sections"], "base_model": "HuggingFaceH4/zephyr-7b-beta", "model-index": [{"name": "conversational-PCRA-and-CCRA-zephyr-7b-beta-sft-qlora", "results": []}]} | null | ajrogier/conversational-PCRA-and-CCRA-zephyr-7b-beta-sft-qlora | [
"peft",
"tensorboard",
"safetensors",
"mistral",
"alignment-handbook",
"generated_from_trainer",
"trl",
"sft",
"dataset:ajrogier/conversational-PCRA-and-CCRA-sections",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"4-bit",
"region:us"
] | 2024-02-13T15:51:52+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #mistral #alignment-handbook #generated_from_trainer #trl #sft #dataset-ajrogier/conversational-PCRA-and-CCRA-sections #base_model-HuggingFaceH4/zephyr-7b-beta #license-mit #4-bit #region-us
| conversational-PCRA-and-CCRA-zephyr-7b-beta-sft-qlora
=====================================================
This model is a fine-tuned version of HuggingFaceH4/zephyr-7b-beta on the ajrogier/conversational-PCRA-and-CCRA-sections dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3551
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 1
### Training results
### Framework versions
* PEFT 0.7.1
* Transformers 4.36.2
* Pytorch 2.1.2
* Datasets 2.14.6
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #tensorboard #safetensors #mistral #alignment-handbook #generated_from_trainer #trl #sft #dataset-ajrogier/conversational-PCRA-and-CCRA-sections #base_model-HuggingFaceH4/zephyr-7b-beta #license-mit #4-bit #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2"
] | [
88,
155,
4,
36
] | [
"passage: TAGS\n#peft #tensorboard #safetensors #mistral #alignment-handbook #generated_from_trainer #trl #sft #dataset-ajrogier/conversational-PCRA-and-CCRA-sections #base_model-HuggingFaceH4/zephyr-7b-beta #license-mit #4-bit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2"
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large v3 PEFT Fine-Tuned Finnish
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 13.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8184
- WER: 14.08
- Normalized WER: 10.29
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
mozilla-foundation/common_voice_13_0
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adafactor with (scale_parameter=True, relative_step=True, warmup_init=True, lr=None)
- lr_scheduler_type: LambdaLR
- lr_scheduler_warmup_steps: 50
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.456 | 0.84 | 100 | 1.5013 |
| 1.4506 | 1.68 | 200 | 1.4955 |
| 1.4408 | 2.52 | 300 | 1.4818 |
| 1.4228 | 3.36 | 400 | 1.4556 |
| 1.3843 | 4.2 | 500 | 1.4082 |
| 1.3263 | 5.04 | 600 | 1.3327 |
| 1.2322 | 5.88 | 700 | 1.2207 |
| 1.1047 | 6.72 | 800 | 1.0514 |
| 0.9088 | 7.56 | 900 | 0.8184 |
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.37.0.dev0
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0
## Evaluation
First 10 transcription of the fine-tuned model is provided together with the reference sentences. Test split of the dataset 'mozilla-foundation/common_voice_13_0' was used for evaluation.
### Predictions
- ' Yhtyessä ei ollut perinteistä tähtisolistia, vaan yhtyettä seurattiin kokonaisuutena, vaikkakin eri jäseniä oli ihailijoita.',
- ' Nyt jo mamman täytyy se myöntää.',
- ' Tällaisina päivinä tulevat mieleen entiset päivän paisteet.',
- ' Ja tuttiaan imivä Luukas nukkui.',
- ' Kaikki jättäytyi näin minun varaani.',
- ' Niin tapaus juoksi kuin vanhaa latuaan.',
- ' He eivät tulisi ikinä pääsemään lähellekään näitä planeettoja, mikäli Marsin johto pysyisi samana.',
- ' Hän juttelee yhtä ja toista.',
- ' Emme tarvitsisi hänestä toista vihollista.',
- ' Olihan videoilla muutakin mielenkiintoista kuin saniaisia muistuttavat kasvit ja puut.'
### References
- 'Yhtyeessä ei ollut perinteistä tähtisolistia, vaan yhtyettä seurattiin kokonaisuutena vaikkakin eri jäsenillä oli ihailijoita',
- 'Nyt jo mammankin täytyy se myöntää.',
- 'Tällaisina päivinä tulevat mieleen entiset päivänpaisteet.',
- 'Ja tuttiaan imevä Luukas nukkui.',
- 'Kaikki jättäytyi näin minun varaani.',
- 'Niin tapaus juoksi kuin vanhaa latuaan.',
- 'He eivät tulisi ikinä pääsemään lähellekään näitä planeettoja, mikäli Marsin johto pysyisi samana.',
- 'Hän juttelee yhtä ja toista.',
- 'Emme tarvitsisi hänestä toista vihollista.',
- 'Olihan videoilla muutakin mielenkiintoista kuin saniaisia muistuttavat kasvit ja puut.' | {"language": ["fi"], "license": "apache-2.0", "library_name": "peft", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_13_0"], "metrics": ["wer"], "base_model": "openai/whisper-large-v3", "pipeline_tag": "automatic-speech-recognition", "model-index": [{"name": "Whisper Large v3 PEFT Fine-Tuned Finnish", "results": []}]} | automatic-speech-recognition | enakilci/whisper-large-v3-fi-8epocs-32batch-adafactor | [
"peft",
"safetensors",
"hf-asr-leaderboard",
"generated_from_trainer",
"automatic-speech-recognition",
"fi",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:openai/whisper-large-v3",
"license:apache-2.0",
"region:us"
] | 2024-02-13T15:52:23+00:00 | [] | [
"fi"
] | TAGS
#peft #safetensors #hf-asr-leaderboard #generated_from_trainer #automatic-speech-recognition #fi #dataset-mozilla-foundation/common_voice_13_0 #base_model-openai/whisper-large-v3 #license-apache-2.0 #region-us
| Whisper Large v3 PEFT Fine-Tuned Finnish
========================================
This model is a fine-tuned version of openai/whisper-large-v3 on the Common Voice 13.0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8184
* WER: 14.08
* Normalized WER: 10.29
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
mozilla-foundation/common\_voice\_13\_0
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adafactor with (scale\_parameter=True, relative\_step=True, warmup\_init=True, lr=None)
* lr\_scheduler\_type: LambdaLR
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 8
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* PEFT 0.7.2.dev0
* Transformers 4.37.0.dev0
* Pytorch 2.0.1
* Datasets 2.16.1
* Tokenizers 0.15.0
Evaluation
----------
First 10 transcription of the fine-tuned model is provided together with the reference sentences. Test split of the dataset 'mozilla-foundation/common\_voice\_13\_0' was used for evaluation.
### Predictions
* ' Yhtyessä ei ollut perinteistä tähtisolistia, vaan yhtyettä seurattiin kokonaisuutena, vaikkakin eri jäseniä oli ihailijoita.',
* ' Nyt jo mamman täytyy se myöntää.',
* ' Tällaisina päivinä tulevat mieleen entiset päivän paisteet.',
* ' Ja tuttiaan imivä Luukas nukkui.',
* ' Kaikki jättäytyi näin minun varaani.',
* ' Niin tapaus juoksi kuin vanhaa latuaan.',
* ' He eivät tulisi ikinä pääsemään lähellekään näitä planeettoja, mikäli Marsin johto pysyisi samana.',
* ' Hän juttelee yhtä ja toista.',
* ' Emme tarvitsisi hänestä toista vihollista.',
* ' Olihan videoilla muutakin mielenkiintoista kuin saniaisia muistuttavat kasvit ja puut.'
### References
* 'Yhtyeessä ei ollut perinteistä tähtisolistia, vaan yhtyettä seurattiin kokonaisuutena vaikkakin eri jäsenillä oli ihailijoita',
* 'Nyt jo mammankin täytyy se myöntää.',
* 'Tällaisina päivinä tulevat mieleen entiset päivänpaisteet.',
* 'Ja tuttiaan imevä Luukas nukkui.',
* 'Kaikki jättäytyi näin minun varaani.',
* 'Niin tapaus juoksi kuin vanhaa latuaan.',
* 'He eivät tulisi ikinä pääsemään lähellekään näitä planeettoja, mikäli Marsin johto pysyisi samana.',
* 'Hän juttelee yhtä ja toista.',
* 'Emme tarvitsisi hänestä toista vihollista.',
* 'Olihan videoilla muutakin mielenkiintoista kuin saniaisia muistuttavat kasvit ja puut.'
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adafactor with (scale\\_parameter=True, relative\\_step=True, warmup\\_init=True, lr=None)\n* lr\\_scheduler\\_type: LambdaLR\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 8\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.7.2.dev0\n* Transformers 4.37.0.dev0\n* Pytorch 2.0.1\n* Datasets 2.16.1\n* Tokenizers 0.15.0\n\n\nEvaluation\n----------\n\n\nFirst 10 transcription of the fine-tuned model is provided together with the reference sentences. Test split of the dataset 'mozilla-foundation/common\\_voice\\_13\\_0' was used for evaluation.",
"### Predictions\n\n\n* ' Yhtyessä ei ollut perinteistä tähtisolistia, vaan yhtyettä seurattiin kokonaisuutena, vaikkakin eri jäseniä oli ihailijoita.',\n* ' Nyt jo mamman täytyy se myöntää.',\n* ' Tällaisina päivinä tulevat mieleen entiset päivän paisteet.',\n* ' Ja tuttiaan imivä Luukas nukkui.',\n* ' Kaikki jättäytyi näin minun varaani.',\n* ' Niin tapaus juoksi kuin vanhaa latuaan.',\n* ' He eivät tulisi ikinä pääsemään lähellekään näitä planeettoja, mikäli Marsin johto pysyisi samana.',\n* ' Hän juttelee yhtä ja toista.',\n* ' Emme tarvitsisi hänestä toista vihollista.',\n* ' Olihan videoilla muutakin mielenkiintoista kuin saniaisia muistuttavat kasvit ja puut.'",
"### References\n\n\n* 'Yhtyeessä ei ollut perinteistä tähtisolistia, vaan yhtyettä seurattiin kokonaisuutena vaikkakin eri jäsenillä oli ihailijoita',\n* 'Nyt jo mammankin täytyy se myöntää.',\n* 'Tällaisina päivinä tulevat mieleen entiset päivänpaisteet.',\n* 'Ja tuttiaan imevä Luukas nukkui.',\n* 'Kaikki jättäytyi näin minun varaani.',\n* 'Niin tapaus juoksi kuin vanhaa latuaan.',\n* 'He eivät tulisi ikinä pääsemään lähellekään näitä planeettoja, mikäli Marsin johto pysyisi samana.',\n* 'Hän juttelee yhtä ja toista.',\n* 'Emme tarvitsisi hänestä toista vihollista.',\n* 'Olihan videoilla muutakin mielenkiintoista kuin saniaisia muistuttavat kasvit ja puut.'"
] | [
"TAGS\n#peft #safetensors #hf-asr-leaderboard #generated_from_trainer #automatic-speech-recognition #fi #dataset-mozilla-foundation/common_voice_13_0 #base_model-openai/whisper-large-v3 #license-apache-2.0 #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adafactor with (scale\\_parameter=True, relative\\_step=True, warmup\\_init=True, lr=None)\n* lr\\_scheduler\\_type: LambdaLR\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 8\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.7.2.dev0\n* Transformers 4.37.0.dev0\n* Pytorch 2.0.1\n* Datasets 2.16.1\n* Tokenizers 0.15.0\n\n\nEvaluation\n----------\n\n\nFirst 10 transcription of the fine-tuned model is provided together with the reference sentences. Test split of the dataset 'mozilla-foundation/common\\_voice\\_13\\_0' was used for evaluation.",
"### Predictions\n\n\n* ' Yhtyessä ei ollut perinteistä tähtisolistia, vaan yhtyettä seurattiin kokonaisuutena, vaikkakin eri jäseniä oli ihailijoita.',\n* ' Nyt jo mamman täytyy se myöntää.',\n* ' Tällaisina päivinä tulevat mieleen entiset päivän paisteet.',\n* ' Ja tuttiaan imivä Luukas nukkui.',\n* ' Kaikki jättäytyi näin minun varaani.',\n* ' Niin tapaus juoksi kuin vanhaa latuaan.',\n* ' He eivät tulisi ikinä pääsemään lähellekään näitä planeettoja, mikäli Marsin johto pysyisi samana.',\n* ' Hän juttelee yhtä ja toista.',\n* ' Emme tarvitsisi hänestä toista vihollista.',\n* ' Olihan videoilla muutakin mielenkiintoista kuin saniaisia muistuttavat kasvit ja puut.'",
"### References\n\n\n* 'Yhtyeessä ei ollut perinteistä tähtisolistia, vaan yhtyettä seurattiin kokonaisuutena vaikkakin eri jäsenillä oli ihailijoita',\n* 'Nyt jo mammankin täytyy se myöntää.',\n* 'Tällaisina päivinä tulevat mieleen entiset päivänpaisteet.',\n* 'Ja tuttiaan imevä Luukas nukkui.',\n* 'Kaikki jättäytyi näin minun varaani.',\n* 'Niin tapaus juoksi kuin vanhaa latuaan.',\n* 'He eivät tulisi ikinä pääsemään lähellekään näitä planeettoja, mikäli Marsin johto pysyisi samana.',\n* 'Hän juttelee yhtä ja toista.',\n* 'Emme tarvitsisi hänestä toista vihollista.',\n* 'Olihan videoilla muutakin mielenkiintoista kuin saniaisia muistuttavat kasvit ja puut.'"
] | [
87,
157,
4,
97,
190,
194
] | [
"passage: TAGS\n#peft #safetensors #hf-asr-leaderboard #generated_from_trainer #automatic-speech-recognition #fi #dataset-mozilla-foundation/common_voice_13_0 #base_model-openai/whisper-large-v3 #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adafactor with (scale\\_parameter=True, relative\\_step=True, warmup\\_init=True, lr=None)\n* lr\\_scheduler\\_type: LambdaLR\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 8\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* PEFT 0.7.2.dev0\n* Transformers 4.37.0.dev0\n* Pytorch 2.0.1\n* Datasets 2.16.1\n* Tokenizers 0.15.0\n\n\nEvaluation\n----------\n\n\nFirst 10 transcription of the fine-tuned model is provided together with the reference sentences. Test split of the dataset 'mozilla-foundation/common\\_voice\\_13\\_0' was used for evaluation."
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] |
null | null | transformers |
## Model Description
This model is finetuned from [Viet-Mistral/Vistral-7B-Chat](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat). The dataset is taken from [bkai-foundation-models/vi-self-chat-sharegpt-format](https://huggingface.co/datasets/bkai-foundation-models/vi-self-chat-sharegpt-format)
This is a **low rank** finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.
The format looks like this:
```
<|im_start|>system
Provide some context and/or instructions to the model.
<|im_end|>
<|im_start|>user
The user’s message goes here
<|im_end|>
<|im_start|>assistant
```
## How to use it
The recommended way is to use the GGUF `vistral-7b-chatml-Q4_K_M.gguf` file included in this repository. Run it via llama.cpp (remember to pass `-cml` argument to use chatml template)
```bash
./main -m vistral-7b-chatml-Q4_K_M.gguf -p "Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực." -cml
```
Additionally, you can run the `python3 run.py` inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.
This is an example of a conversation using llama.cpp:
- <|im_start|>system
- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>
- <|im_start|>user
- > xin chào
- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>
- > trợ lý AI là gì? bạn giải thích được không?
- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>
- > ồ, cảm ơn, vậy bạn có thể làm gì?
- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>
- > tức là sao?
- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>
## Training
You can also look at the training code in the `finetune.py` file.
For tokenizer, I changed these things:
- Token `[INST]` to `<|im_start|>`, make it become special token
- Token `[/INST]` to `<|im_end|>`, make it become special token
- Change to `eos_token` to `<|im_end|>`
- Update `chat_template` to chatml, taken from [this example](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/tokenizer_config.json#L52)
Additionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.
## More information
Disclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a "hobby" to me. Training is done using a VPS on Google Cloud, I paid with my own money.
If you want to discuss, feel free to contact me at `contact at ngxson dot com` - [ngxson.com](https://ngxson.com)
| {"language": ["vi"], "license": "mit", "library_name": "transformers", "tags": ["LLMs", "NLP", "Vietnamese"]} | text-generation | LoneStriker/Vistral-7B-ChatML-6.0bpw-h6-exl2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"LLMs",
"NLP",
"Vietnamese",
"conversational",
"vi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T15:53:45+00:00 | [] | [
"vi"
] | TAGS
#transformers #safetensors #mistral #text-generation #LLMs #NLP #Vietnamese #conversational #vi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Model Description
This model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format
This is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.
The format looks like this:
## How to use it
The recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)
Additionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.
This is an example of a conversation using URL:
- <|im_start|>system
- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>
- <|im_start|>user
- > xin chào
- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>
- > trợ lý AI là gì? bạn giải thích được không?
- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>
- > ồ, cảm ơn, vậy bạn có thể làm gì?
- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>
- > tức là sao?
- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>
## Training
You can also look at the training code in the 'URL' file.
For tokenizer, I changed these things:
- Token '[INST]' to '<|im_start|>', make it become special token
- Token '[/INST]' to '<|im_end|>', make it become special token
- Change to 'eos_token' to '<|im_end|>'
- Update 'chat_template' to chatml, taken from this example
Additionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.
## More information
Disclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a "hobby" to me. Training is done using a VPS on Google Cloud, I paid with my own money.
If you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL
| [
"## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:",
"## How to use it\n\nThe recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)\n\n\n\nAdditionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.\n\nThis is an example of a conversation using URL:\n\n- <|im_start|>system\n- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>\n- <|im_start|>user\n- > xin chào \n- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>\n- > trợ lý AI là gì? bạn giải thích được không?\n- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>\n- > ồ, cảm ơn, vậy bạn có thể làm gì?\n- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>\n- > tức là sao?\n- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>",
"## Training\n\nYou can also look at the training code in the 'URL' file.\n\nFor tokenizer, I changed these things:\n- Token '[INST]' to '<|im_start|>', make it become special token\n- Token '[/INST]' to '<|im_end|>', make it become special token\n- Change to 'eos_token' to '<|im_end|>'\n- Update 'chat_template' to chatml, taken from this example\n\nAdditionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.",
"## More information\n\nDisclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a \"hobby\" to me. Training is done using a VPS on Google Cloud, I paid with my own money.\n\nIf you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #LLMs #NLP #Vietnamese #conversational #vi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:",
"## How to use it\n\nThe recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)\n\n\n\nAdditionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.\n\nThis is an example of a conversation using URL:\n\n- <|im_start|>system\n- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>\n- <|im_start|>user\n- > xin chào \n- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>\n- > trợ lý AI là gì? bạn giải thích được không?\n- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>\n- > ồ, cảm ơn, vậy bạn có thể làm gì?\n- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>\n- > tức là sao?\n- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>",
"## Training\n\nYou can also look at the training code in the 'URL' file.\n\nFor tokenizer, I changed these things:\n- Token '[INST]' to '<|im_start|>', make it become special token\n- Token '[/INST]' to '<|im_end|>', make it become special token\n- Change to 'eos_token' to '<|im_end|>'\n- Update 'chat_template' to chatml, taken from this example\n\nAdditionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.",
"## More information\n\nDisclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a \"hobby\" to me. Training is done using a VPS on Google Cloud, I paid with my own money.\n\nIf you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL"
] | [
70,
98,
478,
141,
76
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #LLMs #NLP #Vietnamese #conversational #vi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vgm_v8.4.6
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0134
- Precision: 0.9190
- Recall: 0.9209
- F1: 0.9200
- Accuracy: 0.9976
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.32 | 100 | 0.0854 | 0.3760 | 0.1968 | 0.2583 | 0.9779 |
| No log | 0.64 | 200 | 0.0523 | 0.6511 | 0.5639 | 0.6043 | 0.9864 |
| No log | 0.96 | 300 | 0.0384 | 0.7068 | 0.7383 | 0.7222 | 0.9902 |
| No log | 1.29 | 400 | 0.0330 | 0.6878 | 0.7911 | 0.7358 | 0.9906 |
| 0.072 | 1.61 | 500 | 0.0302 | 0.7242 | 0.7830 | 0.7524 | 0.9918 |
| 0.072 | 1.93 | 600 | 0.0229 | 0.8036 | 0.8134 | 0.8085 | 0.9937 |
| 0.072 | 2.25 | 700 | 0.0182 | 0.8303 | 0.8235 | 0.8269 | 0.9947 |
| 0.072 | 2.57 | 800 | 0.0211 | 0.8584 | 0.8114 | 0.8342 | 0.9950 |
| 0.072 | 2.89 | 900 | 0.0166 | 0.8848 | 0.8722 | 0.8784 | 0.9959 |
| 0.0156 | 3.22 | 1000 | 0.0157 | 0.8687 | 0.8722 | 0.8704 | 0.9961 |
| 0.0156 | 3.54 | 1100 | 0.0161 | 0.8668 | 0.8844 | 0.8755 | 0.9964 |
| 0.0156 | 3.86 | 1200 | 0.0136 | 0.9215 | 0.9047 | 0.9130 | 0.9973 |
| 0.0156 | 4.18 | 1300 | 0.0143 | 0.8738 | 0.9128 | 0.8929 | 0.9969 |
| 0.0156 | 4.5 | 1400 | 0.0143 | 0.8770 | 0.9108 | 0.8935 | 0.9968 |
| 0.0062 | 4.82 | 1500 | 0.0140 | 0.8814 | 0.9047 | 0.8929 | 0.9970 |
| 0.0062 | 5.14 | 1600 | 0.0161 | 0.8610 | 0.9168 | 0.8880 | 0.9963 |
| 0.0062 | 5.47 | 1700 | 0.0136 | 0.8915 | 0.9168 | 0.9040 | 0.9972 |
| 0.0062 | 5.79 | 1800 | 0.0145 | 0.912 | 0.9249 | 0.9184 | 0.9974 |
| 0.0062 | 6.11 | 1900 | 0.0136 | 0.9052 | 0.9108 | 0.9080 | 0.9972 |
| 0.0031 | 6.43 | 2000 | 0.0134 | 0.9204 | 0.9148 | 0.9176 | 0.9974 |
| 0.0031 | 6.75 | 2100 | 0.0133 | 0.9167 | 0.9148 | 0.9157 | 0.9975 |
| 0.0031 | 7.07 | 2200 | 0.0134 | 0.9190 | 0.9209 | 0.9200 | 0.9976 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu121
- Datasets 2.2.2
- Tokenizers 0.13.3
| {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["sroie"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "vgm_v8.4.6", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "sroie", "type": "sroie", "config": "discharge", "split": "test", "args": "discharge"}, "metrics": [{"type": "precision", "value": 0.9190283400809717, "name": "Precision"}, {"type": "recall", "value": 0.920892494929006, "name": "Recall"}, {"type": "f1", "value": 0.9199594731509625, "name": "F1"}, {"type": "accuracy", "value": 0.9975940230467266, "name": "Accuracy"}]}]}]} | token-classification | DataIntelligenceTeam/vgm_v8.4.6 | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:sroie",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2024-02-13T15:53:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| vgm\_v8.4.6
===========
This model is a fine-tuned version of microsoft/layoutlmv3-base on the sroie dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0134
* Precision: 0.9190
* Recall: 0.9209
* F1: 0.9200
* Accuracy: 0.9976
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2200
### Training results
### Framework versions
* Transformers 4.28.0
* Pytorch 2.1.0+cu121
* Datasets 2.2.2
* Tokenizers 0.13.3
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.2.2\n* Tokenizers 0.13.3"
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.2.2\n* Tokenizers 0.13.3"
] | [
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97,
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"passage: TAGS\n#transformers #pytorch #tensorboard #layoutlmv3 #token-classification #generated_from_trainer #dataset-sroie #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 2200### Training results### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.2.2\n* Tokenizers 0.13.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0088
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1237 | 3.85 | 500 | 0.0088 | 1.0 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "vit_model", "results": []}]} | image-classification | IgnaCodeIA/vit_model | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-13T15:54:19+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| vit\_model
==========
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0088
* Accuracy: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
75,
97,
4,
30
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: andysalerno/mistral-sft-v3
model_type: AutoModelForCausalLM
load_in_8bit: true
load_in_4bit: false
strict: false
datasets:
- path: andysalerno/rainbowfish-v1
type:
system_prompt: ""
field_system: system
field_instruction: input
field_output: output
format: "{instruction}"
no_input_format: "{instruction}"
dataset_prepared_path: last_run_prepared
val_set_size: 0.005
output_dir: ./lora-out-rainbow10
adapter: lora
lora_model_dir:
sequence_len: 2048
sample_packing: false # was true
eval_sample_packing: false
pad_to_sequence_len: false
padding_side: left
lora_r: 64
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
lora_modules_to_save:
- embed_tokens
- lm_head
wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 4
optimizer: paged_adamw_8bit
lr_scheduler: linear
learning_rate: 2e-5
neftune_noise_alpha: 5
train_on_inputs: false
group_by_length: false
bf16: true
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
# early_stopping_patience: 3
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3
hub_strategy: "all_checkpoints"
hub_model_id: andysalerno/rainbowfish-v10-adapter
num_epochs: 3
warmup_steps: 100
eval_steps: 200
eval_table_size:
eval_table_max_new_tokens: 128
# max_steps: 500
saves_per_epoch: 3
debug:
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
bos_token: "<|im_start|>"
eos_token: "<|im_end|>"
unk_token: "<unk>"
```
</details><br>
# rainbowfish-v10-adapter
This model is a fine-tuned version of [andysalerno/mistral-sft-v3](https://huggingface.co/andysalerno/mistral-sft-v3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6439
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6538 | 0.18 | 200 | 0.6836 |
| 0.6886 | 0.37 | 400 | 0.6705 |
| 0.6656 | 0.55 | 600 | 0.6627 |
| 0.6934 | 0.74 | 800 | 0.6574 |
| 0.7166 | 0.92 | 1000 | 0.6538 |
| 0.5317 | 1.11 | 1200 | 0.6520 |
| 0.6308 | 1.29 | 1400 | 0.6503 |
| 0.627 | 1.47 | 1600 | 0.6488 |
| 0.6378 | 1.66 | 1800 | 0.6473 |
| 0.6672 | 1.84 | 2000 | 0.6457 |
| 0.6495 | 2.03 | 2200 | 0.6452 |
| 0.6304 | 2.21 | 2400 | 0.6452 |
| 0.5909 | 2.4 | 2600 | 0.6447 |
| 0.6009 | 2.58 | 2800 | 0.6443 |
| 0.7394 | 2.76 | 3000 | 0.6443 |
| 0.606 | 2.95 | 3200 | 0.6439 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.17.0
- Tokenizers 0.15.0 | {"license": "apache-2.0", "library_name": "peft", "tags": ["axolotl", "generated_from_trainer"], "datasets": ["andysalerno/rainbowfish-v1"], "base_model": "andysalerno/mistral-sft-v3", "model-index": [{"name": "rainbowfish-7B-v10", "results": []}]} | null | andysalerno/rainbowfish-7B-v10 | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"dataset:andysalerno/rainbowfish-v1",
"base_model:andysalerno/mistral-sft-v3",
"license:apache-2.0",
"region:us"
] | 2024-02-13T15:54:29+00:00 | [] | [] | TAGS
#peft #safetensors #mistral #axolotl #generated_from_trainer #dataset-andysalerno/rainbowfish-v1 #base_model-andysalerno/mistral-sft-v3 #license-apache-2.0 #region-us
| <img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
rainbowfish-v10-adapter
=======================
This model is a fine-tuned version of andysalerno/mistral-sft-v3 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6439
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 3
### Training results
### Framework versions
* PEFT 0.8.2
* Transformers 4.38.0.dev0
* Pytorch 2.1.2+cu118
* Datasets 2.17.0
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 3",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.0"
] | [
70,
178,
4,
44
] | [
"passage: TAGS\n#peft #safetensors #mistral #axolotl #generated_from_trainer #dataset-andysalerno/rainbowfish-v1 #base_model-andysalerno/mistral-sft-v3 #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.0"
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] |
null | null | diffusers |
# DreamBooth trained by AutoTrain
Text encoder was not trained.
| {"tags": ["text-to-image", "diffusers", "autotrain"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "<1girl, 20 years old British girl called Amelia. white skin model, detailed face ((( face with a anglular jawline, brown eyes that hint at both strength and vulnerability, and luscious))), detailed eyes ((( brown eyes that hint at both strength and vulnerability))), detailed hair(((cascading curls of long hair. Illuminate the depth of her gaze and the way the curls frame her face, adding an element of sophistication.))), detailed perfect body, ((( medium breast, huge hip))), >", "inference": true} | text-to-image | AmilaUvaz/autotrain-qoxeh-etjgq | [
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"has_space",
"region:us"
] | 2024-02-13T15:56:02+00:00 | [] | [] | TAGS
#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us
|
# DreamBooth trained by AutoTrain
Text encoder was not trained.
| [
"# DreamBooth trained by AutoTrain\n\nText encoder was not trained."
] | [
"TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n",
"# DreamBooth trained by AutoTrain\n\nText encoder was not trained."
] | [
45,
19
] | [
"passage: TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n# DreamBooth trained by AutoTrain\n\nText encoder was not trained."
] | [
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null | null | transformers |
## Model Description
This model is finetuned from [Viet-Mistral/Vistral-7B-Chat](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat). The dataset is taken from [bkai-foundation-models/vi-self-chat-sharegpt-format](https://huggingface.co/datasets/bkai-foundation-models/vi-self-chat-sharegpt-format)
This is a **low rank** finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.
The format looks like this:
```
<|im_start|>system
Provide some context and/or instructions to the model.
<|im_end|>
<|im_start|>user
The user’s message goes here
<|im_end|>
<|im_start|>assistant
```
## How to use it
The recommended way is to use the GGUF `vistral-7b-chatml-Q4_K_M.gguf` file included in this repository. Run it via llama.cpp (remember to pass `-cml` argument to use chatml template)
```bash
./main -m vistral-7b-chatml-Q4_K_M.gguf -p "Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực." -cml
```
Additionally, you can run the `python3 run.py` inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.
This is an example of a conversation using llama.cpp:
- <|im_start|>system
- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>
- <|im_start|>user
- > xin chào
- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>
- > trợ lý AI là gì? bạn giải thích được không?
- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>
- > ồ, cảm ơn, vậy bạn có thể làm gì?
- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>
- > tức là sao?
- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>
## Training
You can also look at the training code in the `finetune.py` file.
For tokenizer, I changed these things:
- Token `[INST]` to `<|im_start|>`, make it become special token
- Token `[/INST]` to `<|im_end|>`, make it become special token
- Change to `eos_token` to `<|im_end|>`
- Update `chat_template` to chatml, taken from [this example](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/tokenizer_config.json#L52)
Additionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.
## More information
Disclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a "hobby" to me. Training is done using a VPS on Google Cloud, I paid with my own money.
If you want to discuss, feel free to contact me at `contact at ngxson dot com` - [ngxson.com](https://ngxson.com)
| {"language": ["vi"], "license": "mit", "library_name": "transformers", "tags": ["LLMs", "NLP", "Vietnamese"]} | text-generation | LoneStriker/Vistral-7B-ChatML-8.0bpw-h8-exl2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"LLMs",
"NLP",
"Vietnamese",
"conversational",
"vi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T15:56:08+00:00 | [] | [
"vi"
] | TAGS
#transformers #safetensors #mistral #text-generation #LLMs #NLP #Vietnamese #conversational #vi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Model Description
This model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format
This is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.
The format looks like this:
## How to use it
The recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)
Additionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.
This is an example of a conversation using URL:
- <|im_start|>system
- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>
- <|im_start|>user
- > xin chào
- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>
- > trợ lý AI là gì? bạn giải thích được không?
- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>
- > ồ, cảm ơn, vậy bạn có thể làm gì?
- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>
- > tức là sao?
- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>
## Training
You can also look at the training code in the 'URL' file.
For tokenizer, I changed these things:
- Token '[INST]' to '<|im_start|>', make it become special token
- Token '[/INST]' to '<|im_end|>', make it become special token
- Change to 'eos_token' to '<|im_end|>'
- Update 'chat_template' to chatml, taken from this example
Additionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.
## More information
Disclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a "hobby" to me. Training is done using a VPS on Google Cloud, I paid with my own money.
If you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL
| [
"## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:",
"## How to use it\n\nThe recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)\n\n\n\nAdditionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.\n\nThis is an example of a conversation using URL:\n\n- <|im_start|>system\n- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>\n- <|im_start|>user\n- > xin chào \n- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>\n- > trợ lý AI là gì? bạn giải thích được không?\n- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>\n- > ồ, cảm ơn, vậy bạn có thể làm gì?\n- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>\n- > tức là sao?\n- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>",
"## Training\n\nYou can also look at the training code in the 'URL' file.\n\nFor tokenizer, I changed these things:\n- Token '[INST]' to '<|im_start|>', make it become special token\n- Token '[/INST]' to '<|im_end|>', make it become special token\n- Change to 'eos_token' to '<|im_end|>'\n- Update 'chat_template' to chatml, taken from this example\n\nAdditionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.",
"## More information\n\nDisclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a \"hobby\" to me. Training is done using a VPS on Google Cloud, I paid with my own money.\n\nIf you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #LLMs #NLP #Vietnamese #conversational #vi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:",
"## How to use it\n\nThe recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)\n\n\n\nAdditionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.\n\nThis is an example of a conversation using URL:\n\n- <|im_start|>system\n- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>\n- <|im_start|>user\n- > xin chào \n- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>\n- > trợ lý AI là gì? bạn giải thích được không?\n- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>\n- > ồ, cảm ơn, vậy bạn có thể làm gì?\n- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>\n- > tức là sao?\n- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>",
"## Training\n\nYou can also look at the training code in the 'URL' file.\n\nFor tokenizer, I changed these things:\n- Token '[INST]' to '<|im_start|>', make it become special token\n- Token '[/INST]' to '<|im_end|>', make it become special token\n- Change to 'eos_token' to '<|im_end|>'\n- Update 'chat_template' to chatml, taken from this example\n\nAdditionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.",
"## More information\n\nDisclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a \"hobby\" to me. Training is done using a VPS on Google Cloud, I paid with my own money.\n\nIf you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL"
] | [
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null | null | setfit |
# SetFit with Sahajtomar/french_semantic
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [Sahajtomar/french_semantic](https://huggingface.co/Sahajtomar/french_semantic) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [Sahajtomar/french_semantic](https://huggingface.co/Sahajtomar/french_semantic)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 514 tokens
- **Number of Classes:** 18 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 14 | <ul><li>'Salut, tout le monde va bien ?'</li><li>'Salut, ça va bien ?'</li><li>'Bonne journée!'</li></ul> |
| 11 | <ul><li>"Quelle est la politique de la banque CDM en cas de défaut de paiement ou de retard dans les contributions à un plan d'épargne éducation?"</li><li>"Est-il possible de nommer un bénéficiaire supplémentaire ou de modifier le bénéficiaire désigné pour un plan d'épargne éducation chez la banque CDM?"</li><li>"Quels sont les différents niveaux de risque associés aux produits d'épargne éducation proposés par la banque CDM?"</li></ul> |
| 1 | <ul><li>"Y a-t-il des conditions spécifiques à respecter pour bénéficier des avantages de l'assurance épargne?"</li><li>"Quels sont les avantages fiscaux associés à la souscription d'une assurance épargne chez CDM?"</li><li>"Est-ce que l'assurance épargne chez CDM offre des options de transfert automatique de fonds depuis mon compte courant ou d'autres comptes?"</li></ul> |
| 8 | <ul><li>'Un prêt à la consommation serait idéal pour couvrir les frais médicaux non prévus.'</li><li>"Je recherche des options de financement pour un voyage et je m'intéresse à un prêt à la consommation."</li><li>'quels sont les avantages du credit a la consommation'</li></ul> |
| 0 | <ul><li>'Quels sont les coûts associés à cette assurance, et comment sont-ils facturés?'</li><li>"Quels documents ou informations supplémentaires sont nécessaires lors de la souscription à l'assurance décès et invalidité chez la banque CDM?"</li><li>'Proposez-vous des assurances décès et invalidité chez CDM?'</li></ul> |
| 16 | <ul><li>"Quels types de protections en cas d'accident proposez-vous?"</li><li>"Puis-je transférer ma protection en cas d'accident si je change de véhicule ou de situation?"</li><li>"Quels sont les documents nécessaires en cas de déclaration d'accident pour bénéficier de la couverture?"</li></ul> |
| 2 | <ul><li>"Quels types de pertes ou de dommages sont couverts par l'assurance des moyens de paiement?"</li><li>"Existe-t-il des options de couverture supplémentaires que je peux ajouter à mon contrat d'assurance des moyens de paiement ?"</li><li>"Quelle est la période de couverture de l'assurance des moyens de paiement?"</li></ul> |
| 13 | <ul><li>"C'était utile, merci!"</li><li>"C'était informatif, au revoir!"</li><li>'BY'</li></ul> |
| 12 | <ul><li>'Est-ce que la banque CDM offre des services de conseil financier pour aider les clients à planifier leur retraite?'</li><li>'Y a-t-il des incitations spéciales pour les jeunes travailleurs ou les personnes qui commencent à épargner tôt pour leur retraite?'</li><li>"Quelles sont les conséquences fiscales si je décide de transférer mon plan d'épargne retraite vers une autre institution financière?"</li></ul> |
| 7 | <ul><li>"Y a-t-il des frais supplémentaires associés à l'obtention d'un crédit auto, comme les frais de traitement du dossier?"</li><li>"Est-ce que CDM propose des prêts auto pour les véhicules d'occasion ?"</li><li>"Quelle est la durée typique d'un prêt automobile chez CDM?"</li></ul> |
| 4 | <ul><li>'Quels services puis-je effectuer en ligne, tels que le virement de fonds ou le paiement de factures?'</li><li>'Quelle est la politique de la banque CDM en matière de protection de la vie privée pour les services bancaires en ligne?'</li><li>"Comment puis-je m'inscrire pour les services de banque en ligne de CDM?"</li></ul> |
| 5 | <ul><li>'Quelle est la politique de remboursement en cas de fraude sur ma carte bancaire?'</li><li>'Comment fonctionne le processus de renouvellement de ma carte bancaire expirée?'</li><li>"Quels sont les critères d'admissibilité pour obtenir une carte de crédit chez CDM?"</li></ul> |
| 10 | <ul><li>"quels types de comptes d'epargne proposez vous"</li><li>"Je suis à la recherche de conseils sur la constitution d'un fonds d'urgence pour faire face aux dépenses imprévues."</li><li>"L'épargne régulière est une habitude financière saine qui peut apporter une tranquillité d'esprit à long terme."</li></ul> |
| 6 | <ul><li>"J'aimerais savoir comment joindre le service de planification financière de la banque CDM pour discuter de mes objectifs financiers à long terme."</li><li>'Comment puis-je contacter le service de gestion des dépôts à terme de la banque CDM pour ouvrir un nouveau compte ?'</li><li>"Où puis-je trouver les coordonnées du service de gestion des transferts internationaux de la banque CDM pour effectuer des virements à l'étranger ?"</li></ul> |
| 9 | <ul><li>'Je veux un crédit pour acheter une maison'</li><li>"La banque CDM offre-t-elle des conseils ou des services d'accompagnement pour les nouveaux propriétaires immobiliers?"</li><li>"Quels sont les avantages ou les programmes spéciaux liés à l'obtention d'un crédit immobilier chez la banque CDM?"</li></ul> |
| 17 | <ul><li>"Y a-t-il des frais associés à la réalisation d'une simulation de crédit chez la banque CDM?"</li><li>'Puis-je réaliser une simulation de crédit pour un prêt personnel, un crédit auto et un crédit immobilier en même temps chez la banque CDM?'</li><li>'Existe-t-il une assistance disponible pour comprendre les résultats de la simulation de crédit chez la banque CDM?'</li></ul> |
| 3 | <ul><li>'assurance habitation'</li><li>"Est-ce que l'assurance multirisques habitation couvre les dommages causés par des catastrophes naturelles?"</li><li>"Quels sont les délais de traitement des demandes de remboursement en cas de sinistre avec l'assurance multirisques habitation de la banque CDM?"</li></ul> |
| 15 | <ul><li>'Quelles sont les options de gestion de compte en ligne disponibles?'</li><li>'je desire creer un compte chez vous'</li><li>"Comment puis-je transférer mes comptes existants d'une autre banque vers CDM?"</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9667 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("yazidtagnaouti/maes")
# Run inference
preds = model("Quelle est la durée typique d'un prêt auto chez la banque CDM?")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 14.5053 | 28 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 16 |
| 1 | 16 |
| 2 | 16 |
| 3 | 16 |
| 4 | 16 |
| 5 | 16 |
| 6 | 15 |
| 7 | 16 |
| 8 | 16 |
| 9 | 16 |
| 10 | 15 |
| 11 | 16 |
| 12 | 16 |
| 13 | 16 |
| 14 | 16 |
| 15 | 16 |
| 16 | 16 |
| 17 | 15 |
### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: True
- use_amp: False
- warmup_proportion: 0.1
- max_length: 256
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0014 | 1 | 0.1659 | - |
| 0.0701 | 50 | 0.044 | - |
| 0.1403 | 100 | 0.0374 | - |
| 0.2104 | 150 | 0.0624 | - |
| 0.2805 | 200 | 0.005 | - |
| 0.3506 | 250 | 0.0022 | - |
| 0.4208 | 300 | 0.0042 | - |
| 0.4909 | 350 | 0.0012 | - |
| 0.5610 | 400 | 0.0016 | - |
| 0.6311 | 450 | 0.001 | - |
| 0.7013 | 500 | 0.0006 | - |
| 0.7714 | 550 | 0.0006 | - |
| 0.8415 | 600 | 0.0009 | - |
| 0.9116 | 650 | 0.0005 | - |
| 0.9818 | 700 | 0.0006 | - |
| **1.0** | **713** | **-** | **0.0202** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.0
- Tokenizers: 0.15.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> | {"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["accuracy"], "widget": [{"text": "Quelle est la dur\u00e9e typique d'un pr\u00eat auto chez la banque CDM?"}, {"text": "Y a-t-il des services d'assistance suppl\u00e9mentaires inclus dans l'assurance d\u00e9c\u00e8s et invalidit\u00e9, tels que des conseils juridiques ou financiers en cas de besoin?"}, {"text": "Y a-t-il des restrictions quant au montant maximum couvert par l'assurance des moyens de paiement ?"}, {"text": "Est-il possible de r\u00e9aliser une simulation de cr\u00e9dit pour diff\u00e9rents montants et dur\u00e9es de pr\u00eat chez la banque CDM?"}, {"text": "Quels sont les avantages li\u00e9s \u00e0 l'utilisation d'une carte de cr\u00e9dit plut\u00f4t qu'une carte de d\u00e9bit?"}], "pipeline_tag": "text-classification", "inference": true, "base_model": "Sahajtomar/french_semantic", "model-index": [{"name": "SetFit with Sahajtomar/french_semantic", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "Unknown", "type": "unknown", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.9666666666666667, "name": "Accuracy"}]}]}]} | text-classification | yazidtagnaouti/maes | [
"setfit",
"safetensors",
"camembert",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:Sahajtomar/french_semantic",
"model-index",
"region:us"
] | 2024-02-13T16:01:20+00:00 | [
"2209.11055"
] | [] | TAGS
#setfit #safetensors #camembert #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-Sahajtomar/french_semantic #model-index #region-us
| SetFit with Sahajtomar/french\_semantic
=======================================
This is a SetFit model that can be used for Text Classification. This SetFit model uses Sahajtomar/french\_semantic as the Sentence Transformer embedding model. A SetFitHead instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
-------------
### Model Description
* Model Type: SetFit
* Sentence Transformer body: Sahajtomar/french\_semantic
* Classification head: a SetFitHead instance
* Maximum Sequence Length: 514 tokens
* Number of Classes: 18 classes
### Model Sources
* Repository: SetFit on GitHub
* Paper: Efficient Few-Shot Learning Without Prompts
* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
### Model Labels
Evaluation
----------
### Metrics
Uses
----
### Direct Use for Inference
First install the SetFit library:
Then you can load this model and run inference.
Training Details
----------------
### Training Set Metrics
### Training Hyperparameters
* batch\_size: (16, 2)
* num\_epochs: (1, 16)
* max\_steps: -1
* sampling\_strategy: oversampling
* num\_iterations: 20
* body\_learning\_rate: (2e-05, 1e-05)
* head\_learning\_rate: 0.01
* loss: CosineSimilarityLoss
* distance\_metric: cosine\_distance
* margin: 0.25
* end\_to\_end: True
* use\_amp: False
* warmup\_proportion: 0.1
* max\_length: 256
* seed: 42
* eval\_max\_steps: -1
* load\_best\_model\_at\_end: True
### Training Results
* The bold row denotes the saved checkpoint.
### Framework Versions
* Python: 3.10.12
* SetFit: 1.0.3
* Sentence Transformers: 2.3.1
* Transformers: 4.35.2
* PyTorch: 2.1.0+cu121
* Datasets: 2.17.0
* Tokenizers: 0.15.1
### BibTeX
| [
"### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: Sahajtomar/french\\_semantic\n* Classification head: a SetFitHead instance\n* Maximum Sequence Length: 514 tokens\n* Number of Classes: 18 classes",
"### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts",
"### Model Labels\n\n\n\nEvaluation\n----------",
"### Metrics\n\n\n\nUses\n----",
"### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------",
"### Training Set Metrics",
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"### Training Results\n\n\n\n* The bold row denotes the saved checkpoint.",
"### Framework Versions\n\n\n* Python: 3.10.12\n* SetFit: 1.0.3\n* Sentence Transformers: 2.3.1\n* Transformers: 4.35.2\n* PyTorch: 2.1.0+cu121\n* Datasets: 2.17.0\n* Tokenizers: 0.15.1",
"### BibTeX"
] | [
"TAGS\n#setfit #safetensors #camembert #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-Sahajtomar/french_semantic #model-index #region-us \n",
"### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: Sahajtomar/french\\_semantic\n* Classification head: a SetFitHead instance\n* Maximum Sequence Length: 514 tokens\n* Number of Classes: 18 classes",
"### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts",
"### Model Labels\n\n\n\nEvaluation\n----------",
"### Metrics\n\n\n\nUses\n----",
"### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------",
"### Training Set Metrics",
"### Training Hyperparameters\n\n\n* batch\\_size: (16, 2)\n* num\\_epochs: (1, 16)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* num\\_iterations: 20\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: True\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* max\\_length: 256\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: True",
"### Training Results\n\n\n\n* The bold row denotes the saved checkpoint.",
"### Framework Versions\n\n\n* Python: 3.10.12\n* SetFit: 1.0.3\n* Sentence Transformers: 2.3.1\n* Transformers: 4.35.2\n* PyTorch: 2.1.0+cu121\n* Datasets: 2.17.0\n* Tokenizers: 0.15.1",
"### BibTeX"
] | [
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] | [
"passage: TAGS\n#setfit #safetensors #camembert #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-Sahajtomar/french_semantic #model-index #region-us \n### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: Sahajtomar/french\\_semantic\n* Classification head: a SetFitHead instance\n* Maximum Sequence Length: 514 tokens\n* Number of Classes: 18 classes### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts### Model Labels\n\n\n\nEvaluation\n----------### Metrics\n\n\n\nUses\n----### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------### Training Set Metrics### Training Hyperparameters\n\n\n* batch\\_size: (16, 2)\n* num\\_epochs: (1, 16)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* num\\_iterations: 20\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: True\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* max\\_length: 256\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: True### Training Results\n\n\n\n* The bold row denotes the saved checkpoint.### Framework Versions\n\n\n* Python: 3.10.12\n* SetFit: 1.0.3\n* Sentence Transformers: 2.3.1\n* Transformers: 4.35.2\n* PyTorch: 2.1.0+cu121\n* Datasets: 2.17.0\n* Tokenizers: 0.15.1### BibTeX"
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null | null | transformers |
# AIFT-42dot_LLM-PLM-1.3B-ao-instruct-all-v1.0
베이스 모델 : 42dot/42dot_LLM-PLM-1.3B
학습 데이터 : 자체 제작한 Open Orca 스타일 데이터셋 약 48,000건 (중복 제거 및 데이터 분포 조정)
학습 방법 : Full finetuning
epoch : 3
## ko-lm-evaluation-harness(5-shot)
|kobest_boolq|kobest_copa|kobest_hellaswag|pawsx_ko|
|--|--|--|--|
|0.5220797720797721|0.72|0.458|0.563|
## Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.0.0
- Tokenizers 0.15.0 | {"license": "cc-by-nc-4.0"} | text-generation | mu0gum/AIFT-42dot_LLM-PLM-1.3B-ao-instruct-all-v1.0 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T16:05:06+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| AIFT-42dot\_LLM-PLM-1.3B-ao-instruct-all-v1.0
=============================================
베이스 모델 : 42dot/42dot\_LLM-PLM-1.3B
학습 데이터 : 자체 제작한 Open Orca 스타일 데이터셋 약 48,000건 (중복 제거 및 데이터 분포 조정)
학습 방법 : Full finetuning
epoch : 3
ko-lm-evaluation-harness(5-shot)
--------------------------------
Framework versions
------------------
* Transformers 4.36.2
* Pytorch 2.1.2+cu121
* Datasets 2.0.0
* Tokenizers 0.15.0
| [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
58
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dummy-model
This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| {"license": "other", "tags": ["generated_from_trainer"], "base_model": "Qwen/Qwen1.5-0.5B-Chat", "model-index": [{"name": "dummy-model", "results": []}]} | text-generation | lewtun/dummy-model | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen1.5-0.5B-Chat",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-13T16:05:25+00:00 | [] | [] | TAGS
#transformers #safetensors #qwen2 #text-generation #generated_from_trainer #conversational #base_model-Qwen/Qwen1.5-0.5B-Chat #license-other #autotrain_compatible #endpoints_compatible #region-us
|
# dummy-model
This model is a fine-tuned version of Qwen/Qwen1.5-0.5B-Chat on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| [
"# dummy-model\n\nThis model is a fine-tuned version of Qwen/Qwen1.5-0.5B-Chat on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 2\n- total_train_batch_size: 16\n- total_eval_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- training_steps: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #qwen2 #text-generation #generated_from_trainer #conversational #base_model-Qwen/Qwen1.5-0.5B-Chat #license-other #autotrain_compatible #endpoints_compatible #region-us \n",
"# dummy-model\n\nThis model is a fine-tuned version of Qwen/Qwen1.5-0.5B-Chat on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 2\n- total_train_batch_size: 16\n- total_eval_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- training_steps: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
71,
33,
6,
12,
8,
3,
146,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #qwen2 #text-generation #generated_from_trainer #conversational #base_model-Qwen/Qwen1.5-0.5B-Chat #license-other #autotrain_compatible #endpoints_compatible #region-us \n# dummy-model\n\nThis model is a fine-tuned version of Qwen/Qwen1.5-0.5B-Chat on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 2\n- total_train_batch_size: 16\n- total_eval_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- training_steps: 1### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-cased-lora-text-classification
This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5474
- Precision: 0.7635
- Recall: 0.9338
- F1 and accuracy: {'accuracy': 0.7456359102244389, 'f1': 0.8401253918495297}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 and accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:----------------------------------------------------------:|
| No log | 1.0 | 401 | 0.6017 | 0.7157 | 1.0 | {'accuracy': 0.71571072319202, 'f1': 0.8343023255813953} |
| 0.5798 | 2.0 | 802 | 0.5967 | 0.7157 | 1.0 | {'accuracy': 0.71571072319202, 'f1': 0.8343023255813953} |
| 0.5546 | 3.0 | 1203 | 0.5722 | 0.7157 | 1.0 | {'accuracy': 0.71571072319202, 'f1': 0.8343023255813953} |
| 0.5403 | 4.0 | 1604 | 0.5624 | 0.7259 | 0.9965 | {'accuracy': 0.7281795511221946, 'f1': 0.8399412628487517} |
| 0.5206 | 5.0 | 2005 | 0.5597 | 0.7368 | 0.9756 | {'accuracy': 0.7331670822942643, 'f1': 0.8395802098950524} |
| 0.5206 | 6.0 | 2406 | 0.5588 | 0.7520 | 0.9617 | {'accuracy': 0.7456359102244389, 'f1': 0.8440366972477064} |
| 0.5153 | 7.0 | 2807 | 0.5679 | 0.7554 | 0.9686 | {'accuracy': 0.7531172069825436, 'f1': 0.8488549618320611} |
| 0.4959 | 8.0 | 3208 | 0.5693 | 0.7576 | 0.9582 | {'accuracy': 0.7506234413965087, 'f1': 0.8461538461538461} |
| 0.4801 | 9.0 | 3609 | 0.5466 | 0.7635 | 0.9338 | {'accuracy': 0.7456359102244389, 'f1': 0.8401253918495297} |
| 0.4949 | 10.0 | 4010 | 0.5474 | 0.7635 | 0.9338 | {'accuracy': 0.7456359102244389, 'f1': 0.8401253918495297} |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall"], "base_model": "distilbert/distilbert-base-multilingual-cased", "model-index": [{"name": "distilbert-base-multilingual-cased-lora-text-classification", "results": []}]} | null | nhankins/zh_distilbert_lora_adapter_1.0 | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-multilingual-cased",
"license:apache-2.0",
"region:us"
] | 2024-02-13T16:05:36+00:00 | [] | [] | TAGS
#tensorboard #safetensors #generated_from_trainer #base_model-distilbert/distilbert-base-multilingual-cased #license-apache-2.0 #region-us
| distilbert-base-multilingual-cased-lora-text-classification
===========================================================
This model is a fine-tuned version of distilbert/distilbert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5474
* Precision: 0.7635
* Recall: 0.9338
* F1 and accuracy: {'accuracy': 0.7456359102244389, 'f1': 0.8401253918495297}
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-distilbert/distilbert-base-multilingual-cased #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-cased-lora-text-classification
This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5714
- Precision: 0.7417
- Recall: 1.0
- F1 and accuracy: {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 and accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:----------------------------------------------------------:|
| No log | 1.0 | 391 | 0.5780 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.647 | 2.0 | 782 | 0.5748 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6216 | 3.0 | 1173 | 0.5713 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6201 | 4.0 | 1564 | 0.5726 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6201 | 5.0 | 1955 | 0.5765 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6199 | 6.0 | 2346 | 0.5756 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6365 | 7.0 | 2737 | 0.5827 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6165 | 8.0 | 3128 | 0.5715 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6185 | 9.0 | 3519 | 0.5715 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6185 | 10.0 | 3910 | 0.5714 | 0.7417 | 1.0 | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall"], "base_model": "distilbert/distilbert-base-multilingual-cased", "model-index": [{"name": "distilbert-base-multilingual-cased-lora-text-classification", "results": []}]} | null | nhankins/en_distilbert_lora_adapter_2.0 | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-multilingual-cased",
"license:apache-2.0",
"region:us"
] | 2024-02-13T16:08:43+00:00 | [] | [] | TAGS
#tensorboard #safetensors #generated_from_trainer #base_model-distilbert/distilbert-base-multilingual-cased #license-apache-2.0 #region-us
| distilbert-base-multilingual-cased-lora-text-classification
===========================================================
This model is a fine-tuned version of distilbert/distilbert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5714
* Precision: 0.7417
* Recall: 1.0
* F1 and accuracy: {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847}
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.001
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-distilbert/distilbert-base-multilingual-cased #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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] |
null | null | transformers |
# FractalGPT/EmbedderDecoder
* **Оригинальная модель**
[[ai-forever/rugpt3small_based_on_gpt2](https://huggingface.co/ai-forever/rugpt3small_based_on_gpt2)]
* **Код генерации вдохновлен этим проектом**
[[vector2text](https://github.com/Koziev/vector2text)]
* Заменен эмбеддер
* Добавлена возможность задать промпт
* Вместо нулей вектор дополняется квадратами чисел (далее можно кубами и т.д.)
* Создан класс для генератора
* Добавлен ранжировщик
* Заменена модель вместо large — small
* Убран top_p
* Добавлен расчет среднего эмбеддинга (для ранжировщика в случае работы с массивом)
* Добавлена работа с матрицами эмбеддингов
* Добавлены 2 новых способа смеси эмбеддингов:
* Эмбеддинги в первой степени из одного текста, а квадраты из другого(в эмбеддинги и их квадраты также можно включать разную по структуре информацию)
* Передавать массив эмбеддингов и их квадратов
* **Пример использования**
```python
import torch
import numpy as np
from torch.nn import functional as F
from transformers import GPT2Tokenizer, GPT2LMHeadModel
def top_filtering(logits, top_k):
"""
Фильтрация top-k, в фильтрации top-p в этой задаче особо смысла нет
код с top-p: https://github.com/ictnlp/DSTC8-AVSD/blob/master/generate.py
"""
assert logits.dim() == 1
top_k = min(top_k, logits.size(-1))
if top_k > 0:
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits[indices_to_remove] = -float('Inf')
return logits
class TextEmbdGenerator:
def __init__(self, name_or_path, sbert, device = None):
"""
Инициализация генератора текста с моделью и токенизатором.
name_or_path: путь до модели токенизатора или ее имя для загрузки из Hugging Face.
sbert: модель для ранжирования (такая же что и создает эбеддинги)
"""
self.device = device
if self.device == None:
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.tokenizer = GPT2Tokenizer.from_pretrained(name_or_path)
self.model = GPT2LMHeadModel.from_pretrained(name_or_path).to(self.device)
self.sbert = sbert
def __get_embds(self, embds, sqr_embds):
'''Работает с матрицей эмбеддингов'''
list_emb = []
sq = embds if sqr_embds == None else sqr_embds
for i, embd in enumerate(embds):
vector = np.concatenate([embd,sq[i]**2])
list_emb.append(list(vector))
return torch.FloatTensor(list_emb).to(self.device)
def __det_mean(self, embds):
'''Получение среднего'''
m = np.zeros((384))
for embd in embds:
m += embd
m /= len(embds)
return m
def generate_embedding(self, embds, prompt = '', sqr_embds = None, temperature=0.26, top_k=4, max_len=100):
"""
Генерация текста на основе начального эмбеддинга и заданного начального текста.
"""
current_output_ids = self.tokenizer.encode(prompt) # Промпт
embedding = self.__get_embds(embds, sqr_embds) # Матрица входа
word_tokens = self.model.base_model.wte # Словарь токенов
while len(current_output_ids) < max_len:
with torch.no_grad():
outp_ids_tensor = torch.LongTensor(current_output_ids).to(self.device) # Выходы
token_embeddings = word_tokens(outp_ids_tensor) # эмбеддинги
input_vectors = torch.vstack((embedding, token_embeddings)).unsqueeze(dim=0)
output_model = self.model(inputs_embeds=input_vectors)
logits = output_model.logits
if isinstance(logits, tuple):
logits = logits[0]
logits = logits[0, -1, :]
logits /= temperature
logits = top_filtering(logits, top_k)
probs = F.softmax(logits, dim=-1)
prev = torch.multinomial(probs, 1)
if prev.item() == self.tokenizer.eos_token_id:
break
current_output_ids.append(prev.item())
output_text = self.tokenizer.decode(current_output_ids)
return output_text.split('\n')[0]
def cosine_similarity(self, x, y):
"""Вычисление косинусного сходства."""
return np.dot(x, y) / (np.linalg.norm(x) * np.linalg.norm(y))
def generate_with_ranker(self, embds, prompt = '', sqr_embds = None, seq=10, temperature=0.6, top_k=10, max_len=100):
"""Генерация и ранжирование текста. Поумолчанию создаются 10 текстов"""
sequences = [self.generate_embedding(embds, prompt, sqr_embds, temperature, top_k, max_len) for _ in range(seq)]
sequences = list(set(sequences)) # Удаление дубликатов
# Ранжирование
embd = self.__det_mean(embds)
embeddings = self.sbert.encode(sequences)
similarities = [self.cosine_similarity(embd, emb) for emb in embeddings]
best_index = np.argmax(similarities)
return sequences[best_index]
```
---
```bash
pip install sentence-transformers -q
```
```python
from sentence_transformers import SentenceTransformer
sbert = SentenceTransformer('FractalGPT/SbertDistil')
generator = TextEmbdGenerator('FractalGPT/EmbedderDecoder', sbert)
```
```python
embd = sbert.encode('там живут англичане')
generator.generate_with_ranker([embd])
```
```bash
>>> я бы его в Англию привез.
```
```python
embd = sbert.encode('там живут немцы')
generator.generate_with_ranker([embd], prompt = 'он всегда был в')
```
```bash
>>> он всегда был в Германии
```
```python
embd = sbert.encode('он сделает вывод на основе анализа ситуации')
generator.generate_with_ranker(embd)
```
```bash
>>> в процессе анализа ситуации необходимо выяснить:
```
```python
embd = sbert.encode('машина') - sbert.encode('колеса') + sbert.encode('крылья')
generator.generate_with_ranker([embd], 'это')
```
```bash
>>> этот самолёт
```
```python
embd = sbert.encode('полицейский - главный герой') + sbert.encode('Произошло ужасное событие в фильме')
embd /= 2
generator.generate_with_ranker([embd], 'Собеседование на')
```
```bash
>>> Собеседование на роль главного героя фильма — молодого лейтенанта полиции — происходит в доме
```
**После дообучения**
```python
embd_1 = sbert.encode('полицейский - главный герой')
embd_2 = sbert.encode('Произошло событие в фильме')
generator.generate_with_ranker([embd_1, embd_2], 'В ')
```
```bash
>>> В этом фильме главный герой - полицейский.
```
```python
embd_1 = sbert.encode('полицейский - главный герой')
embd_2 = sbert.encode('Произошло событие в фильме')
generator.generate_with_ranker([embd_1], 'Это', [embd_2])
```
```bash
>>> Это полицейский, который в полицейском участке снимается в фильме.
```
```python
embd = sbert.encode('радиоприемник')
ans = vector_answer(embd, 'Как это устроено?')
print(ans)
```
```bash
>>> Радиоволны распространяются в воздухе, создавая электромагнитное поле, которое может быть использовано для передачи информации.
``` | {"license": "apache-2.0", "datasets": ["wikimedia/wikipedia"]} | text-generation | FractalGPT/EmbedderDecoder | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"dataset:wikimedia/wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T16:09:17+00:00 | [] | [] | TAGS
#transformers #safetensors #gpt2 #text-generation #dataset-wikimedia/wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# FractalGPT/EmbedderDecoder
* Оригинальная модель
[ai-forever/rugpt3small_based_on_gpt2]
* Код генерации вдохновлен этим проектом
[vector2text]
* Заменен эмбеддер
* Добавлена возможность задать промпт
* Вместо нулей вектор дополняется квадратами чисел (далее можно кубами и т.д.)
* Создан класс для генератора
* Добавлен ранжировщик
* Заменена модель вместо large — small
* Убран top_p
* Добавлен расчет среднего эмбеддинга (для ранжировщика в случае работы с массивом)
* Добавлена работа с матрицами эмбеддингов
* Добавлены 2 новых способа смеси эмбеддингов:
* Эмбеддинги в первой степени из одного текста, а квадраты из другого(в эмбеддинги и их квадраты также можно включать разную по структуре информацию)
* Передавать массив эмбеддингов и их квадратов
* Пример использования
---
После дообучения
| [
"# FractalGPT/EmbedderDecoder\n\n* Оригинальная модель\n[ai-forever/rugpt3small_based_on_gpt2]\n\n* Код генерации вдохновлен этим проектом\n[vector2text]\n\n * Заменен эмбеддер\n * Добавлена возможность задать промпт\n * Вместо нулей вектор дополняется квадратами чисел (далее можно кубами и т.д.)\n * Создан класс для генератора\n * Добавлен ранжировщик\n * Заменена модель вместо large — small\n * Убран top_p\n * Добавлен расчет среднего эмбеддинга (для ранжировщика в случае работы с массивом)\n * Добавлена работа с матрицами эмбеддингов\n * Добавлены 2 новых способа смеси эмбеддингов:\n * Эмбеддинги в первой степени из одного текста, а квадраты из другого(в эмбеддинги и их квадраты также можно включать разную по структуре информацию)\n * Передавать массив эмбеддингов и их квадратов \n\n* Пример использования\n\n\n\n\n---\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nПосле дообучения"
] | [
"TAGS\n#transformers #safetensors #gpt2 #text-generation #dataset-wikimedia/wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# FractalGPT/EmbedderDecoder\n\n* Оригинальная модель\n[ai-forever/rugpt3small_based_on_gpt2]\n\n* Код генерации вдохновлен этим проектом\n[vector2text]\n\n * Заменен эмбеддер\n * Добавлена возможность задать промпт\n * Вместо нулей вектор дополняется квадратами чисел (далее можно кубами и т.д.)\n * Создан класс для генератора\n * Добавлен ранжировщик\n * Заменена модель вместо large — small\n * Убран top_p\n * Добавлен расчет среднего эмбеддинга (для ранжировщика в случае работы с массивом)\n * Добавлена работа с матрицами эмбеддингов\n * Добавлены 2 новых способа смеси эмбеддингов:\n * Эмбеддинги в первой степени из одного текста, а квадраты из другого(в эмбеддинги и их квадраты также можно включать разную по структуре информацию)\n * Передавать массив эмбеддингов и их квадратов \n\n* Пример использования\n\n\n\n\n---\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nПосле дообучения"
] | [
64,
231
] | [
"passage: TAGS\n#transformers #safetensors #gpt2 #text-generation #dataset-wikimedia/wikipedia #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# FractalGPT/EmbedderDecoder\n\n* Оригинальная модель\n[ai-forever/rugpt3small_based_on_gpt2]\n\n* Код генерации вдохновлен этим проектом\n[vector2text]\n\n * Заменен эмбеддер\n * Добавлена возможность задать промпт\n * Вместо нулей вектор дополняется квадратами чисел (далее можно кубами и т.д.)\n * Создан класс для генератора\n * Добавлен ранжировщик\n * Заменена модель вместо large — small\n * Убран top_p\n * Добавлен расчет среднего эмбеддинга (для ранжировщика в случае работы с массивом)\n * Добавлена работа с матрицами эмбеддингов\n * Добавлены 2 новых способа смеси эмбеддингов:\n * Эмбеддинги в первой степени из одного текста, а квадраты из другого(в эмбеддинги и их квадраты также можно включать разную по структуре информацию)\n * Передавать массив эмбеддингов и их квадратов \n\n* Пример использования\n\n\n\n\n---\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nПосле дообучения"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ocr8_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2673
- Accuracy: 0.7087
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 52 | 1.5032 | 0.6602 |
| No log | 2.0 | 104 | 1.2673 | 0.7087 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "ocr8_model", "results": []}]} | text-classification | sebastiencormier/ocr8_model | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-13T16:10:35+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ocr8\_model
===========
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2673
* Accuracy: 0.7087
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
72,
98,
4,
30
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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[More Information Needed] | {"license": "apache-2.0", "library_name": "transformers"} | text-generation | yam-peleg/Experiment10-7B | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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## Uses
### Direct Use
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### Out-of-Scope Use
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### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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## Technical Specifications [optional]
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### Compute Infrastructure
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[optional]
BibTeX:
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## Model Card Contact
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# T10
This model is a fine-tuned version of [hhhhzy/deltalm-base-xlsum](https://huggingface.co/hhhhzy/deltalm-base-xlsum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6357
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 64
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.805 | 1.0 | 6 | 0.3684 |
| 0.2843 | 2.0 | 12 | 0.3604 |
| 0.2494 | 3.0 | 18 | 0.3970 |
| 0.1528 | 4.0 | 24 | 0.4507 |
| 0.0779 | 5.0 | 30 | 0.5024 |
| 0.0482 | 6.0 | 36 | 0.5399 |
| 0.0246 | 7.0 | 42 | 0.5612 |
| 0.0202 | 8.0 | 48 | 0.5788 |
| 0.0172 | 9.0 | 54 | 0.6024 |
| 0.0147 | 10.0 | 60 | 0.6003 |
| 0.0115 | 11.0 | 66 | 0.5960 |
| 0.0124 | 12.0 | 72 | 0.6035 |
| 0.0122 | 13.0 | 78 | 0.6135 |
| 0.0121 | 14.0 | 84 | 0.6105 |
| 0.0101 | 15.0 | 90 | 0.6155 |
| 0.0103 | 16.0 | 96 | 0.6188 |
| 0.0087 | 17.0 | 102 | 0.6192 |
| 0.015 | 18.0 | 108 | 0.6113 |
| 0.0092 | 19.0 | 114 | 0.6141 |
| 0.0091 | 20.0 | 120 | 0.6220 |
| 0.0088 | 21.0 | 126 | 0.6243 |
| 0.009 | 22.0 | 132 | 0.6239 |
| 0.0085 | 23.0 | 138 | 0.6199 |
| 0.0093 | 24.0 | 144 | 0.6183 |
| 0.0092 | 25.0 | 150 | 0.6170 |
| 0.0086 | 26.0 | 156 | 0.6154 |
| 0.0084 | 27.0 | 162 | 0.6154 |
| 0.0082 | 28.0 | 168 | 0.6182 |
| 0.0083 | 29.0 | 174 | 0.6224 |
| 0.0082 | 30.0 | 180 | 0.6250 |
| 0.0086 | 31.0 | 186 | 0.6263 |
| 0.0078 | 32.0 | 192 | 0.6270 |
| 0.0081 | 33.0 | 198 | 0.6271 |
| 0.0081 | 34.0 | 204 | 0.6276 |
| 0.0082 | 35.0 | 210 | 0.6280 |
| 0.0078 | 36.0 | 216 | 0.6292 |
| 0.0078 | 37.0 | 222 | 0.6302 |
| 0.0079 | 38.0 | 228 | 0.6314 |
| 0.0081 | 39.0 | 234 | 0.6319 |
| 0.0083 | 40.0 | 240 | 0.6318 |
| 0.0076 | 41.0 | 246 | 0.6317 |
| 0.0079 | 42.0 | 252 | 0.6309 |
| 0.0084 | 43.0 | 258 | 0.6304 |
| 0.0078 | 44.0 | 264 | 0.6307 |
| 0.0079 | 45.0 | 270 | 0.6309 |
| 0.0076 | 46.0 | 276 | 0.6312 |
| 0.0076 | 47.0 | 282 | 0.6313 |
| 0.008 | 48.0 | 288 | 0.6316 |
| 0.0081 | 49.0 | 294 | 0.6320 |
| 0.0077 | 50.0 | 300 | 0.6323 |
| 0.0075 | 51.0 | 306 | 0.6328 |
| 0.0077 | 52.0 | 312 | 0.6336 |
| 0.0076 | 53.0 | 318 | 0.6342 |
| 0.0077 | 54.0 | 324 | 0.6344 |
| 0.0075 | 55.0 | 330 | 0.6346 |
| 0.0079 | 56.0 | 336 | 0.6350 |
| 0.0076 | 57.0 | 342 | 0.6350 |
| 0.0078 | 58.0 | 348 | 0.6355 |
| 0.0077 | 59.0 | 354 | 0.6357 |
| 0.0074 | 60.0 | 360 | 0.6358 |
| 0.0075 | 61.0 | 366 | 0.6358 |
| 0.0075 | 62.0 | 372 | 0.6358 |
| 0.0077 | 63.0 | 378 | 0.6357 |
| 0.0073 | 64.0 | 384 | 0.6357 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "hhhhzy/deltalm-base-xlsum", "model-index": [{"name": "T10", "results": []}]} | text2text-generation | shahadalll/T10 | [
"transformers",
"tensorboard",
"safetensors",
"Deltalm",
"text2text-generation",
"generated_from_trainer",
"base_model:hhhhzy/deltalm-base-xlsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-13T16:11:46+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #Deltalm #text2text-generation #generated_from_trainer #base_model-hhhhzy/deltalm-base-xlsum #autotrain_compatible #endpoints_compatible #region-us
| T10
===
This model is a fine-tuned version of hhhhzy/deltalm-base-xlsum on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6357
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 64
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 64",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #Deltalm #text2text-generation #generated_from_trainer #base_model-hhhhzy/deltalm-base-xlsum #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 64",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
68,
97,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #Deltalm #text2text-generation #generated_from_trainer #base_model-hhhhzy/deltalm-base-xlsum #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 64### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | null |
===== Solstice-11B-v1 =====
A model trained with the sole goal of NSFW.
That is it.
Results are to be as expected.
Finetuned off several instruct datasets that are NSFW. Example subset Below, other instruct datasets modified are private for now. Outputs were manually verified by me and two good friends.
[Lewd-Assistant-v1](https://huggingface.co/datasets/Himitsui/Lewd-Assistant-v1) ---> Used a combination of Claude 2.0, GPT-4-Turbo and WinterGoddess-1.4x to reformat instead of a small 10B model [Fimbulvetr-v1] like the example dataset shown, which resulted in less errors and better answers. Private for now.
Ruled Out names:
<br>Solarslut
<br>Solascivious
<br>Sultry
<br>Sundress
<br>Scorch
***
Prompt Format: Alpaca
There are several issues with the model, but this is an experimental one so :shrug:
<br>----> May speak as {{user}} sometimes. I know what causes it. I kinda like it though that way.
<br>----> May ramble on or give small outputs. Sampler settings.
<br>----> May be a little inconsistent at times. Yeah its inevitable due to the nature of the data.
<br>----> Steers towards NSFW --> As Expected.
***
GGUF: https://huggingface.co/Sao10K/Solstice-11B-v1-GGUF | {"language": ["en"], "license": "cc-by-nc-4.0", "datasets": ["Himitsui/Lewd-Assistant-v1"]} | null | LoneStriker/Solstice-11B-v1-GGUF | [
"gguf",
"en",
"dataset:Himitsui/Lewd-Assistant-v1",
"license:cc-by-nc-4.0",
"region:us"
] | 2024-02-13T16:12:25+00:00 | [] | [
"en"
] | TAGS
#gguf #en #dataset-Himitsui/Lewd-Assistant-v1 #license-cc-by-nc-4.0 #region-us
|
===== Solstice-11B-v1 =====
A model trained with the sole goal of NSFW.
That is it.
Results are to be as expected.
Finetuned off several instruct datasets that are NSFW. Example subset Below, other instruct datasets modified are private for now. Outputs were manually verified by me and two good friends.
Lewd-Assistant-v1 ---> Used a combination of Claude 2.0, GPT-4-Turbo and WinterGoddess-1.4x to reformat instead of a small 10B model [Fimbulvetr-v1] like the example dataset shown, which resulted in less errors and better answers. Private for now.
Ruled Out names:
<br>Solarslut
<br>Solascivious
<br>Sultry
<br>Sundress
<br>Scorch
*
Prompt Format: Alpaca
There are several issues with the model, but this is an experimental one so :shrug:
<br>----> May speak as {{user}} sometimes. I know what causes it. I kinda like it though that way.
<br>----> May ramble on or give small outputs. Sampler settings.
<br>----> May be a little inconsistent at times. Yeah its inevitable due to the nature of the data.
<br>----> Steers towards NSFW --> As Expected.
*
GGUF: URL | [] | [
"TAGS\n#gguf #en #dataset-Himitsui/Lewd-Assistant-v1 #license-cc-by-nc-4.0 #region-us \n"
] | [
40
] | [
"passage: TAGS\n#gguf #en #dataset-Himitsui/Lewd-Assistant-v1 #license-cc-by-nc-4.0 #region-us \n"
] | [
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] |
null | null | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: hugo-massonnat/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | reinforcement-learning | hugo-massonnat/ppo-Pyramids | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | 2024-02-13T16:13:08+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your
browser: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### Watch your Agent play
You can watch your agent playing directly in your browser
1. If the environment is part of ML-Agents official environments, go to URL
2. Step 1: Find your model_id: hugo-massonnat/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: hugo-massonnat/ppo-Pyramids\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: hugo-massonnat/ppo-Pyramids\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
48,
206
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: hugo-massonnat/ppo-Pyramids\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SpeechT5-TTS-Dutch
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4564
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4862 | 2.15 | 1000 | 0.4564 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"language": ["nl"], "license": "mit", "tags": ["text-to-speech", "generated_from_trainer"], "datasets": ["facebook/voxpopuli"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "SpeechT5-TTS-Dutch", "results": []}]} | text-to-speech | arshsin/speecht5_tts_voxpopuli_nl | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"text-to-speech",
"generated_from_trainer",
"nl",
"dataset:facebook/voxpopuli",
"base_model:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-13T16:13:26+00:00 | [] | [
"nl"
] | TAGS
#transformers #tensorboard #safetensors #speecht5 #text-to-audio #text-to-speech #generated_from_trainer #nl #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us
| SpeechT5-TTS-Dutch
==================
This model is a fine-tuned version of microsoft/speecht5\_tts on the VoxPopuli dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4564
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 1000
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.38.0.dev0
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 1000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #text-to-speech #generated_from_trainer #nl #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 1000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
83,
157,
4,
38
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #text-to-speech #generated_from_trainer #nl #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 1000\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | null |
4-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2).
| {"license": "apache-2.0"} | null | numen-tech/Mistral-7B-Instruct-v0.2-w4a16g128asym | [
"arxiv:2308.13137",
"license:apache-2.0",
"region:us"
] | 2024-02-13T16:13:54+00:00 | [
"2308.13137"
] | [] | TAGS
#arxiv-2308.13137 #license-apache-2.0 #region-us
|
4-bit OmniQuant quantized version of Mistral-7B-Instruct-v0.2.
| [] | [
"TAGS\n#arxiv-2308.13137 #license-apache-2.0 #region-us \n"
] | [
22
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null | null | transformers |
# Model Card for Model ID
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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] |
null | null | gguf | GGUF importance matrix (imatrix) quants for https://huggingface.co/WhiteRabbitNeo/WhiteRabbitNeo-33B-v1.5
The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using wiki.train.raw.
| Layers | Context | Template |
| --- | --- | --- |
| <pre>62</pre> | <pre>16384</pre> | <pre>SYSTEM:<br>{instructions}<br>USER:<br>{prompt}<br>ASSISTANT:<br>{response}</pre> | | {"license": "other", "library_name": "gguf", "license_name": "deepseek", "license_link": "https://huggingface.co/deepseek-ai/deepseek-coder-33b-base/blob/main/LICENSE", "pipeline_tag": "text-generation"} | text-generation | dranger003/WhiteRabbitNeo-33B-v1.5-iMat.GGUF | [
"gguf",
"text-generation",
"license:other",
"region:us"
] | 2024-02-13T16:16:10+00:00 | [] | [] | TAGS
#gguf #text-generation #license-other #region-us
| GGUF importance matrix (imatrix) quants for URL
The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using URL.
Layers:
```
62
```
, Context:
```
16384
```
, Template:
```
SYSTEM:
{instructions}
USER:
{prompt}
ASSISTANT:
{response}
```
| [] | [
"TAGS\n#gguf #text-generation #license-other #region-us \n"
] | [
19
] | [
"passage: TAGS\n#gguf #text-generation #license-other #region-us \n"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_Ws_extraction_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2355
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.9570
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 3 | 1.4172 | 0.0526 | 0.0833 | 0.0645 | 0.9083 |
| No log | 2.0 | 6 | 1.2355 | 0.0 | 0.0 | 0.0 | 0.9570 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "my_Ws_extraction_model", "results": []}]} | token-classification | manimaranpa07/my_Ws_extraction_model | [
"transformers",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-13T16:16:58+00:00 | [] | [] | TAGS
#transformers #safetensors #distilbert #token-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| my\_Ws\_extraction\_model
=========================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2355
* Precision: 0.0
* Recall: 0.0
* F1: 0.0
* Accuracy: 0.9570
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0+cu118
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #distilbert #token-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
69,
98,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #distilbert #token-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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null | null | transformers |
# Model Card for Model ID
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"transformers",
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"bert",
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|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Uses
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### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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APA:
## Glossary [optional]
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] |
null | null | transformers |
# Danish-Swedish Merged Model
This is a merge of the following models, all based on `mistralai/Mistral-7B-v0.1`:
1. `danish-foundation-models/munin-7b-alpha`, continued pretraining on Danish data;
2. `timpal0l/Mistral-7B-v0.1-flashback-v2`, continued pretraining on Swedish data.
## Model Details
- **Merged by:** [Dan Saattrup Nielsen](https://www.saattrupdan.com/)
- **Model type:** Decoder model, based on `mistralai/Mistral-7B-v0.1`
- **Language(s):** Danish and Swedish
- **License:** [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)
- **Merge configuration:**
```python
dict(
models=[
dict(
model="danish-foundation-models/munin-7b-alpha",
parameters=dict(
weight=1.0,
density=0.9,
),
),
dict(
model="timpal0l/Mistral-7B-v0.1-flashback-v2",
parameters=dict(
weight=1.0,
density=0.9,
),
),
],
merge_method="dare_ties",
random_seed=4242
base_model="mistralai/Mistral-7B-v0.1",
parameters=dict(
int8_mask=True,
normalize=True,
),
dtype="bfloat16",
)
``` | {"language": ["da", "sv"], "license": "cc-by-4.0", "library_name": "transformers", "tags": ["merge", "mergekit"], "base_model": ["danish-foundation-models/munin-7b-alpha", "timpal0l/Mistral-7B-v0.1-flashback-v2"]} | text-generation | merge-crew/da-sv-dare-ties-density-0.9 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"da",
"sv",
"base_model:danish-foundation-models/munin-7b-alpha",
"base_model:timpal0l/Mistral-7B-v0.1-flashback-v2",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-13T16:17:41+00:00 | [] | [
"da",
"sv"
] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #da #sv #base_model-danish-foundation-models/munin-7b-alpha #base_model-timpal0l/Mistral-7B-v0.1-flashback-v2 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Danish-Swedish Merged Model
This is a merge of the following models, all based on 'mistralai/Mistral-7B-v0.1':
1. 'danish-foundation-models/munin-7b-alpha', continued pretraining on Danish data;
2. 'timpal0l/Mistral-7B-v0.1-flashback-v2', continued pretraining on Swedish data.
## Model Details
- Merged by: Dan Saattrup Nielsen
- Model type: Decoder model, based on 'mistralai/Mistral-7B-v0.1'
- Language(s): Danish and Swedish
- License: CC-BY-4.0
- Merge configuration:
| [
"# Danish-Swedish Merged Model\n\nThis is a merge of the following models, all based on 'mistralai/Mistral-7B-v0.1':\n\n1. 'danish-foundation-models/munin-7b-alpha', continued pretraining on Danish data;\n2. 'timpal0l/Mistral-7B-v0.1-flashback-v2', continued pretraining on Swedish data.",
"## Model Details\n\n- Merged by: Dan Saattrup Nielsen\n- Model type: Decoder model, based on 'mistralai/Mistral-7B-v0.1'\n- Language(s): Danish and Swedish\n- License: CC-BY-4.0\n- Merge configuration:"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #da #sv #base_model-danish-foundation-models/munin-7b-alpha #base_model-timpal0l/Mistral-7B-v0.1-flashback-v2 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Danish-Swedish Merged Model\n\nThis is a merge of the following models, all based on 'mistralai/Mistral-7B-v0.1':\n\n1. 'danish-foundation-models/munin-7b-alpha', continued pretraining on Danish data;\n2. 'timpal0l/Mistral-7B-v0.1-flashback-v2', continued pretraining on Swedish data.",
"## Model Details\n\n- Merged by: Dan Saattrup Nielsen\n- Model type: Decoder model, based on 'mistralai/Mistral-7B-v0.1'\n- Language(s): Danish and Swedish\n- License: CC-BY-4.0\n- Merge configuration:"
] | [
112,
92,
57
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #da #sv #base_model-danish-foundation-models/munin-7b-alpha #base_model-timpal0l/Mistral-7B-v0.1-flashback-v2 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Danish-Swedish Merged Model\n\nThis is a merge of the following models, all based on 'mistralai/Mistral-7B-v0.1':\n\n1. 'danish-foundation-models/munin-7b-alpha', continued pretraining on Danish data;\n2. 'timpal0l/Mistral-7B-v0.1-flashback-v2', continued pretraining on Swedish data.## Model Details\n\n- Merged by: Dan Saattrup Nielsen\n- Model type: Decoder model, based on 'mistralai/Mistral-7B-v0.1'\n- Language(s): Danish and Swedish\n- License: CC-BY-4.0\n- Merge configuration:"
] | [
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