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# **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": "3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "6.47 +/- 10.92", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | JiajingChen/3 | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2024-02-07T21:03:29+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 | stable-baselines3 |
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.22 +/- 0.14", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | JiajingChen/4 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-07T21:03:32+00:00 | [] | [] | TAGS
#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing PandaReachDense-v3
This is a trained model of a A2C agent playing PandaReachDense-v3
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
41,
45,
17
] | [
"passage: TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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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": "5", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "405.90 +/- 47.27", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | JiajingChen/5 | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2024-02-07T21:03:35+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 | 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: JiajingChen/6
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 | JiajingChen/6 | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | 2024-02-07T21:03:39+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: JiajingChen/6
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: JiajingChen/6\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: JiajingChen/6\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
50,
201
] | [
"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: JiajingChen/6\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. -->
# wav2vec_RTSplit0208_8
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0162
- Wer: 0.1956
- Cer: 0.1557
## 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: 5.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 11
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 3.438 | 1.0 | 120 | 3.2713 | 0.9922 | 0.9976 |
| 1.4577 | 2.0 | 240 | 1.2657 | 1.0 | 0.7431 |
| 0.9504 | 3.0 | 360 | 0.7310 | 0.7740 | 0.4578 |
| 0.6237 | 4.0 | 480 | 0.4084 | 0.5590 | 0.3053 |
| 0.488 | 5.0 | 600 | 0.2735 | 0.4714 | 0.2328 |
| 0.3652 | 6.0 | 720 | 0.1508 | 0.3326 | 0.2069 |
| 0.264 | 7.0 | 840 | 0.0773 | 0.2506 | 0.1641 |
| 0.2181 | 8.0 | 960 | 0.0446 | 0.2223 | 0.1574 |
| 0.1697 | 9.0 | 1080 | 0.0253 | 0.2047 | 0.1651 |
| 0.1068 | 10.0 | 1200 | 0.0223 | 0.2001 | 0.1578 |
| 0.1157 | 11.0 | 1320 | 0.0162 | 0.1956 | 0.1557 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "model-index": [{"name": "wav2vec_RTSplit0208_8", "results": []}]} | automatic-speech-recognition | tndklab/wav2vec_RTSplit0208_8 | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-07T21:04:21+00:00 | [] | [] | TAGS
#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec\_RTSplit0208\_8
=======================
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-japanese on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0162
* Wer: 0.1956
* Cer: 0.1557
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: 5.5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 11
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 11",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #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: 5.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 11",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
80,
116,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #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: 5.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 11### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\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/mistral7btimebookFinetune50rows | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T21:04:28+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #autotrain #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 #mistral #text-generation #autotrain #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"
] | [
56,
29,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #autotrain #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 | 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. -->
# whisper-tiny-ft-verbatim-clean-cy
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7654
- Wer: 54.1118
## 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: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.959 | 1.41 | 1000 | 0.9705 | 66.1843 |
| 0.7164 | 2.83 | 2000 | 0.8185 | 59.5372 |
| 0.5856 | 4.24 | 3000 | 0.7779 | 55.2935 |
| 0.5403 | 5.66 | 4000 | 0.7654 | 54.1118 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-tiny", "model-index": [{"name": "whisper-tiny-ft-verbatim-clean-cy", "results": []}]} | automatic-speech-recognition | DewiBrynJones/whisper-tiny-ft-verbatim-clean-cy | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-07T21:09:10+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-openai/whisper-tiny #license-apache-2.0 #endpoints_compatible #region-us
| whisper-tiny-ft-verbatim-clean-cy
=================================
This model is a fine-tuned version of openai/whisper-tiny on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7654
* Wer: 54.1118
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: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* 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: 500
* training\_steps: 4000
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\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: 500\n* training\\_steps: 4000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
68,
143,
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33
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"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-openai/whisper-tiny #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: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\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: 500\n* training\\_steps: 4000### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | hanspeterlyngsoeraaschoujensen/deepseek-math-7b-instruct-GPTQ | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2024-02-07T21:16:32+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #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 | 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. -->
# Llama2-7b-finetuned-alpaca
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- 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: 2
- training_steps: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-hf", "model-index": [{"name": "Llama2-7b-finetuned-alpaca", "results": []}]} | null | Utshav/Llama2-7b-finetuned-alpaca | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"region:us"
] | 2024-02-07T21:16:41+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-meta-llama/Llama-2-7b-hf #region-us
|
# Llama2-7b-finetuned-alpaca
This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on an unknown 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- 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: 2
- training_steps: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | [
"# Llama2-7b-finetuned-alpaca\n\nThis model is a fine-tuned version of meta-llama/Llama-2-7b-hf on an unknown 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: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 16\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: 2\n- training_steps: 10\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-meta-llama/Llama-2-7b-hf #region-us \n",
"# Llama2-7b-finetuned-alpaca\n\nThis model is a fine-tuned version of meta-llama/Llama-2-7b-hf on an unknown 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: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 16\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: 2\n- training_steps: 10\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
43,
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139,
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"passage: TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-meta-llama/Llama-2-7b-hf #region-us \n# Llama2-7b-finetuned-alpaca\n\nThis model is a fine-tuned version of meta-llama/Llama-2-7b-hf on an unknown 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: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 16\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: 2\n- training_steps: 10\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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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 atmikah -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 atmikah -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 atmikah
```
## 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', 0.0001),
('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": "285.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | atmikah/dqn-SpaceInvadersNoFrameskip-v4 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-07T21:18:26+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 |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | davisalex22/GPT2-TurismEC-xl-ft | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
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|
# Model Card for Model ID
## Model Details
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- Developed by:
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## 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
### Training Data
### Training Procedure
#### Preprocessing [optional]
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- 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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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. -->
# st_vit_trained-8epoch-ucf101-subset
This model is a fine-tuned version of [Tommidi/st_vit_untrained](https://huggingface.co/Tommidi/st_vit_untrained) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0648
- Accuracy: 0.9733
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 296
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6314 | 0.13 | 38 | 0.1264 | 0.9333 |
| 0.3547 | 1.13 | 76 | 0.0077 | 1.0 |
| 0.0189 | 2.13 | 114 | 0.5103 | 0.9333 |
| 0.0611 | 3.13 | 152 | 0.1508 | 0.9333 |
| 0.0027 | 4.13 | 190 | 0.0018 | 1.0 |
| 0.0812 | 5.13 | 228 | 0.0943 | 0.9333 |
| 0.0005 | 6.13 | 266 | 0.0635 | 0.9667 |
| 0.3035 | 7.1 | 296 | 0.0530 | 0.9667 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "Tommidi/st_vit_untrained", "model-index": [{"name": "st_vit_trained-8epoch-ucf101-subset", "results": []}]} | null | Tommidi/st_vit_trained-8epoch-ucf101-subset | [
"transformers",
"tensorboard",
"safetensors",
"st_vit",
"generated_from_trainer",
"base_model:Tommidi/st_vit_untrained",
"endpoints_compatible",
"region:us"
] | 2024-02-07T21:31:16+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #st_vit #generated_from_trainer #base_model-Tommidi/st_vit_untrained #endpoints_compatible #region-us
| st\_vit\_trained-8epoch-ucf101-subset
=====================================
This model is a fine-tuned version of Tommidi/st\_vit\_untrained on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0648
* Accuracy: 0.9733
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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 296
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 296",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #st_vit #generated_from_trainer #base_model-Tommidi/st_vit_untrained #endpoints_compatible #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: 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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 296",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
52,
116,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #st_vit #generated_from_trainer #base_model-Tommidi/st_vit_untrained #endpoints_compatible #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: 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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 296### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | null | test model 1 | {"license": "unknown"} | null | Colbot/testmodel1 | [
"license:unknown",
"region:us"
] | 2024-02-07T21:37:37+00:00 | [] | [] | TAGS
#license-unknown #region-us
| test model 1 | [] | [
"TAGS\n#license-unknown #region-us \n"
] | [
13
] | [
"passage: TAGS\n#license-unknown #region-us \n"
] | [
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null | null | diffusers |
# Introduction
<a href="https://colab.research.google.com/drive/1VcqzXZmilntec3AsIyzCqlstEhX4Pa1o?usp=sharing" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
The λ-ECLIPSE model is a light weight support for multi-concept personalization. λ-ECLIPSE is tiny T2I prior model designed for Kandinsky v2.2 diffusion image generator.
λ-ECLIPSE model extends the [ECLIPSE-Prior](https://huggingface.co/ECLIPSE-Community/ECLIPSE_KandinskyV22_Prior) via incorporating the image-text interleaved data.
λ-ECLIPSE shows that we do not need to train the Personalized T2I (P-T2I) models on lot of resources. For instance, λ-ECLIPSE is trained on mere 74 GPU Hours (A100) compared to it's couterparts BLIP-Diffusion (2304 GPU hours) and Kosmos-G (12300 GPU hours).
- **Project Page:** [https://eclipse-t2i.github.io/Lambda-ECLIPSE/](https://eclipse-t2i.github.io/Lambda-ECLIPSE/)
- **GitHub:** [https://github.com/Maitreyapatel/lambda-eclipse-inference](https://github.com/Maitreyapatel/lambda-eclipse-inference)
- **Paper (arXiv):** [https://arxiv.org/abs/2402.05195](https://arxiv.org/abs/2402.05195)
Importantly, λ-ECLIPSE works in pure CLIP latent space without any additional information. Hence, it's performance can be easily imporved via test-time adaption to increase the concept alignment while having solid composition alignment.
More examples at: [Gallery](https://eclipse-t2i.github.io/Lambda-ECLIPSE/gallery.html)
## Installation
```bash
git clone https://github.com/eclipse-t2i/lambda-eclipse-inference.git
conda create -p ./venv python=3.9
pip install -r requirements.txt
```
## Run Inference
<a href="https://colab.research.google.com/drive/1VcqzXZmilntec3AsIyzCqlstEhX4Pa1o?usp=sharing" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```bash
import os
import torch
from transformers import (
CLIPTextModelWithProjection,
CLIPTokenizer,
)
from src.pipelines.pipeline_kandinsky_subject_prior import KandinskyPriorPipeline
from src.priors.lambda_prior_transformer import PriorTransformer
from diffusers import DiffusionPipeline
text_encoder = CLIPTextModelWithProjection.from_pretrained(
"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k",
projection_dim=1280,
torch_dtype=torch.float32,
)
tokenizer = CLIPTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k")
prior = PriorTransformer.from_pretrained("ECLIPSE-Community/Lambda-ECLIPSE-Prior-v1.0")
pipe_prior = KandinskyPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior",
prior=prior,
text_encoder=text_encoder,
tokenizer=tokenizer,
).to("cuda")
pipe = DiffusionPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-decoder"
).to("cuda")
raw_data = {
"prompt": args.prompt,
"subject_images": [args.subject1_path, args.subject2_path],
"subject_keywords": [args.subject1_name, args.subject2_name]
}
image_emb, negative_image_emb = pipe_prior(
raw_data=raw_data,
).to_tuple()
image = pipe(
image_embeds=image_emb,
negative_image_embeds=negative_image_emb,
num_inference_steps=50,
guidance_scale=7.5,
).images
image[0]
```
## Important Notes (and limitations):
- λ-ECLIPSE is trained to support upto four unique concepts, however, this version is trained on biased datasets heavily focusing on single and two subjects. Therefore, it maynot perform expectadly as number of subjects increases.
- As this model is trained for P-T2I specifically, it might not perform well on traditional T2I task.
- λ-ECLIPSE achieves SOTA compositional performance on composition alignment while maintaining the concept alignment. However, there is still a big gap compared to the finetuning based methodologies. | {"language": ["en"], "license": "apache-2.0", "library_name": "diffusers", "tags": ["text-to-image", "prior", "eclipse", "unclip", "kandinskyv2.2"]} | text-to-image | ECLIPSE-Community/Lambda-ECLIPSE-Prior-v1.0 | [
"diffusers",
"safetensors",
"text-to-image",
"prior",
"eclipse",
"unclip",
"kandinskyv2.2",
"en",
"arxiv:2402.05195",
"license:apache-2.0",
"has_space",
"diffusers:PriorTransformer",
"region:us"
] | 2024-02-07T21:38:33+00:00 | [
"2402.05195"
] | [
"en"
] | TAGS
#diffusers #safetensors #text-to-image #prior #eclipse #unclip #kandinskyv2.2 #en #arxiv-2402.05195 #license-apache-2.0 #has_space #diffusers-PriorTransformer #region-us
|
# Introduction
<a href="URL target="_parent"><img src="URL alt="Open In Colab"/></a>
The λ-ECLIPSE model is a light weight support for multi-concept personalization. λ-ECLIPSE is tiny T2I prior model designed for Kandinsky v2.2 diffusion image generator.
λ-ECLIPSE model extends the ECLIPSE-Prior via incorporating the image-text interleaved data.
λ-ECLIPSE shows that we do not need to train the Personalized T2I (P-T2I) models on lot of resources. For instance, λ-ECLIPSE is trained on mere 74 GPU Hours (A100) compared to it's couterparts BLIP-Diffusion (2304 GPU hours) and Kosmos-G (12300 GPU hours).
- Project Page: URL
- GitHub: URL
- Paper (arXiv): URL
Importantly, λ-ECLIPSE works in pure CLIP latent space without any additional information. Hence, it's performance can be easily imporved via test-time adaption to increase the concept alignment while having solid composition alignment.
!Qualitative example
More examples at: Gallery
## Installation
## Run Inference
<a href="URL target="_parent"><img src="URL alt="Open In Colab"/></a>
## Important Notes (and limitations):
- λ-ECLIPSE is trained to support upto four unique concepts, however, this version is trained on biased datasets heavily focusing on single and two subjects. Therefore, it maynot perform expectadly as number of subjects increases.
- As this model is trained for P-T2I specifically, it might not perform well on traditional T2I task.
- λ-ECLIPSE achieves SOTA compositional performance on composition alignment while maintaining the concept alignment. However, there is still a big gap compared to the finetuning based methodologies. | [
"# Introduction\n<a href=\"URL target=\"_parent\"><img src=\"URL alt=\"Open In Colab\"/></a>\n\nThe λ-ECLIPSE model is a light weight support for multi-concept personalization. λ-ECLIPSE is tiny T2I prior model designed for Kandinsky v2.2 diffusion image generator.\n\nλ-ECLIPSE model extends the ECLIPSE-Prior via incorporating the image-text interleaved data.\n\nλ-ECLIPSE shows that we do not need to train the Personalized T2I (P-T2I) models on lot of resources. For instance, λ-ECLIPSE is trained on mere 74 GPU Hours (A100) compared to it's couterparts BLIP-Diffusion (2304 GPU hours) and Kosmos-G (12300 GPU hours).\n\n- Project Page: URL\n- GitHub: URL\n- Paper (arXiv): URL\n\nImportantly, λ-ECLIPSE works in pure CLIP latent space without any additional information. Hence, it's performance can be easily imporved via test-time adaption to increase the concept alignment while having solid composition alignment.\n\n\n!Qualitative example\n\nMore examples at: Gallery",
"## Installation",
"## Run Inference\n<a href=\"URL target=\"_parent\"><img src=\"URL alt=\"Open In Colab\"/></a>",
"## Important Notes (and limitations):\n\n- λ-ECLIPSE is trained to support upto four unique concepts, however, this version is trained on biased datasets heavily focusing on single and two subjects. Therefore, it maynot perform expectadly as number of subjects increases. \n- As this model is trained for P-T2I specifically, it might not perform well on traditional T2I task.\n- λ-ECLIPSE achieves SOTA compositional performance on composition alignment while maintaining the concept alignment. However, there is still a big gap compared to the finetuning based methodologies."
] | [
"TAGS\n#diffusers #safetensors #text-to-image #prior #eclipse #unclip #kandinskyv2.2 #en #arxiv-2402.05195 #license-apache-2.0 #has_space #diffusers-PriorTransformer #region-us \n",
"# Introduction\n<a href=\"URL target=\"_parent\"><img src=\"URL alt=\"Open In Colab\"/></a>\n\nThe λ-ECLIPSE model is a light weight support for multi-concept personalization. λ-ECLIPSE is tiny T2I prior model designed for Kandinsky v2.2 diffusion image generator.\n\nλ-ECLIPSE model extends the ECLIPSE-Prior via incorporating the image-text interleaved data.\n\nλ-ECLIPSE shows that we do not need to train the Personalized T2I (P-T2I) models on lot of resources. For instance, λ-ECLIPSE is trained on mere 74 GPU Hours (A100) compared to it's couterparts BLIP-Diffusion (2304 GPU hours) and Kosmos-G (12300 GPU hours).\n\n- Project Page: URL\n- GitHub: URL\n- Paper (arXiv): URL\n\nImportantly, λ-ECLIPSE works in pure CLIP latent space without any additional information. Hence, it's performance can be easily imporved via test-time adaption to increase the concept alignment while having solid composition alignment.\n\n\n!Qualitative example\n\nMore examples at: Gallery",
"## Installation",
"## Run Inference\n<a href=\"URL target=\"_parent\"><img src=\"URL alt=\"Open In Colab\"/></a>",
"## Important Notes (and limitations):\n\n- λ-ECLIPSE is trained to support upto four unique concepts, however, this version is trained on biased datasets heavily focusing on single and two subjects. Therefore, it maynot perform expectadly as number of subjects increases. \n- As this model is trained for P-T2I specifically, it might not perform well on traditional T2I task.\n- λ-ECLIPSE achieves SOTA compositional performance on composition alignment while maintaining the concept alignment. However, there is still a big gap compared to the finetuning based methodologies."
] | [
68,
295,
2,
34,
150
] | [
"passage: TAGS\n#diffusers #safetensors #text-to-image #prior #eclipse #unclip #kandinskyv2.2 #en #arxiv-2402.05195 #license-apache-2.0 #has_space #diffusers-PriorTransformer #region-us \n# Introduction\n<a href=\"URL target=\"_parent\"><img src=\"URL alt=\"Open In Colab\"/></a>\n\nThe λ-ECLIPSE model is a light weight support for multi-concept personalization. λ-ECLIPSE is tiny T2I prior model designed for Kandinsky v2.2 diffusion image generator.\n\nλ-ECLIPSE model extends the ECLIPSE-Prior via incorporating the image-text interleaved data.\n\nλ-ECLIPSE shows that we do not need to train the Personalized T2I (P-T2I) models on lot of resources. For instance, λ-ECLIPSE is trained on mere 74 GPU Hours (A100) compared to it's couterparts BLIP-Diffusion (2304 GPU hours) and Kosmos-G (12300 GPU hours).\n\n- Project Page: URL\n- GitHub: URL\n- Paper (arXiv): URL\n\nImportantly, λ-ECLIPSE works in pure CLIP latent space without any additional information. Hence, it's performance can be easily imporved via test-time adaption to increase the concept alignment while having solid composition alignment.\n\n\n!Qualitative example\n\nMore examples at: Gallery## Installation## Run Inference\n<a href=\"URL target=\"_parent\"><img src=\"URL alt=\"Open In Colab\"/></a>"
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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. -->
# bertin-gpt-j-6B_8bit_13
This model is a fine-tuned version of [bertin-project/bertin-gpt-j-6B](https://huggingface.co/bertin-project/bertin-gpt-j-6B) on an unknown 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: 1.41e-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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "bertin-project/bertin-gpt-j-6B", "model-index": [{"name": "bertin-gpt-j-6B_8bit_13", "results": []}]} | null | versae/bertin-gpt-j-6B_8bit_13 | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:bertin-project/bertin-gpt-j-6B",
"license:apache-2.0",
"region:us"
] | 2024-02-07T21:45:35+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-bertin-project/bertin-gpt-j-6B #license-apache-2.0 #region-us
|
# bertin-gpt-j-6B_8bit_13
This model is a fine-tuned version of bertin-project/bertin-gpt-j-6B on an unknown 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: 1.41e-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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.1 | [
"# bertin-gpt-j-6B_8bit_13\n\nThis model is a fine-tuned version of bertin-project/bertin-gpt-j-6B on an unknown 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: 1.41e-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\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-bertin-project/bertin-gpt-j-6B #license-apache-2.0 #region-us \n",
"# bertin-gpt-j-6B_8bit_13\n\nThis model is a fine-tuned version of bertin-project/bertin-gpt-j-6B on an unknown 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: 1.41e-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\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.1"
] | [
52,
47,
6,
12,
8,
3,
104,
4,
39
] | [
"passage: TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-bertin-project/bertin-gpt-j-6B #license-apache-2.0 #region-us \n# bertin-gpt-j-6B_8bit_13\n\nThis model is a fine-tuned version of bertin-project/bertin-gpt-j-6B on an unknown 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: 1.41e-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\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.1"
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] |
null | null | null | git lfs install
git clone https://huggingface.co/spaces/tonyassi/text-to-image-SDXL | {} | null | Jcarmody93/Uhd | [
"region:us"
] | 2024-02-07T21:50:30+00:00 | [] | [] | TAGS
#region-us
| git lfs install
git clone URL | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] | [
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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-CartPole-v1", "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 | ORromu/Reinforce-CartPole-v1 | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2024-02-07T22:01:34+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 |
<center><img src='https://i.imgur.com/0xFTuAX.png' width='450px'></center>
# Pearl-3x7B, an xtraordinary Mixture of Experts (MoE) for data science
Pearl-3x7B is a Mixture of Experts (MoE) made with the following models :
* [dvilasuero/DistilabelBeagle14-7B](https://huggingface.co/dvilasuero/DistilabelBeagle14-7B)
* [beowolx/CodeNinja-1.0-OpenChat-7B](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B)
* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
A Mixture of Experts (MoE) model represents a sophisticated architecture that amalgamates the capabilities of multiple specialized models to address a wide array of tasks within a unified framework. Within the realm of a MoE model tailored for a chat application, the integration of expertise spanning three distinct domains - chat, code, and mathematics - substantially enhances its capacity to furnish nuanced and precise responses to a diverse spectrum of user inquiries.
The initial expert model, honed for chat applications, exhibits prowess in comprehending natural language nuances, conversational dynamics, and contextual cues. Drawing upon extensive conversational data, it adeptly generates engaging and contextually pertinent responses, thereby fostering meaningful interactions with users.
The subsequent expert model, centered on code, brings to the fore proficiency in programming languages, algorithms, and software engineering principles. Possessing a deep-seated understanding of syntax, logical constructs, and problem-solving methodologies, it deftly tackles queries spanning coding challenges, debugging assistance, and software development inquiries.
Lastly, the third expert model, specializing in mathematics, boasts expertise in mathematical reasoning, problem-solving strategies, and analytical techniques. Armed with a breadth of knowledge encompassing arithmetic, algebra, calculus, and beyond, it offers precise solutions, lucid explanations, and profound insights for mathematical queries, equations, and proofs.
## Configuration
```yaml
base_model: argilla/CapybaraHermes-2.5-Mistral-7B
experts:
- source_model: dvilasuero/DistilabelBeagle14-7B
positive_prompts:
- "chat"
- "assistant"
- "tell me"
- "explain"
- "help"
- "guide"
- "assist"
- "answer"
- "support"
- "clarify"
- "elaborate"
- "educate"
- "inform"
- "advise"
- "instruct"
- source_model: beowolx/CodeNinja-1.0-OpenChat-7B
positive_prompts:
- "code"
- "python"
- "javascript"
- "programming"
- "algorithm"
- "develop"
- "debug"
- "optimize"
- "software"
- "engineer"
- "web"
- "application"
- "framework"
- "library"
- "syntax"
- "logic"
- "compile"
- "execute"
- source_model: WizardLM/WizardMath-7B-V1.1
positive_prompts:
- "reason"
- "math"
- "mathematics"
- "solve"
- "count"
- "calculate"
- "analyze"
- "derive"
- "compute"
- "numbers"
- "equation"
- "theorem"
- "proof"
- "geometry"
- "trigonometry"
- "statistics"
- "probability"
- "algebra"
- "integral"
```
## Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "louisbrulenaudet/Pearl-3x7B"
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"])
```
## Citing & Authors
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Pearl-3x7B, an xtraordinary Mixture of Experts (MoE) for data science},
year = {2023}
howpublished = {\url{https://huggingface.co/louisbrulenaudet/Pearl-3x7B}},
}
```
## Feedback
If you have any feedback, please reach out at [[email protected]](mailto:[email protected]). | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "dvilasuero/DistilabelBeagle14-7B", "beowolx/CodeNinja-1.0-OpenChat-7B", "WizardLM/WizardMath-7B-V1.1", "Maths", "Code", "Python"], "base_model": ["dvilasuero/DistilabelBeagle14-7B", "beowolx/CodeNinja-1.0-OpenChat-7B", "WizardLM/WizardMath-7B-V1.1"], "pipeline_tag": "text-generation"} | text-generation | louisbrulenaudet/Pearl-3x7B | [
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"license:apache-2.0",
"autotrain_compatible",
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"region:us"
] | 2024-02-07T22:02:37+00:00 | [] | [
"en"
] | TAGS
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|
<center><img src='https://i.URL width='450px'></center>
# Pearl-3x7B, an xtraordinary Mixture of Experts (MoE) for data science
Pearl-3x7B is a Mixture of Experts (MoE) made with the following models :
* dvilasuero/DistilabelBeagle14-7B
* beowolx/CodeNinja-1.0-OpenChat-7B
* WizardLM/WizardMath-7B-V1.1
A Mixture of Experts (MoE) model represents a sophisticated architecture that amalgamates the capabilities of multiple specialized models to address a wide array of tasks within a unified framework. Within the realm of a MoE model tailored for a chat application, the integration of expertise spanning three distinct domains - chat, code, and mathematics - substantially enhances its capacity to furnish nuanced and precise responses to a diverse spectrum of user inquiries.
The initial expert model, honed for chat applications, exhibits prowess in comprehending natural language nuances, conversational dynamics, and contextual cues. Drawing upon extensive conversational data, it adeptly generates engaging and contextually pertinent responses, thereby fostering meaningful interactions with users.
The subsequent expert model, centered on code, brings to the fore proficiency in programming languages, algorithms, and software engineering principles. Possessing a deep-seated understanding of syntax, logical constructs, and problem-solving methodologies, it deftly tackles queries spanning coding challenges, debugging assistance, and software development inquiries.
Lastly, the third expert model, specializing in mathematics, boasts expertise in mathematical reasoning, problem-solving strategies, and analytical techniques. Armed with a breadth of knowledge encompassing arithmetic, algebra, calculus, and beyond, it offers precise solutions, lucid explanations, and profound insights for mathematical queries, equations, and proofs.
## Configuration
## Usage
## Citing & Authors
If you use this code in your research, please use the following BibTeX entry.
## Feedback
If you have any feedback, please reach out at louisbrulenaudet@URL. | [
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"## Configuration",
"## Usage",
"## Citing & Authors\n\nIf you use this code in your research, please use the following BibTeX entry.",
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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. -->
# hubert_1
This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2381
- Wer: 0.4180
- Cer: 0.1482
## 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: 0.00015
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 15.253 | 1.0 | 60 | 13.7834 | 0.9664 | 0.9898 |
| 7.6775 | 2.0 | 120 | 6.7622 | 0.9664 | 0.9898 |
| 5.871 | 3.0 | 180 | 5.6071 | 0.9664 | 0.9898 |
| 4.8058 | 4.0 | 240 | 4.5471 | 0.9664 | 0.9898 |
| 3.946 | 5.0 | 300 | 3.6487 | 0.9664 | 0.9898 |
| 3.1431 | 6.0 | 360 | 3.1120 | 0.9664 | 0.9898 |
| 2.8039 | 7.0 | 420 | 2.6477 | 0.9664 | 0.9898 |
| 2.0778 | 8.0 | 480 | 1.9071 | 1.0 | 0.8367 |
| 1.509 | 9.0 | 540 | 1.3678 | 1.0 | 0.5685 |
| 1.1834 | 10.0 | 600 | 1.1179 | 1.0 | 0.5775 |
| 1.015 | 11.0 | 660 | 0.8911 | 0.9265 | 0.5082 |
| 0.8326 | 12.0 | 720 | 0.7762 | 0.8106 | 0.4560 |
| 0.8002 | 13.0 | 780 | 0.7251 | 0.8080 | 0.4705 |
| 0.7281 | 14.0 | 840 | 0.7420 | 0.8110 | 0.4872 |
| 0.6855 | 15.0 | 900 | 0.6547 | 0.8016 | 0.4762 |
| 0.6512 | 16.0 | 960 | 0.6274 | 0.8069 | 0.4871 |
| 0.6432 | 17.0 | 1020 | 0.6026 | 0.7722 | 0.4108 |
| 0.93 | 18.0 | 1080 | 0.5734 | 0.7021 | 0.3093 |
| 0.5854 | 19.0 | 1140 | 0.5499 | 0.7614 | 0.3819 |
| 0.5804 | 20.0 | 1200 | 0.5263 | 0.6920 | 0.3123 |
| 0.5085 | 21.0 | 1260 | 0.4617 | 0.6603 | 0.2739 |
| 0.5421 | 22.0 | 1320 | 0.4104 | 0.6245 | 0.2610 |
| 0.4387 | 23.0 | 1380 | 0.3837 | 0.5887 | 0.2024 |
| 0.4018 | 24.0 | 1440 | 0.3480 | 0.5477 | 0.1898 |
| 0.3639 | 25.0 | 1500 | 0.3120 | 0.5086 | 0.1828 |
| 0.3328 | 26.0 | 1560 | 0.2881 | 0.4720 | 0.1563 |
| 0.3258 | 27.0 | 1620 | 0.2636 | 0.4452 | 0.1483 |
| 0.3226 | 28.0 | 1680 | 0.2518 | 0.4403 | 0.1531 |
| 0.2743 | 29.0 | 1740 | 0.2431 | 0.4284 | 0.1532 |
| 0.2805 | 30.0 | 1800 | 0.2381 | 0.4180 | 0.1482 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "rinna/japanese-hubert-base", "model-index": [{"name": "hubert_1", "results": []}]} | automatic-speech-recognition | tndklab/hubert_1 | [
"transformers",
"safetensors",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:rinna/japanese-hubert-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-07T22:03:19+00:00 | [] | [] | TAGS
#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-base #license-apache-2.0 #endpoints_compatible #region-us
| hubert\_1
=========
This model is a fine-tuned version of rinna/japanese-hubert-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2381
* Wer: 0.4180
* Cer: 0.1482
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: 0.00015
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00015\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-base #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: 0.00015\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
66,
116,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-base #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: 0.00015\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
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null | null | transformers |
# NeuraLake-m7-v2-7B⚡
NeuraLake-m7-v2-7B is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B)
* [chargoddard/loyal-piano-m7](https://huggingface.co/chargoddard/loyal-piano-m7)
* [macadeliccc/WestLake-7B-v2-laser-truthy-dpo](https://huggingface.co/macadeliccc/WestLake-7B-v2-laser-truthy-dpo)
* [athirdpath/NSFW_DPO_vmgb-7b](https://huggingface.co/athirdpath/NSFW_DPO_vmgb-7b)
## 🛠️ Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
# No parameters necessary for base model
- model: mlabonne/NeuralBeagle14-7B
parameters:
weight: 0.3
density: 0.8
- model: chargoddard/loyal-piano-m7
parameters:
weight: 0.4
density: 0.8
- model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
parameters:
weight: 0.3
density: 0.4
- model: athirdpath/NSFW_DPO_vmgb-7b
parameters:
weight: 0.2
density: 0.4
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
# normalize: true
dtype: bfloat16
``` | {"license": "cc-by-nc-4.0", "tags": ["merge", "mergekit"]} | text-generation | Meggido/NeuraLake-m7-v2-7B | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T22:05:59+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# NeuraLake-m7-v2-7B
NeuraLake-m7-v2-7B is a merge of the following models using mergekit:
* mlabonne/NeuralBeagle14-7B
* chargoddard/loyal-piano-m7
* macadeliccc/WestLake-7B-v2-laser-truthy-dpo
* athirdpath/NSFW_DPO_vmgb-7b
## ️ Configuration
| [
"# NeuraLake-m7-v2-7B\n\nNeuraLake-m7-v2-7B is a merge of the following models using mergekit:\n* mlabonne/NeuralBeagle14-7B\n* chargoddard/loyal-piano-m7\n* macadeliccc/WestLake-7B-v2-laser-truthy-dpo\n* athirdpath/NSFW_DPO_vmgb-7b",
"## ️ Configuration"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# NeuraLake-m7-v2-7B\n\nNeuraLake-m7-v2-7B is a merge of the following models using mergekit:\n* mlabonne/NeuralBeagle14-7B\n* chargoddard/loyal-piano-m7\n* macadeliccc/WestLake-7B-v2-laser-truthy-dpo\n* athirdpath/NSFW_DPO_vmgb-7b",
"## ️ Configuration"
] | [
65,
100,
6
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# NeuraLake-m7-v2-7B\n\nNeuraLake-m7-v2-7B is a merge of the following models using mergekit:\n* mlabonne/NeuralBeagle14-7B\n* chargoddard/loyal-piano-m7\n* macadeliccc/WestLake-7B-v2-laser-truthy-dpo\n* athirdpath/NSFW_DPO_vmgb-7b## ️ Configuration"
] | [
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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. -->
# multiple_choice_cowese_beto_2
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) 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: 1.5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "dccuchile/bert-base-spanish-wwm-cased", "model-index": [{"name": "multiple_choice_cowese_beto_2", "results": []}]} | multiple-choice | tomashs/multiple_choice_cowese_beto_2 | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"multiple-choice",
"generated_from_trainer",
"base_model:dccuchile/bert-base-spanish-wwm-cased",
"endpoints_compatible",
"region:us"
] | 2024-02-07T22:08:04+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #multiple-choice #generated_from_trainer #base_model-dccuchile/bert-base-spanish-wwm-cased #endpoints_compatible #region-us
|
# multiple_choice_cowese_beto_2
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased 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: 1.5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"# multiple_choice_cowese_beto_2\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased 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: 1.5e-05\n- train_batch_size: 1\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: 2\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #bert #multiple-choice #generated_from_trainer #base_model-dccuchile/bert-base-spanish-wwm-cased #endpoints_compatible #region-us \n",
"# multiple_choice_cowese_beto_2\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased 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: 1.5e-05\n- train_batch_size: 1\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: 2\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
63,
48,
6,
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103,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #bert #multiple-choice #generated_from_trainer #base_model-dccuchile/bert-base-spanish-wwm-cased #endpoints_compatible #region-us \n# multiple_choice_cowese_beto_2\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased 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: 1.5e-05\n- train_batch_size: 1\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: 2\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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null | null | transformers | <div align="center">
<h1 style="margin-bottom: 0.5em;">WebLINX: Real-World Website Navigation with Multi-Turn Dialogue</h1>
<em>Xing Han Lù*, Zdeněk Kasner*, Siva Reddy</em>
</div>
<div style="margin-bottom: 2em"></div>
<div style="display: flex; justify-content: space-around; align-items: center; font-size: 120%;">
<div><a href="https://arxiv.org/abs/2402.05930">📄Paper</a></div>
<div><a href="https://mcgill-nlp.github.io/weblinx">🌐Website</a></div>
<div><a href="https://huggingface.co/spaces/McGill-NLP/weblinx-explorer">💻Explorer</a></div>
<div><a href="https://huggingface.co/datasets/McGill-NLP/WebLINX">🤗Dataset</a></div>
<div><a href="https://github.com/McGill-NLP/weblinx">💾Code</a></div>
</div>
## Original Model
This model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\
[Click here to access the original model.](https://huggingface.co/google/pix2struct-large) | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["weblinx", "text-generation-inference", "web-agents", "agents"], "datasets": ["McGill-NLP/WebLINX", "McGill-NLP/WebLINX-full"], "metrics": ["f1", "iou", "chrf"], "pipeline_tag": "text-generation"} | text-generation | McGill-NLP/pix2act-large-weblinx | [
"transformers",
"pytorch",
"weblinx",
"text-generation-inference",
"web-agents",
"agents",
"text-generation",
"en",
"dataset:McGill-NLP/WebLINX",
"dataset:McGill-NLP/WebLINX-full",
"arxiv:2402.05930",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-07T22:11:48+00:00 | [
"2402.05930"
] | [
"en"
] | TAGS
#transformers #pytorch #weblinx #text-generation-inference #web-agents #agents #text-generation #en #dataset-McGill-NLP/WebLINX #dataset-McGill-NLP/WebLINX-full #arxiv-2402.05930 #license-apache-2.0 #endpoints_compatible #region-us
| <div align="center">
<h1 style="margin-bottom: 0.5em;">WebLINX: Real-World Website Navigation with Multi-Turn Dialogue</h1>
<em>Xing Han Lù*, Zdeněk Kasner*, Siva Reddy</em>
</div>
<div style="margin-bottom: 2em"></div>
<div style="display: flex; justify-content: space-around; align-items: center; font-size: 120%;">
<div><a href="URL
<div><a href="URL
<div><a href="URL
<div><a href="URL
<div><a href="URL
</div>
## Original Model
This model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\
Click here to access the original model. | [
"## Original Model\n\nThis model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\\\nClick here to access the original model."
] | [
"TAGS\n#transformers #pytorch #weblinx #text-generation-inference #web-agents #agents #text-generation #en #dataset-McGill-NLP/WebLINX #dataset-McGill-NLP/WebLINX-full #arxiv-2402.05930 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Original Model\n\nThis model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\\\nClick here to access the original model."
] | [
98,
34
] | [
"passage: TAGS\n#transformers #pytorch #weblinx #text-generation-inference #web-agents #agents #text-generation #en #dataset-McGill-NLP/WebLINX #dataset-McGill-NLP/WebLINX-full #arxiv-2402.05930 #license-apache-2.0 #endpoints_compatible #region-us \n## Original Model\n\nThis model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\\\nClick here to access the original model."
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] |
null | null | transformers |
### Grafted-Wind-Elementals-2x70B
<img src=https://huggingface.co/lodrick-the-lafted/Grafted-Wind-Elementals-2x70B/resolve/main/gwl.png>
A MoE merge of:
- [alchemonaut/BoreanGale-70B](https://huggingface.co/alchemonaut/BoreanGale-70B)
- [ShinojiResearch/Senku-70B-Full](https://huggingface.co/ShinojiResearch/Senku-70B-Full)
Why use one 70B when you could use two? <br/>
I really liked this model's writing ability. Just need a few more 3090s, right?
License: Non-commercial research use only.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lodrick-the-lafted__Grafted-Wind-Elementals-2x70B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |76.21|
|AI2 Reasoning Challenge (25-Shot)|73.38|
|HellaSwag (10-Shot) |89.08|
|MMLU (5-Shot) |75.79|
|TruthfulQA (0-shot) |65.57|
|Winogrande (5-shot) |84.85|
|GSM8k (5-shot) |68.61|
| {"license": "other", "tags": ["merge", "moe"], "base_model": "152334H/miqu-1-70b-sf", "model-index": [{"name": "Grafted-Wind-Elementals-2x70B", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 73.38, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Wind-Elementals-2x70B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 89.08, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Wind-Elementals-2x70B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 75.79, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Wind-Elementals-2x70B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 65.57}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Wind-Elementals-2x70B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 84.85, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Wind-Elementals-2x70B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 68.61, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Wind-Elementals-2x70B", "name": "Open LLM Leaderboard"}}]}]} | text-generation | lodrick-the-lafted/Grafted-Wind-Elementals-2x70B | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"merge",
"moe",
"base_model:152334H/miqu-1-70b-sf",
"license:other",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T22:23:45+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #merge #moe #base_model-152334H/miqu-1-70b-sf #license-other #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ### Grafted-Wind-Elementals-2x70B
<img src=URL
A MoE merge of:
* alchemonaut/BoreanGale-70B
* ShinojiResearch/Senku-70B-Full
Why use one 70B when you could use two?
I really liked this model's writing ability. Just need a few more 3090s, right?
License: Non-commercial research use only.
Open LLM Leaderboard Evaluation Results
=======================================
Detailed results can be found here
| [
"### Grafted-Wind-Elementals-2x70B\n\n\n<img src=URL\n\n\nA MoE merge of:\n\n\n* alchemonaut/BoreanGale-70B\n* ShinojiResearch/Senku-70B-Full\n\n\nWhy use one 70B when you could use two? \n\nI really liked this model's writing ability. Just need a few more 3090s, right?\n\n\nLicense: Non-commercial research use only.\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here"
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #merge #moe #base_model-152334H/miqu-1-70b-sf #license-other #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Grafted-Wind-Elementals-2x70B\n\n\n<img src=URL\n\n\nA MoE merge of:\n\n\n* alchemonaut/BoreanGale-70B\n* ShinojiResearch/Senku-70B-Full\n\n\nWhy use one 70B when you could use two? \n\nI really liked this model's writing ability. Just need a few more 3090s, right?\n\n\nLicense: Non-commercial research use only.\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here"
] | [
79,
116
] | [
"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #merge #moe #base_model-152334H/miqu-1-70b-sf #license-other #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Grafted-Wind-Elementals-2x70B\n\n\n<img src=URL\n\n\nA MoE merge of:\n\n\n* alchemonaut/BoreanGale-70B\n* ShinojiResearch/Senku-70B-Full\n\n\nWhy use one 70B when you could use two? \n\nI really liked this model's writing ability. Just need a few more 3090s, right?\n\n\nLicense: Non-commercial research use only.\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here"
] | [
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] |
null | null | transformers |
llama.cpp conversion of https://huggingface.co/euclaise/Memphis-scribe-3B
except for f16 and q8_0, every quant is using the imatrix from wiki-train
### ppl (512 wiki.test)
| quant | ppl (lower is better) |
|---------------|-----|
| f16(baseline) | 9.9437 +/- 0.07019 |
| q8_0 | 9.9474 +/- 0.07022 |
| q5_k_m | 10.0347 +/- 0.07091 |
| q4_k_m | 10.1192 +/- 0.07152 |
| iq3_xxs | 11.5266 +/- 0.08157 |
| q2_k | 13.0623 +/- 0.09548 |
| iq2_xs | 16.6174 +/- 0.11807 |
| iq2_xxs | 22.6462 +/- 0.16226 | | {"language": ["en"], "license": "cc-by-4.0", "library_name": "transformers", "tags": ["supertrainer2000", "not-for-all-audiences", "writing", "roleplay", "gguf", "stablelm_epoch", "gguf-imatrix"], "base_model": ["euclaise/Memphis-scribe-3B"], "model_type": "stablelm_epoch", "quantized_by": "Green-Sky"} | null | Green-Sky/euclaise-Memphis-scribe-3B-GGUF-iMatrix | [
"transformers",
"gguf",
"supertrainer2000",
"not-for-all-audiences",
"writing",
"roleplay",
"stablelm_epoch",
"gguf-imatrix",
"en",
"base_model:euclaise/Memphis-scribe-3B",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | 2024-02-07T22:23:57+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #supertrainer2000 #not-for-all-audiences #writing #roleplay #stablelm_epoch #gguf-imatrix #en #base_model-euclaise/Memphis-scribe-3B #license-cc-by-4.0 #endpoints_compatible #region-us
| URL conversion of URL
except for f16 and q8\_0, every quant is using the imatrix from wiki-train
### ppl (512 URL)
| [
"### ppl (512 URL)"
] | [
"TAGS\n#transformers #gguf #supertrainer2000 #not-for-all-audiences #writing #roleplay #stablelm_epoch #gguf-imatrix #en #base_model-euclaise/Memphis-scribe-3B #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"### ppl (512 URL)"
] | [
79,
8
] | [
"passage: TAGS\n#transformers #gguf #supertrainer2000 #not-for-all-audiences #writing #roleplay #stablelm_epoch #gguf-imatrix #en #base_model-euclaise/Memphis-scribe-3B #license-cc-by-4.0 #endpoints_compatible #region-us \n### ppl (512 URL)"
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] |
null | null | transformers |
# Roya
Roya is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [mlabonne/Daredevil-7B](https://huggingface.co/mlabonne/Daredevil-7B)
* [senseable/garten2-7b](https://huggingface.co/senseable/garten2-7b)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: mlabonne/Daredevil-7B
layer_range: [0, 32]
- model: senseable/garten2-7b
layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/Daredevil-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "mlabonne/Daredevil-7B", "senseable/garten2-7b"]} | text-generation | Eric111/Roya | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"mlabonne/Daredevil-7B",
"senseable/garten2-7b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T22:26:31+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/Daredevil-7B #senseable/garten2-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Roya
Roya is a merge of the following models using mergekit:
* mlabonne/Daredevil-7B
* senseable/garten2-7b
## Configuration
| [
"# Roya\n\nRoya is a merge of the following models using mergekit:\n* mlabonne/Daredevil-7B\n* senseable/garten2-7b",
"## Configuration"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/Daredevil-7B #senseable/garten2-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Roya\n\nRoya is a merge of the following models using mergekit:\n* mlabonne/Daredevil-7B\n* senseable/garten2-7b",
"## Configuration"
] | [
85,
34,
4
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/Daredevil-7B #senseable/garten2-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Roya\n\nRoya is a merge of the following models using mergekit:\n* mlabonne/Daredevil-7B\n* senseable/garten2-7b## Configuration"
] | [
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null | null | null | ### Original model: https://civitai.com/models/139565/realistic-stock-photo?modelVersionId=294470
#### Utilize it judiciously and adhere to the legal regulations established by authorities | {"tags": ["safetensors", "realistic", "civitai"], "pipeline_tag": "text-to-image"} | text-to-image | tensor-diffusion/Realistic_Stock_Photo_v2 | [
"safetensors",
"realistic",
"civitai",
"text-to-image",
"region:us"
] | 2024-02-07T22:33:06+00:00 | [] | [] | TAGS
#safetensors #realistic #civitai #text-to-image #region-us
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#### Utilize it judiciously and adhere to the legal regulations established by authorities | [
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null | null | transformers |
# AraDPR: Arabic Dense Passage Retrieval Model
AraDPR is a state-of-the-art dense passage retrieval model specifically designed for the Arabic language. It leverages deep learning techniques to encode passages and questions into dense vectors, facilitating efficient and accurate retrieval for question-answering systems.
## Model Details
### Model Description
- **Developed by:**
- **Model type:** Dense Passage Retrieval (DPR)
- **Language(s) (NLP):** Arabic
- **License:** MIT
- **Finetuned from:** AraBERT
### Model Sources
- **Repository:** https://github.com/DataScienceUIBK/ArabicaQA
- **Paper:** will be available soon
- **Demo:** will be available soon
## Uses
### Direct Use
AraDPR is designed for use in Arabic question-answering systems, enabling these systems to retrieve the most relevant passages from a large corpus efficiently.
### Downstream Use
Beyond question answering, AraDPR can be integrated into various NLP applications requiring passage retrieval, such as document summarization, information extraction, and more.
### Out-of-Scope Use
AraDPR is not intended for languages other than Arabic or for tasks that do not involve passage retrieval.
## Bias, Risks, and Limitations
While AraDPR represents a significant advancement in Arabic NLP, users should be aware of the model's limitations, particularly in handling dialects or very domain-specific texts. Further research and development are encouraged to address these challenges.
## How to Get Started with the Model
To get started with AraDPR, you can use the following code snippet:
Please check out our github page: https://github.com/DataScienceUIBK/ArabicaQA
## Training Details
AraDPR was trained on a diverse corpus from Arabic Wikipedia, covering a wide range of topics to ensure comprehensive language representation.
## Results
AraDPR demonstrates superior performance over traditional retrieval methods, significantly improving the efficiency and accuracy of question answering in Arabic.
## Technical Specifications
Model Architecture and Objective
AraDPR utilizes a dual-encoder architecture, with separate encoders for questions and passages. The model is optimized to project semantically related questions and passages closer in the vector space. | {"language": ["ar"], "license": "mit", "library_name": "transformers", "datasets": ["abdoelsayed/Open-ArabicaQA", "abdoelsayed/ArabicaQA"], "metrics": ["accuracy"], "pipeline_tag": "question-answering"} | question-answering | abdoelsayed/AraDPR | [
"transformers",
"pytorch",
"question-answering",
"ar",
"dataset:abdoelsayed/Open-ArabicaQA",
"dataset:abdoelsayed/ArabicaQA",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-07T22:34:57+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #question-answering #ar #dataset-abdoelsayed/Open-ArabicaQA #dataset-abdoelsayed/ArabicaQA #license-mit #endpoints_compatible #region-us
|
# AraDPR: Arabic Dense Passage Retrieval Model
AraDPR is a state-of-the-art dense passage retrieval model specifically designed for the Arabic language. It leverages deep learning techniques to encode passages and questions into dense vectors, facilitating efficient and accurate retrieval for question-answering systems.
## Model Details
### Model Description
- Developed by:
- Model type: Dense Passage Retrieval (DPR)
- Language(s) (NLP): Arabic
- License: MIT
- Finetuned from: AraBERT
### Model Sources
- Repository: URL
- Paper: will be available soon
- Demo: will be available soon
## Uses
### Direct Use
AraDPR is designed for use in Arabic question-answering systems, enabling these systems to retrieve the most relevant passages from a large corpus efficiently.
### Downstream Use
Beyond question answering, AraDPR can be integrated into various NLP applications requiring passage retrieval, such as document summarization, information extraction, and more.
### Out-of-Scope Use
AraDPR is not intended for languages other than Arabic or for tasks that do not involve passage retrieval.
## Bias, Risks, and Limitations
While AraDPR represents a significant advancement in Arabic NLP, users should be aware of the model's limitations, particularly in handling dialects or very domain-specific texts. Further research and development are encouraged to address these challenges.
## How to Get Started with the Model
To get started with AraDPR, you can use the following code snippet:
Please check out our github page: URL
## Training Details
AraDPR was trained on a diverse corpus from Arabic Wikipedia, covering a wide range of topics to ensure comprehensive language representation.
## Results
AraDPR demonstrates superior performance over traditional retrieval methods, significantly improving the efficiency and accuracy of question answering in Arabic.
## Technical Specifications
Model Architecture and Objective
AraDPR utilizes a dual-encoder architecture, with separate encoders for questions and passages. The model is optimized to project semantically related questions and passages closer in the vector space. | [
"# AraDPR: Arabic Dense Passage Retrieval Model\n\nAraDPR is a state-of-the-art dense passage retrieval model specifically designed for the Arabic language. It leverages deep learning techniques to encode passages and questions into dense vectors, facilitating efficient and accurate retrieval for question-answering systems.",
"## Model Details",
"### Model Description\n\n- Developed by: \n- Model type: Dense Passage Retrieval (DPR)\n- Language(s) (NLP): Arabic\n- License: MIT\n- Finetuned from: AraBERT",
"### Model Sources\n\n- Repository: URL\n- Paper: will be available soon\n- Demo: will be available soon",
"## Uses",
"### Direct Use\n\nAraDPR is designed for use in Arabic question-answering systems, enabling these systems to retrieve the most relevant passages from a large corpus efficiently.",
"### Downstream Use\n\nBeyond question answering, AraDPR can be integrated into various NLP applications requiring passage retrieval, such as document summarization, information extraction, and more.",
"### Out-of-Scope Use\n\nAraDPR is not intended for languages other than Arabic or for tasks that do not involve passage retrieval.",
"## Bias, Risks, and Limitations\n\nWhile AraDPR represents a significant advancement in Arabic NLP, users should be aware of the model's limitations, particularly in handling dialects or very domain-specific texts. Further research and development are encouraged to address these challenges.",
"## How to Get Started with the Model\n\nTo get started with AraDPR, you can use the following code snippet:\n\nPlease check out our github page: URL",
"## Training Details\nAraDPR was trained on a diverse corpus from Arabic Wikipedia, covering a wide range of topics to ensure comprehensive language representation.",
"## Results\nAraDPR demonstrates superior performance over traditional retrieval methods, significantly improving the efficiency and accuracy of question answering in Arabic.",
"## Technical Specifications\nModel Architecture and Objective\nAraDPR utilizes a dual-encoder architecture, with separate encoders for questions and passages. The model is optimized to project semantically related questions and passages closer in the vector space."
] | [
"TAGS\n#transformers #pytorch #question-answering #ar #dataset-abdoelsayed/Open-ArabicaQA #dataset-abdoelsayed/ArabicaQA #license-mit #endpoints_compatible #region-us \n",
"# AraDPR: Arabic Dense Passage Retrieval Model\n\nAraDPR is a state-of-the-art dense passage retrieval model specifically designed for the Arabic language. It leverages deep learning techniques to encode passages and questions into dense vectors, facilitating efficient and accurate retrieval for question-answering systems.",
"## Model Details",
"### Model Description\n\n- Developed by: \n- Model type: Dense Passage Retrieval (DPR)\n- Language(s) (NLP): Arabic\n- License: MIT\n- Finetuned from: AraBERT",
"### Model Sources\n\n- Repository: URL\n- Paper: will be available soon\n- Demo: will be available soon",
"## Uses",
"### Direct Use\n\nAraDPR is designed for use in Arabic question-answering systems, enabling these systems to retrieve the most relevant passages from a large corpus efficiently.",
"### Downstream Use\n\nBeyond question answering, AraDPR can be integrated into various NLP applications requiring passage retrieval, such as document summarization, information extraction, and more.",
"### Out-of-Scope Use\n\nAraDPR is not intended for languages other than Arabic or for tasks that do not involve passage retrieval.",
"## Bias, Risks, and Limitations\n\nWhile AraDPR represents a significant advancement in Arabic NLP, users should be aware of the model's limitations, particularly in handling dialects or very domain-specific texts. Further research and development are encouraged to address these challenges.",
"## How to Get Started with the Model\n\nTo get started with AraDPR, you can use the following code snippet:\n\nPlease check out our github page: URL",
"## Training Details\nAraDPR was trained on a diverse corpus from Arabic Wikipedia, covering a wide range of topics to ensure comprehensive language representation.",
"## Results\nAraDPR demonstrates superior performance over traditional retrieval methods, significantly improving the efficiency and accuracy of question answering in Arabic.",
"## Technical Specifications\nModel Architecture and Objective\nAraDPR utilizes a dual-encoder architecture, with separate encoders for questions and passages. The model is optimized to project semantically related questions and passages closer in the vector space."
] | [
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"passage: TAGS\n#transformers #pytorch #question-answering #ar #dataset-abdoelsayed/Open-ArabicaQA #dataset-abdoelsayed/ArabicaQA #license-mit #endpoints_compatible #region-us \n# AraDPR: Arabic Dense Passage Retrieval Model\n\nAraDPR is a state-of-the-art dense passage retrieval model specifically designed for the Arabic language. It leverages deep learning techniques to encode passages and questions into dense vectors, facilitating efficient and accurate retrieval for question-answering systems.## Model Details### Model Description\n\n- Developed by: \n- Model type: Dense Passage Retrieval (DPR)\n- Language(s) (NLP): Arabic\n- License: MIT\n- Finetuned from: AraBERT### Model Sources\n\n- Repository: URL\n- Paper: will be available soon\n- Demo: will be available soon## Uses### Direct Use\n\nAraDPR is designed for use in Arabic question-answering systems, enabling these systems to retrieve the most relevant passages from a large corpus efficiently.### Downstream Use\n\nBeyond question answering, AraDPR can be integrated into various NLP applications requiring passage retrieval, such as document summarization, information extraction, and more.### Out-of-Scope Use\n\nAraDPR is not intended for languages other than Arabic or for tasks that do not involve passage retrieval.## Bias, Risks, and Limitations\n\nWhile AraDPR represents a significant advancement in Arabic NLP, users should be aware of the model's limitations, particularly in handling dialects or very domain-specific texts. Further research and development are encouraged to address these challenges.## How to Get Started with the Model\n\nTo get started with AraDPR, you can use the following code snippet:\n\nPlease check out our github page: URL## Training Details\nAraDPR was trained on a diverse corpus from Arabic Wikipedia, covering a wide range of topics to ensure comprehensive language representation.## Results\nAraDPR demonstrates superior performance over traditional retrieval methods, significantly improving the efficiency and accuracy of question answering in Arabic."
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] |
null | null | transformers |
# NeuraLake-m7-v2-AshhLimaRP-Mistral-7B ⚡
- **Base model :** [Meggido/NeuraLake-m7-v2-7B](https://huggingface.co/Meggido/NeuraLake-m7-v2-7B)
- **LoRA adapter :** [lemonilia/AshhLimaRP-Mistral-7B](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B)
Many thanks to [Herman555](https://huggingface.co/Herman555) for his invaluable help 🤗.
## 📜 Prompt format
[Extended Alpaca format](https://github.com/tatsu-lab/stanford_alpaca),
with `### Instruction:`, `### Input:` immediately preceding user inputs and `### Response:`
immediately preceding model outputs. While Alpaca wasn't originally intended for multi-turn
responses, in practice this is not a problem; the format follows a pattern already used by
other models.
```
### Instruction:
Character's Persona: {bot character description}
User's Persona: {user character description}
Scenario: {what happens in the story}
Play the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User.
### Input:
User: {utterance}
### Response:
Character: {utterance}
### Input
User: {utterance}
### Response:
Character: {utterance}
(etc.)
```
### Message length control
Inspired by the previously named "Roleplay" preset in SillyTavern, with this
version of LimaRP it is possible to append a length modifier to the response instruction
sequence, like this:
```
### Input
User: {utterance}
### Response: (length = medium)
Character: {utterance}
```
This has an immediately noticeable effect on bot responses. The lengths using during training are:
`micro`, `tiny`, `short`, `medium`, `long`, `massive`, `huge`, `enormous`, `humongous`, `unlimited`.
**The recommended starting length is medium**. Keep in mind that the AI can ramble or impersonate
the user with very long messages.
The length control effect is reproducible, but the messages will not necessarily follow
lengths very precisely, rather follow certain ranges on average, as seen in this table
with data from tests made with one reply at the beginning of the conversation:
Response length control appears to work well also deep into the conversation. **By omitting
the modifier, the model will choose the most appropriate response length** (although it might
not necessarily be what the user desires). | {"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences"]} | text-generation | Meggido/NeuraLake-m7-v2-AshhLimaRP-Mistral-7B | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"not-for-all-audiences",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T22:36:32+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #not-for-all-audiences #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# NeuraLake-m7-v2-AshhLimaRP-Mistral-7B
- Base model : Meggido/NeuraLake-m7-v2-7B
- LoRA adapter : lemonilia/AshhLimaRP-Mistral-7B
Many thanks to Herman555 for his invaluable help .
## Prompt format
Extended Alpaca format,
with '### Instruction:', '### Input:' immediately preceding user inputs and '### Response:'
immediately preceding model outputs. While Alpaca wasn't originally intended for multi-turn
responses, in practice this is not a problem; the format follows a pattern already used by
other models.
### Message length control
Inspired by the previously named "Roleplay" preset in SillyTavern, with this
version of LimaRP it is possible to append a length modifier to the response instruction
sequence, like this:
This has an immediately noticeable effect on bot responses. The lengths using during training are:
'micro', 'tiny', 'short', 'medium', 'long', 'massive', 'huge', 'enormous', 'humongous', 'unlimited'.
The recommended starting length is medium. Keep in mind that the AI can ramble or impersonate
the user with very long messages.
The length control effect is reproducible, but the messages will not necessarily follow
lengths very precisely, rather follow certain ranges on average, as seen in this table
with data from tests made with one reply at the beginning of the conversation:
!lengths
Response length control appears to work well also deep into the conversation. By omitting
the modifier, the model will choose the most appropriate response length (although it might
not necessarily be what the user desires). | [
"# NeuraLake-m7-v2-AshhLimaRP-Mistral-7B \n\n- Base model : Meggido/NeuraLake-m7-v2-7B\n- LoRA adapter : lemonilia/AshhLimaRP-Mistral-7B\n\nMany thanks to Herman555 for his invaluable help .",
"## Prompt format\nExtended Alpaca format,\nwith '### Instruction:', '### Input:' immediately preceding user inputs and '### Response:'\nimmediately preceding model outputs. While Alpaca wasn't originally intended for multi-turn\nresponses, in practice this is not a problem; the format follows a pattern already used by\nother models.",
"### Message length control\nInspired by the previously named \"Roleplay\" preset in SillyTavern, with this\nversion of LimaRP it is possible to append a length modifier to the response instruction\nsequence, like this:\n\n\nThis has an immediately noticeable effect on bot responses. The lengths using during training are:\n'micro', 'tiny', 'short', 'medium', 'long', 'massive', 'huge', 'enormous', 'humongous', 'unlimited'.\nThe recommended starting length is medium. Keep in mind that the AI can ramble or impersonate\nthe user with very long messages.\n\nThe length control effect is reproducible, but the messages will not necessarily follow\nlengths very precisely, rather follow certain ranges on average, as seen in this table\nwith data from tests made with one reply at the beginning of the conversation:\n\n!lengths\n\nResponse length control appears to work well also deep into the conversation. By omitting\nthe modifier, the model will choose the most appropriate response length (although it might\nnot necessarily be what the user desires)."
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #not-for-all-audiences #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# NeuraLake-m7-v2-AshhLimaRP-Mistral-7B \n\n- Base model : Meggido/NeuraLake-m7-v2-7B\n- LoRA adapter : lemonilia/AshhLimaRP-Mistral-7B\n\nMany thanks to Herman555 for his invaluable help .",
"## Prompt format\nExtended Alpaca format,\nwith '### Instruction:', '### Input:' immediately preceding user inputs and '### Response:'\nimmediately preceding model outputs. While Alpaca wasn't originally intended for multi-turn\nresponses, in practice this is not a problem; the format follows a pattern already used by\nother models.",
"### Message length control\nInspired by the previously named \"Roleplay\" preset in SillyTavern, with this\nversion of LimaRP it is possible to append a length modifier to the response instruction\nsequence, like this:\n\n\nThis has an immediately noticeable effect on bot responses. The lengths using during training are:\n'micro', 'tiny', 'short', 'medium', 'long', 'massive', 'huge', 'enormous', 'humongous', 'unlimited'.\nThe recommended starting length is medium. Keep in mind that the AI can ramble or impersonate\nthe user with very long messages.\n\nThe length control effect is reproducible, but the messages will not necessarily follow\nlengths very precisely, rather follow certain ranges on average, as seen in this table\nwith data from tests made with one reply at the beginning of the conversation:\n\n!lengths\n\nResponse length control appears to work well also deep into the conversation. By omitting\nthe modifier, the model will choose the most appropriate response length (although it might\nnot necessarily be what the user desires)."
] | [
67,
69,
82,
244
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #not-for-all-audiences #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# NeuraLake-m7-v2-AshhLimaRP-Mistral-7B \n\n- Base model : Meggido/NeuraLake-m7-v2-7B\n- LoRA adapter : lemonilia/AshhLimaRP-Mistral-7B\n\nMany thanks to Herman555 for his invaluable help .## Prompt format\nExtended Alpaca format,\nwith '### Instruction:', '### Input:' immediately preceding user inputs and '### Response:'\nimmediately preceding model outputs. While Alpaca wasn't originally intended for multi-turn\nresponses, in practice this is not a problem; the format follows a pattern already used by\nother models.### Message length control\nInspired by the previously named \"Roleplay\" preset in SillyTavern, with this\nversion of LimaRP it is possible to append a length modifier to the response instruction\nsequence, like this:\n\n\nThis has an immediately noticeable effect on bot responses. The lengths using during training are:\n'micro', 'tiny', 'short', 'medium', 'long', 'massive', 'huge', 'enormous', 'humongous', 'unlimited'.\nThe recommended starting length is medium. Keep in mind that the AI can ramble or impersonate\nthe user with very long messages.\n\nThe length control effect is reproducible, but the messages will not necessarily follow\nlengths very precisely, rather follow certain ranges on average, as seen in this table\nwith data from tests made with one reply at the beginning of the conversation:\n\n!lengths\n\nResponse length control appears to work well also deep into the conversation. By omitting\nthe modifier, the model will choose the most appropriate response length (although it might\nnot necessarily be what the user desires)."
] | [
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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": "261.40 +/- 21.63", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Stoub/Stoub-ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-07T22:37:59+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 | peft |
[<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)
### Model Description
A model that can generate [Honeycomb Queries](https://www.honeycomb.io/blog/introducing-query-assistant).
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).
_fine-tuned by [Hamel Husain](https://hamel.dev)_
# Usage
You can use this model with the following code:
First, download the model
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model_id='parlance-labs/hc-mistral-alpaca'
model = AutoPeftModelForCausalLM.from_pretrained(model_id).cuda()
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token
```
Then, construct the prompt template like so:
```python
def prompt(nlq, cols):
return f"""Honeycomb is an observability platform that allows you to write queries to inspect trace data. You are an assistant that takes a natural language query (NLQ) and a list of valid columns and produce a Honeycomb query.
### Instruction:
NLQ: "{nlq}"
Columns: {cols}
### Response:
"""
def prompt_tok(nlq, cols):
_p = prompt(nlq, cols)
input_ids = tokenizer(_p, return_tensors="pt", truncation=True).input_ids.cuda()
out_ids = model.generate(input_ids=input_ids, max_new_tokens=5000,
do_sample=False)
return tokenizer.batch_decode(out_ids.detach().cpu().numpy(),
skip_special_tokens=True)[0][len(_p):]
```
Finally, you can get predictions like this:
```python
# model inputs
nlq = "Exception count by exception and caller"
cols = ['error', 'exception.message', 'exception.type', 'exception.stacktrace', 'SampleRate', 'name', 'db.user', 'type', 'duration_ms', 'db.name', 'service.name', 'http.method', 'db.system', 'status_code', 'db.operation', 'library.name', 'process.pid', 'net.transport', 'messaging.system', 'rpc.system', 'http.target', 'db.statement', 'library.version', 'status_message', 'parent_name', 'aws.region', 'process.command', 'rpc.method', 'span.kind', 'serializer.name', 'net.peer.name', 'rpc.service', 'http.scheme', 'process.runtime.name', 'serializer.format', 'serializer.renderer', 'net.peer.port', 'process.runtime.version', 'http.status_code', 'telemetry.sdk.language', 'trace.parent_id', 'process.runtime.description', 'span.num_events', 'messaging.destination', 'net.peer.ip', 'trace.trace_id', 'telemetry.instrumentation_library', 'trace.span_id', 'span.num_links', 'meta.signal_type', 'http.route']
# print prediction
out = prompt_tok(nlq, cols)
print(nlq, '\n', out)
```
This will give you a prediction that looks like this:
```md
"{'breakdowns': ['exception.message', 'exception.type'], 'calculations': [{'op': 'COUNT'}], 'filters': [{'column': 'exception.message', 'op': 'exists'}, {'column': 'exception.type', 'op': 'exists'}], 'orders': [{'op': 'COUNT', 'order': 'descending'}], 'time_range': 7200}"
```
Alternatively, you can play with this model on Replicate: [hamelsmu/honeycomb-2](https://replicate.com/hamelsmu/honeycomb-2)
# Hosted Inference
This model is hosted on Replicate: (hamelsmu/honeycomb-2)[https://replicate.com/hamelsmu/honeycomb-2], using [this config](https://github.com/hamelsmu/replicate-examples/tree/master/mistral-transformers-2).
# Training Procedure
Used [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl/tree/main), see [this config](configs/axolotl_config.yml). See this [wandb run](https://wandb.ai/hamelsmu/hc-axolotl-mistral/runs/7dq9l9vu/overview) to see training metrics.
### Framework versions
- PEFT 0.7.0
- Transformers 4.37.0.dev0
- Pytorch 2.1.0
- Datasets 2.15.0
- Tokenizers 0.15.0 | {"license": "apache-2.0", "library_name": "peft", "tags": ["axolotl", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "hc-mistral-alpaca", "results": []}]} | null | parlance-labs/hc-mistral-alpaca | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"4-bit",
"region:us"
] | 2024-02-07T22:43:59+00:00 | [] | [] | TAGS
#peft #safetensors #mistral #axolotl #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #4-bit #region-us
|
<img src="URL alt="Built with Axolotl" width="200" height="32"/>
### Model Description
A model that can generate Honeycomb Queries.
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1.
_fine-tuned by Hamel Husain_
# Usage
You can use this model with the following code:
First, download the model
Then, construct the prompt template like so:
Finally, you can get predictions like this:
This will give you a prediction that looks like this:
Alternatively, you can play with this model on Replicate: hamelsmu/honeycomb-2
# Hosted Inference
This model is hosted on Replicate: (hamelsmu/honeycomb-2)[URL using this config.
# Training Procedure
Used axolotl, see this config. See this wandb run to see training metrics.
### Framework versions
- PEFT 0.7.0
- Transformers 4.37.0.dev0
- Pytorch 2.1.0
- Datasets 2.15.0
- Tokenizers 0.15.0 | [
"### Model Description\n\nA model that can generate Honeycomb Queries. \nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1.\n\n_fine-tuned by Hamel Husain_",
"# Usage\n\nYou can use this model with the following code:\n\nFirst, download the model \n\n\n\nThen, construct the prompt template like so:\n\n\n\nFinally, you can get predictions like this:\n\n\n\nThis will give you a prediction that looks like this:\n\n \n\nAlternatively, you can play with this model on Replicate: hamelsmu/honeycomb-2",
"# Hosted Inference\n\nThis model is hosted on Replicate: (hamelsmu/honeycomb-2)[URL using this config.",
"# Training Procedure \n\nUsed axolotl, see this config. See this wandb run to see training metrics.",
"### Framework versions\n\n- PEFT 0.7.0\n- Transformers 4.37.0.dev0\n- Pytorch 2.1.0\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
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"### Model Description\n\nA model that can generate Honeycomb Queries. \nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1.\n\n_fine-tuned by Hamel Husain_",
"# Usage\n\nYou can use this model with the following code:\n\nFirst, download the model \n\n\n\nThen, construct the prompt template like so:\n\n\n\nFinally, you can get predictions like this:\n\n\n\nThis will give you a prediction that looks like this:\n\n \n\nAlternatively, you can play with this model on Replicate: hamelsmu/honeycomb-2",
"# Hosted Inference\n\nThis model is hosted on Replicate: (hamelsmu/honeycomb-2)[URL using this config.",
"# Training Procedure \n\nUsed axolotl, see this config. See this wandb run to see training metrics.",
"### Framework versions\n\n- PEFT 0.7.0\n- Transformers 4.37.0.dev0\n- Pytorch 2.1.0\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
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"passage: TAGS\n#peft #safetensors #mistral #axolotl #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #4-bit #region-us \n### Model Description\n\nA model that can generate Honeycomb Queries. \nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1.\n\n_fine-tuned by Hamel Husain_# Usage\n\nYou can use this model with the following code:\n\nFirst, download the model \n\n\n\nThen, construct the prompt template like so:\n\n\n\nFinally, you can get predictions like this:\n\n\n\nThis will give you a prediction that looks like this:\n\n \n\nAlternatively, you can play with this model on Replicate: hamelsmu/honeycomb-2# Hosted Inference\n\nThis model is hosted on Replicate: (hamelsmu/honeycomb-2)[URL using this config.# Training Procedure \n\nUsed axolotl, see this config. See this wandb run to see training metrics.### Framework versions\n\n- PEFT 0.7.0\n- Transformers 4.37.0.dev0\n- Pytorch 2.1.0\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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null | null | stable-baselines3 |
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.26 +/- 0.11", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | lockylocks/a2c-PandaReachDense-v3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-07T22:44:13+00:00 | [] | [] | TAGS
#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing PandaReachDense-v3
This is a trained model of a A2C agent playing PandaReachDense-v3
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
41,
45,
17
] | [
"passage: TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\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
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| {"library_name": "transformers", "tags": []} | text-generation | ydang/jsd_Mistral-7B-v0.1-M3 | [
"transformers",
"safetensors",
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# Model Card for Model ID
## Model Details
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- Developed by:
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## 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
### Training Data
### Training Procedure
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
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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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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-PixelCopter1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "31.30 +/- 19.87", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | ORromu/Reinforce-PixelCopter1 | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2024-02-07T22:47:45+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 | null |
## Exllama v2 Quantizations of Everyone-Coder-33b-v2-Base
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization.
## The "main" branch only contains the measurement.json, 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 meaurement.json for further conversions.
Conversion was done using the default calibration dataset.
Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
Original model: https://huggingface.co/rombodawg/Everyone-Coder-33b-v2-Base
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
| ------ | ---- | ------------ | ---- | ---- | ---- | ----------- |
| [6_5](https://huggingface.co/bartowski/Everyone-Coder-33b-v2-Base-exl2/tree/6_5) | 6.5 | 8.0 | 28.9 GB | 31.6 GB | 35.6 GB | Near unquantized performance at vastly reduced size, **recommended**. |
| [4_25](https://huggingface.co/bartowski/Everyone-Coder-33b-v2-Base-exl2/tree/4_25) | 4.25 | 6.0 | 19.5 GB | 22.2 GB | 26.2 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/Everyone-Coder-33b-v2-Base-exl2/tree/3_5) | 3.5 | 6.0 | 16.5 GB | 19.2 GB | 23.2 GB | Lower quality, only use if you have to. |
| [3_0](https://huggingface.co/bartowski/Everyone-Coder-33b-v2-Base-exl2/tree/3_0) | 3.0 | 6.0 | 14.3 GB | 17.0 GB | 21.0 GB | Very low quality, usable with 16gb of VRAM. |
| [2_4](https://huggingface.co/bartowski/Everyone-Coder-33b-v2-Base-exl2/tree/3_0) | 2.4 | 6.0 | 12.0 GB | 14.7 GB | 18.7 GB | Extremely low quality, only recommended if trying to fit into 12GB with under 4k context. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Everyone-Coder-33b-v2-Base-exl2
```
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 `Everyone-Coder-33b-v2-Base-exl2`:
```shell
mkdir Everyone-Coder-33b-v2-Base-exl2
huggingface-cli download bartowski/Everyone-Coder-33b-v2-Base-exl2 --local-dir Everyone-Coder-33b-v2-Base-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir Everyone-Coder-33b-v2-Base-exl2-6_5
huggingface-cli download bartowski/Everyone-Coder-33b-v2-Base-exl2 --revision 6_5 --local-dir Everyone-Coder-33b-v2-Base-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir Everyone-Coder-33b-v2-Base-exl2-6.5
huggingface-cli download bartowski/Everyone-Coder-33b-v2-Base-exl2 --revision 6_5 --local-dir Everyone-Coder-33b-v2-Base-exl2-6.5 --local-dir-use-symlinks False
``` | {"license": "other", "tags": ["merge"], "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL", "quantized_by": "bartowski", "pipeline_tag": "text-generation"} | text-generation | bartowski/Everyone-Coder-33b-v2-Base-exl2 | [
"merge",
"text-generation",
"license:other",
"region:us"
] | 2024-02-07T22:48:24+00:00 | [] | [] | TAGS
#merge #text-generation #license-other #region-us
| Exllama v2 Quantizations of Everyone-Coder-33b-v2-Base
------------------------------------------------------
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.
Conversion was done using the default calibration dataset.
Default arguments used except when the bits per weight is above 6.0, at that point the lm\_head layer is quantized at 8 bits per weight instead of the default 6.
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 'Everyone-Coder-33b-v2-Base-exl2':
To download from a different branch, add the '--revision' parameter:
Linux:
Windows (which apparently doesn't like \_ in folders sometimes?):
| [] | [
"TAGS\n#merge #text-generation #license-other #region-us \n"
] | [
19
] | [
"passage: TAGS\n#merge #text-generation #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 | davisalex22/BLOOMTurismEC-7b1-ft | [
"transformers",
"safetensors",
"bloom",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #bloom #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
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## 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
### 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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## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
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## Glossary [optional]
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"passage: TAGS\n#transformers #safetensors #bloom #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 | 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. -->
# hubert_2
This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1346
- Wer: 0.3402
- Cer: 0.1540
## 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: 0.00045
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 9.7055 | 1.0 | 60 | 7.8663 | 0.9866 | 0.9959 |
| 5.5899 | 2.0 | 120 | 5.2642 | 0.9866 | 0.9959 |
| 3.7833 | 3.0 | 180 | 3.5788 | 0.9866 | 0.9959 |
| 2.9012 | 4.0 | 240 | 2.8210 | 0.9866 | 0.9959 |
| 2.1341 | 5.0 | 300 | 1.9008 | 1.0 | 0.9586 |
| 1.2658 | 6.0 | 360 | 1.1701 | 1.0 | 0.5694 |
| 0.9522 | 7.0 | 420 | 0.8699 | 0.8286 | 0.5799 |
| 0.8633 | 8.0 | 480 | 0.7279 | 0.8141 | 0.4884 |
| 0.7882 | 9.0 | 540 | 0.6970 | 0.8130 | 0.4735 |
| 0.7349 | 10.0 | 600 | 0.6688 | 0.8167 | 0.5408 |
| 0.7332 | 11.0 | 660 | 0.6299 | 0.8092 | 0.4509 |
| 0.7166 | 12.0 | 720 | 0.6356 | 0.8130 | 0.4636 |
| 0.6788 | 13.0 | 780 | 0.6422 | 0.8342 | 0.4957 |
| 0.6788 | 14.0 | 840 | 0.6045 | 0.8234 | 0.4841 |
| 0.6693 | 15.0 | 900 | 0.6465 | 0.8092 | 0.5623 |
| 0.7044 | 16.0 | 960 | 0.6108 | 0.7891 | 0.4061 |
| 0.6139 | 17.0 | 1020 | 0.5790 | 0.8085 | 0.4295 |
| 1.3112 | 18.0 | 1080 | 0.5187 | 0.7861 | 0.4443 |
| 0.5269 | 19.0 | 1140 | 0.4804 | 0.7623 | 0.4557 |
| 0.5145 | 20.0 | 1200 | 0.4333 | 0.7139 | 0.3764 |
| 0.4495 | 21.0 | 1260 | 0.4026 | 0.6788 | 0.3723 |
| 0.5265 | 22.0 | 1320 | 0.3457 | 0.5384 | 0.2027 |
| 0.3611 | 23.0 | 1380 | 0.2956 | 0.5075 | 0.2162 |
| 0.3112 | 24.0 | 1440 | 0.2769 | 0.4732 | 0.2023 |
| 0.2774 | 25.0 | 1500 | 0.2221 | 0.4486 | 0.1999 |
| 0.2443 | 26.0 | 1560 | 0.1885 | 0.3908 | 0.1658 |
| 0.244 | 27.0 | 1620 | 0.1756 | 0.3826 | 0.1667 |
| 0.2252 | 28.0 | 1680 | 0.1531 | 0.3551 | 0.1528 |
| 0.1783 | 29.0 | 1740 | 0.1400 | 0.3480 | 0.1535 |
| 0.187 | 30.0 | 1800 | 0.1346 | 0.3402 | 0.1540 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "rinna/japanese-hubert-base", "model-index": [{"name": "hubert_2", "results": []}]} | automatic-speech-recognition | tndklab/hubert_2 | [
"transformers",
"safetensors",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:rinna/japanese-hubert-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-07T22:51:48+00:00 | [] | [] | TAGS
#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-base #license-apache-2.0 #endpoints_compatible #region-us
| hubert\_2
=========
This model is a fine-tuned version of rinna/japanese-hubert-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1346
* Wer: 0.3402
* Cer: 0.1540
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: 0.00045
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00045\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00045\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
66,
116,
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"passage: TAGS\n#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-base #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: 0.00045\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
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null | null | null |
# CharGen v2
## Live version
https://chargen.kubes-lab.com
## Overview
This repository contains GGUF quants of [CharGen v2](https://huggingface.co/kubernetes-bad/chargen-v2) model. Please see [original repository](https://huggingface.co/kubernetes-bad/chargen-v2) for proper full model card with prompting format and other details.
CharGen is a model that helps you to write characters for role playing with.
It produces character description based on your input prompt, step-by-step, in a dialogue format.
Warning: this model was trained on some NSFW content, so it may produce NSFW characters.
CharGen v2 is a project of several months of work. It's trained on a custom non-synthetic dataset, manually curated by hand. Read below on how it came together. | {"license": "cc-by-nc-4.0"} | null | kubernetes-bad/CharGen-v2-GGUF | [
"gguf",
"license:cc-by-nc-4.0",
"region:us"
] | 2024-02-07T22:52:41+00:00 | [] | [] | TAGS
#gguf #license-cc-by-nc-4.0 #region-us
|
# CharGen v2
!CharGen v2
## Live version
URL
## Overview
This repository contains GGUF quants of CharGen v2 model. Please see original repository for proper full model card with prompting format and other details.
CharGen is a model that helps you to write characters for role playing with.
It produces character description based on your input prompt, step-by-step, in a dialogue format.
Warning: this model was trained on some NSFW content, so it may produce NSFW characters.
CharGen v2 is a project of several months of work. It's trained on a custom non-synthetic dataset, manually curated by hand. Read below on how it came together. | [
"# CharGen v2\n\n!CharGen v2",
"## Live version\n\nURL",
"## Overview\n\nThis repository contains GGUF quants of CharGen v2 model. Please see original repository for proper full model card with prompting format and other details.\n\nCharGen is a model that helps you to write characters for role playing with.\n\nIt produces character description based on your input prompt, step-by-step, in a dialogue format.\n\nWarning: this model was trained on some NSFW content, so it may produce NSFW characters.\n\nCharGen v2 is a project of several months of work. It's trained on a custom non-synthetic dataset, manually curated by hand. Read below on how it came together."
] | [
"TAGS\n#gguf #license-cc-by-nc-4.0 #region-us \n",
"# CharGen v2\n\n!CharGen v2",
"## Live version\n\nURL",
"## Overview\n\nThis repository contains GGUF quants of CharGen v2 model. Please see original repository for proper full model card with prompting format and other details.\n\nCharGen is a model that helps you to write characters for role playing with.\n\nIt produces character description based on your input prompt, step-by-step, in a dialogue format.\n\nWarning: this model was trained on some NSFW content, so it may produce NSFW characters.\n\nCharGen v2 is a project of several months of work. It's trained on a custom non-synthetic dataset, manually curated by hand. Read below on how it came together."
] | [
20,
10,
4,
144
] | [
"passage: TAGS\n#gguf #license-cc-by-nc-4.0 #region-us \n# CharGen v2\n\n!CharGen v2## Live version\n\nURL## Overview\n\nThis repository contains GGUF quants of CharGen v2 model. Please see original repository for proper full model card with prompting format and other details.\n\nCharGen is a model that helps you to write characters for role playing with.\n\nIt produces character description based on your input prompt, step-by-step, in a dialogue format.\n\nWarning: this model was trained on some NSFW content, so it may produce NSFW characters.\n\nCharGen v2 is a project of several months of work. It's trained on a custom non-synthetic dataset, manually curated by hand. Read below on how it came together."
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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. -->
# SMIDS_3x_beit_large_RMSProp_lr00001_fold3
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9533
- Accuracy: 0.9233
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2525 | 1.0 | 450 | 0.2872 | 0.9017 |
| 0.1086 | 2.0 | 900 | 0.3110 | 0.9267 |
| 0.0278 | 3.0 | 1350 | 0.3964 | 0.93 |
| 0.0541 | 4.0 | 1800 | 0.4686 | 0.93 |
| 0.0004 | 5.0 | 2250 | 0.6394 | 0.915 |
| 0.1023 | 6.0 | 2700 | 0.6975 | 0.8983 |
| 0.0137 | 7.0 | 3150 | 0.6089 | 0.9117 |
| 0.0022 | 8.0 | 3600 | 0.7210 | 0.9133 |
| 0.0001 | 9.0 | 4050 | 0.7248 | 0.9133 |
| 0.0038 | 10.0 | 4500 | 0.6888 | 0.91 |
| 0.0001 | 11.0 | 4950 | 0.7490 | 0.9167 |
| 0.0035 | 12.0 | 5400 | 0.8593 | 0.9 |
| 0.0179 | 13.0 | 5850 | 0.7739 | 0.9067 |
| 0.005 | 14.0 | 6300 | 0.7562 | 0.9117 |
| 0.0 | 15.0 | 6750 | 0.6997 | 0.9317 |
| 0.0001 | 16.0 | 7200 | 0.5965 | 0.925 |
| 0.0012 | 17.0 | 7650 | 0.6911 | 0.925 |
| 0.0074 | 18.0 | 8100 | 0.8634 | 0.91 |
| 0.0 | 19.0 | 8550 | 0.8063 | 0.9267 |
| 0.0266 | 20.0 | 9000 | 0.7583 | 0.93 |
| 0.0 | 21.0 | 9450 | 0.7765 | 0.93 |
| 0.0 | 22.0 | 9900 | 0.7439 | 0.93 |
| 0.0 | 23.0 | 10350 | 0.6526 | 0.935 |
| 0.0 | 24.0 | 10800 | 0.9448 | 0.9183 |
| 0.0 | 25.0 | 11250 | 0.8450 | 0.9267 |
| 0.0 | 26.0 | 11700 | 0.8371 | 0.9183 |
| 0.0 | 27.0 | 12150 | 0.8002 | 0.92 |
| 0.0 | 28.0 | 12600 | 0.9266 | 0.9233 |
| 0.0 | 29.0 | 13050 | 0.8112 | 0.9267 |
| 0.0 | 30.0 | 13500 | 0.8040 | 0.9267 |
| 0.0 | 31.0 | 13950 | 0.8574 | 0.9317 |
| 0.0 | 32.0 | 14400 | 0.9024 | 0.9167 |
| 0.0 | 33.0 | 14850 | 0.9218 | 0.9167 |
| 0.0 | 34.0 | 15300 | 0.9073 | 0.9167 |
| 0.0 | 35.0 | 15750 | 0.9218 | 0.925 |
| 0.0 | 36.0 | 16200 | 0.8685 | 0.925 |
| 0.0 | 37.0 | 16650 | 0.9477 | 0.9217 |
| 0.0 | 38.0 | 17100 | 0.8189 | 0.925 |
| 0.0 | 39.0 | 17550 | 0.8582 | 0.9267 |
| 0.0 | 40.0 | 18000 | 0.9125 | 0.9233 |
| 0.0 | 41.0 | 18450 | 1.0271 | 0.9167 |
| 0.0 | 42.0 | 18900 | 0.9926 | 0.92 |
| 0.0 | 43.0 | 19350 | 1.0122 | 0.9167 |
| 0.0 | 44.0 | 19800 | 0.9838 | 0.925 |
| 0.0 | 45.0 | 20250 | 1.0945 | 0.91 |
| 0.0 | 46.0 | 20700 | 1.0282 | 0.9167 |
| 0.0 | 47.0 | 21150 | 1.0077 | 0.92 |
| 0.0 | 48.0 | 21600 | 0.9532 | 0.9233 |
| 0.0 | 49.0 | 22050 | 0.9527 | 0.9233 |
| 0.0 | 50.0 | 22500 | 0.9533 | 0.9233 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_RMSProp_lr00001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9233333333333333, "name": "Accuracy"}]}]}]} | image-classification | onizukal/SMIDS_3x_beit_large_RMSProp_lr00001_fold3 | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T22:54:53+00:00 | [] | [] | TAGS
#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| SMIDS\_3x\_beit\_large\_RMSProp\_lr00001\_fold3
===============================================
This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9533
* Accuracy: 0.9233
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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 50
### Training results
### Framework versions
* Transformers 4.32.1
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.2
| [
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"### Training results",
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] | [
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"passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #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: 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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
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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. -->
# detr-resnet-50_finetuned_highsub
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- 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": "facebook/detr-resnet-50", "model-index": [{"name": "detr-resnet-50_finetuned_highsub", "results": []}]} | object-detection | alexthomas4/detr-resnet-50_finetuned_highsub | [
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-07T23:07:06+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us
|
# detr-resnet-50_finetuned_highsub
This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- 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
| [
"# detr-resnet-50_finetuned_highsub\n\nThis model is a fine-tuned version of facebook/detr-resnet-50 on an unknown 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: 1e-05\n- train_batch_size: 4\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: 10\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 #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# detr-resnet-50_finetuned_highsub\n\nThis model is a fine-tuned version of facebook/detr-resnet-50 on an unknown 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: 1e-05\n- train_batch_size: 4\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: 10\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"
] | [
62,
40,
6,
12,
8,
3,
103,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us \n# detr-resnet-50_finetuned_highsub\n\nThis model is a fine-tuned version of facebook/detr-resnet-50 on an unknown 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: 1e-05\n- train_batch_size: 4\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: 10\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 | timm | # Model card for poolformerv2_s12.st_safebooru_1k
## Model Details
- **metrics:**
|Precision|Recall|F1-score|
|-|-|-|
|0.7908013266056414|0.42004463885987053|0.525185181114124|
| {"license": "apache-2.0", "library_name": "timm", "tags": ["image-classification", "timm"]} | image-classification | STomoya/poolformerv2_s12.st_safebooru_1k | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"license:apache-2.0",
"region:us"
] | 2024-02-07T23:15:24+00:00 | [] | [] | TAGS
#timm #pytorch #safetensors #image-classification #license-apache-2.0 #region-us
| # Model card for poolformerv2_s12.st_safebooru_1k
## Model Details
- metrics:
|Precision|Recall|F1-score|
|-|-|-|
|0.7908013266056414|0.42004463885987053|0.525185181114124|
| [
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"## Model Details\n- metrics: \n|Precision|Recall|F1-score|\n|-|-|-|\n|0.7908013266056414|0.42004463885987053|0.525185181114124|"
] | [
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"# Model card for poolformerv2_s12.st_safebooru_1k",
"## Model Details\n- metrics: \n|Precision|Recall|F1-score|\n|-|-|-|\n|0.7908013266056414|0.42004463885987053|0.525185181114124|"
] | [
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"passage: TAGS\n#timm #pytorch #safetensors #image-classification #license-apache-2.0 #region-us \n# Model card for poolformerv2_s12.st_safebooru_1k## Model Details\n- metrics: \n|Precision|Recall|F1-score|\n|-|-|-|\n|0.7908013266056414|0.42004463885987053|0.525185181114124|"
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] |
null | null | gguf | GGUF importance matrix (imatrix) quants for https://huggingface.co/ShinojiResearch/Senku-70B-Full
| Layers | Context | Template |
| --- | --- | --- |
| <pre>80</pre> | <pre>32764</pre> | <pre><\|im_start\|>system<br>{instructions}<\|im_end\|><br><\|im_start\|>user<br>{prompt}<\|im_end\|><br><\|im_start\|>assistant<br>{response}</pre> | | {"license": "cc-by-2.0", "library_name": "gguf", "pipeline_tag": "text-generation"} | text-generation | dranger003/Senku-70B-iMat.GGUF | [
"gguf",
"text-generation",
"license:cc-by-2.0",
"region:us"
] | 2024-02-07T23:17:59+00:00 | [] | [] | TAGS
#gguf #text-generation #license-cc-by-2.0 #region-us
| GGUF importance matrix (imatrix) quants for URL
Layers:
```
80
```
, Context:
```
32764
```
, Template:
```
<|im_start|>system
{instructions}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
{response}
```
| [] | [
"TAGS\n#gguf #text-generation #license-cc-by-2.0 #region-us \n"
] | [
23
] | [
"passage: TAGS\n#gguf #text-generation #license-cc-by-2.0 #region-us \n"
] | [
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null | null | transformers |
# AraDPR: Arabic Dense Passage Retrieval Model
AraDPR is a state-of-the-art dense passage retrieval model specifically designed for the Arabic language. It leverages deep learning techniques to encode passages and questions into dense vectors, facilitating efficient and accurate retrieval for question-answering systems.
## Model Details
### Model Description
- **Developed by:**
- **Model type:** Dense Passage Retrieval (DPR)
- **Language(s) (NLP):** Arabic
- **License:** MIT
- **Finetuned from:** AraBERT
### Model Sources
- **Repository:** https://github.com/DataScienceUIBK/ArabicaQA
- **Paper:** will be available soon
- **Demo:** will be available soon
## Uses
### Direct Use
AraDPR is designed for use in Arabic question-answering systems, enabling these systems to retrieve the most relevant passages from a large corpus efficiently.
### Downstream Use
Beyond question answering, AraDPR can be integrated into various NLP applications requiring passage retrieval, such as document summarization, information extraction, and more.
### Out-of-Scope Use
AraDPR is not intended for languages other than Arabic or for tasks that do not involve passage retrieval.
## Bias, Risks, and Limitations
While AraDPR represents a significant advancement in Arabic NLP, users should be aware of the model's limitations, particularly in handling dialects or very domain-specific texts. Further research and development are encouraged to address these challenges.
## How to Get Started with the Model
To get started with AraDPR, you can use the following code snippet:
Please check out our github page: https://github.com/DataScienceUIBK/ArabicaQA
## Training Details
AraDPR was trained on a diverse corpus from Arabic Wikipedia, covering a wide range of topics to ensure comprehensive language representation.
## Results
AraDPR demonstrates superior performance over traditional retrieval methods, significantly improving the efficiency and accuracy of question answering in Arabic.
## Technical Specifications
Model Architecture and Objective
AraDPR utilizes a dual-encoder architecture, with separate encoders for questions and passages. The model is optimized to project semantically related questions and passages closer in the vector space. | {"language": ["ar"], "license": "mit", "library_name": "transformers", "datasets": ["abdoelsayed/Open-ArabicaQA", "abdoelsayed/ArabicaQA"], "metrics": ["accuracy"], "pipeline_tag": "question-answering"} | question-answering | abdoelsayed/AraDPR_index | [
"transformers",
"question-answering",
"ar",
"dataset:abdoelsayed/Open-ArabicaQA",
"dataset:abdoelsayed/ArabicaQA",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-07T23:18:27+00:00 | [] | [
"ar"
] | TAGS
#transformers #question-answering #ar #dataset-abdoelsayed/Open-ArabicaQA #dataset-abdoelsayed/ArabicaQA #license-mit #endpoints_compatible #region-us
|
# AraDPR: Arabic Dense Passage Retrieval Model
AraDPR is a state-of-the-art dense passage retrieval model specifically designed for the Arabic language. It leverages deep learning techniques to encode passages and questions into dense vectors, facilitating efficient and accurate retrieval for question-answering systems.
## Model Details
### Model Description
- Developed by:
- Model type: Dense Passage Retrieval (DPR)
- Language(s) (NLP): Arabic
- License: MIT
- Finetuned from: AraBERT
### Model Sources
- Repository: URL
- Paper: will be available soon
- Demo: will be available soon
## Uses
### Direct Use
AraDPR is designed for use in Arabic question-answering systems, enabling these systems to retrieve the most relevant passages from a large corpus efficiently.
### Downstream Use
Beyond question answering, AraDPR can be integrated into various NLP applications requiring passage retrieval, such as document summarization, information extraction, and more.
### Out-of-Scope Use
AraDPR is not intended for languages other than Arabic or for tasks that do not involve passage retrieval.
## Bias, Risks, and Limitations
While AraDPR represents a significant advancement in Arabic NLP, users should be aware of the model's limitations, particularly in handling dialects or very domain-specific texts. Further research and development are encouraged to address these challenges.
## How to Get Started with the Model
To get started with AraDPR, you can use the following code snippet:
Please check out our github page: URL
## Training Details
AraDPR was trained on a diverse corpus from Arabic Wikipedia, covering a wide range of topics to ensure comprehensive language representation.
## Results
AraDPR demonstrates superior performance over traditional retrieval methods, significantly improving the efficiency and accuracy of question answering in Arabic.
## Technical Specifications
Model Architecture and Objective
AraDPR utilizes a dual-encoder architecture, with separate encoders for questions and passages. The model is optimized to project semantically related questions and passages closer in the vector space. | [
"# AraDPR: Arabic Dense Passage Retrieval Model\n\nAraDPR is a state-of-the-art dense passage retrieval model specifically designed for the Arabic language. It leverages deep learning techniques to encode passages and questions into dense vectors, facilitating efficient and accurate retrieval for question-answering systems.",
"## Model Details",
"### Model Description\n\n- Developed by: \n- Model type: Dense Passage Retrieval (DPR)\n- Language(s) (NLP): Arabic\n- License: MIT\n- Finetuned from: AraBERT",
"### Model Sources\n\n- Repository: URL\n- Paper: will be available soon\n- Demo: will be available soon",
"## Uses",
"### Direct Use\n\nAraDPR is designed for use in Arabic question-answering systems, enabling these systems to retrieve the most relevant passages from a large corpus efficiently.",
"### Downstream Use\n\nBeyond question answering, AraDPR can be integrated into various NLP applications requiring passage retrieval, such as document summarization, information extraction, and more.",
"### Out-of-Scope Use\n\nAraDPR is not intended for languages other than Arabic or for tasks that do not involve passage retrieval.",
"## Bias, Risks, and Limitations\n\nWhile AraDPR represents a significant advancement in Arabic NLP, users should be aware of the model's limitations, particularly in handling dialects or very domain-specific texts. Further research and development are encouraged to address these challenges.",
"## How to Get Started with the Model\n\nTo get started with AraDPR, you can use the following code snippet:\n\nPlease check out our github page: URL",
"## Training Details\nAraDPR was trained on a diverse corpus from Arabic Wikipedia, covering a wide range of topics to ensure comprehensive language representation.",
"## Results\nAraDPR demonstrates superior performance over traditional retrieval methods, significantly improving the efficiency and accuracy of question answering in Arabic.",
"## Technical Specifications\nModel Architecture and Objective\nAraDPR utilizes a dual-encoder architecture, with separate encoders for questions and passages. The model is optimized to project semantically related questions and passages closer in the vector space."
] | [
"TAGS\n#transformers #question-answering #ar #dataset-abdoelsayed/Open-ArabicaQA #dataset-abdoelsayed/ArabicaQA #license-mit #endpoints_compatible #region-us \n",
"# AraDPR: Arabic Dense Passage Retrieval Model\n\nAraDPR is a state-of-the-art dense passage retrieval model specifically designed for the Arabic language. It leverages deep learning techniques to encode passages and questions into dense vectors, facilitating efficient and accurate retrieval for question-answering systems.",
"## Model Details",
"### Model Description\n\n- Developed by: \n- Model type: Dense Passage Retrieval (DPR)\n- Language(s) (NLP): Arabic\n- License: MIT\n- Finetuned from: AraBERT",
"### Model Sources\n\n- Repository: URL\n- Paper: will be available soon\n- Demo: will be available soon",
"## Uses",
"### Direct Use\n\nAraDPR is designed for use in Arabic question-answering systems, enabling these systems to retrieve the most relevant passages from a large corpus efficiently.",
"### Downstream Use\n\nBeyond question answering, AraDPR can be integrated into various NLP applications requiring passage retrieval, such as document summarization, information extraction, and more.",
"### Out-of-Scope Use\n\nAraDPR is not intended for languages other than Arabic or for tasks that do not involve passage retrieval.",
"## Bias, Risks, and Limitations\n\nWhile AraDPR represents a significant advancement in Arabic NLP, users should be aware of the model's limitations, particularly in handling dialects or very domain-specific texts. Further research and development are encouraged to address these challenges.",
"## How to Get Started with the Model\n\nTo get started with AraDPR, you can use the following code snippet:\n\nPlease check out our github page: URL",
"## Training Details\nAraDPR was trained on a diverse corpus from Arabic Wikipedia, covering a wide range of topics to ensure comprehensive language representation.",
"## Results\nAraDPR demonstrates superior performance over traditional retrieval methods, significantly improving the efficiency and accuracy of question answering in Arabic.",
"## Technical Specifications\nModel Architecture and Objective\nAraDPR utilizes a dual-encoder architecture, with separate encoders for questions and passages. The model is optimized to project semantically related questions and passages closer in the vector space."
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"passage: TAGS\n#transformers #question-answering #ar #dataset-abdoelsayed/Open-ArabicaQA #dataset-abdoelsayed/ArabicaQA #license-mit #endpoints_compatible #region-us \n# AraDPR: Arabic Dense Passage Retrieval Model\n\nAraDPR is a state-of-the-art dense passage retrieval model specifically designed for the Arabic language. It leverages deep learning techniques to encode passages and questions into dense vectors, facilitating efficient and accurate retrieval for question-answering systems.## Model Details### Model Description\n\n- Developed by: \n- Model type: Dense Passage Retrieval (DPR)\n- Language(s) (NLP): Arabic\n- License: MIT\n- Finetuned from: AraBERT### Model Sources\n\n- Repository: URL\n- Paper: will be available soon\n- Demo: will be available soon## Uses### Direct Use\n\nAraDPR is designed for use in Arabic question-answering systems, enabling these systems to retrieve the most relevant passages from a large corpus efficiently.### Downstream Use\n\nBeyond question answering, AraDPR can be integrated into various NLP applications requiring passage retrieval, such as document summarization, information extraction, and more.### Out-of-Scope Use\n\nAraDPR is not intended for languages other than Arabic or for tasks that do not involve passage retrieval.## Bias, Risks, and Limitations\n\nWhile AraDPR represents a significant advancement in Arabic NLP, users should be aware of the model's limitations, particularly in handling dialects or very domain-specific texts. Further research and development are encouraged to address these challenges.## How to Get Started with the Model\n\nTo get started with AraDPR, you can use the following code snippet:\n\nPlease check out our github page: URL## Training Details\nAraDPR was trained on a diverse corpus from Arabic Wikipedia, covering a wide range of topics to ensure comprehensive language representation.## Results\nAraDPR demonstrates superior performance over traditional retrieval methods, significantly improving the efficiency and accuracy of question answering in Arabic."
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null | null | null | This file was Originally created by:
sail-rvc/Ronnie_James_Dio
https://huggingface.co/sail-rvc/Ronnie_James_Dio/tree/main
It didn't have a zip file containing the model.zip file, so I made one out of the .model.index and model.pth, that was made by sail-rvc.
| {"license": "openrail"} | null | DrSeedr/sail-rvc_RJD | [
"license:openrail",
"region:us"
] | 2024-02-07T23:18:50+00:00 | [] | [] | TAGS
#license-openrail #region-us
| This file was Originally created by:
sail-rvc/Ronnie_James_Dio
URL
It didn't have a zip file containing the URL file, so I made one out of the .URL and URL, that was made by sail-rvc.
| [] | [
"TAGS\n#license-openrail #region-us \n"
] | [
12
] | [
"passage: TAGS\n#license-openrail #region-us \n"
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null | null | diffusers | # lola-gunvolt
<Gallery />
## Trigger words
You should use `lola` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/joislosinghermind/lola-gunvolt/tree/main) them in the Files & versions tab.
| {"license": "unlicense", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "UNICODE\u0000\u00002\u0000d\u0000,\u0000 \u0000m\u0000a\u0000s\u0000t\u0000e\u0000r\u0000p\u0000i\u0000e\u0000c\u0000e\u0000,\u0000 \u0000b\u0000e\u0000s\u0000t\u0000 \u0000q\u0000u\u0000a\u0000l\u0000i\u0000t\u0000y\u0000,\u0000 \u0000a\u0000n\u0000i\u0000m\u0000e\u0000,\u0000 \u0000h\u0000i\u0000g\u0000h\u0000l\u0000y\u0000 \u0000d\u0000e\u0000t\u0000a\u0000i\u0000l\u0000e\u0000d\u0000 \u0000f\u0000a\u0000c\u0000e\u0000,\u0000 \u0000h\u0000i\u0000g\u0000h\u0000l\u0000y\u0000 \u0000d\u0000e\u0000t\u0000a\u0000i\u0000l\u0000e\u0000d\u0000 \u0000b\u0000a\u0000c\u0000k\u0000g\u0000r\u0000o\u0000u\u0000n\u0000d\u0000,\u0000 \u0000p\u0000e\u0000r\u0000f\u0000e\u0000c\u0000t\u0000 \u0000l\u0000i\u0000g\u0000h\u0000t\u0000i\u0000n\u0000g\u0000,\u0000 \u0000l\u0000o\u0000l\u0000a\u0000,\u0000 \u0000b\u0000l\u0000u\u0000e\u0000 \u0000e\u0000y\u0000e\u0000s\u0000,\u0000 \u0000g\u0000r\u0000e\u0000e\u0000n\u0000_\u0000h\u0000a\u0000i\u0000r\u0000,\u0000 \u0000c\u0000i\u0000t\u0000y\u0000s\u0000c\u0000a\u0000p\u0000e\u0000,\u0000 \u0000f\u0000u\u0000l\u0000l\u0000_\u0000b\u0000o\u0000d\u0000y\u0000,\u0000 \u0000s\u0000o\u0000l\u0000o\u0000,\u0000 \u0000s\u0000o\u0000l\u0000o\u0000 \u0000f\u0000o\u0000c\u0000u\u0000s\u0000,\u0000 \u0000t\u0000-\u0000s\u0000h\u0000i\u0000r\u0000t\u0000,\u0000 \u0000 \u0000s\u0000h\u0000o\u0000r\u0000t\u0000s\u0000,\u0000 \u0000<\u0000l\u0000o\u0000r\u0000a\u0000:\u0000l\u0000o\u0000l\u0000a\u0000:\u00001\u0000>\u0000", "output": {"url": "images/00492-abyssorangemix3AOM3_aom3a1b_3939236143.jpeg"}}], "base_model": "runwayml/stable-diffusion-v1-5", "instance_prompt": "lola"} | text-to-image | joislosinghermind/lola-gunvolt | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:runwayml/stable-diffusion-v1-5",
"license:unlicense",
"region:us"
] | 2024-02-07T23:20:12+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-runwayml/stable-diffusion-v1-5 #license-unlicense #region-us
| # lola-gunvolt
<Gallery />
## Trigger words
You should use 'lola' to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"# lola-gunvolt\n\n<Gallery />",
"## Trigger words\n\nYou should use 'lola' 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-runwayml/stable-diffusion-v1-5 #license-unlicense #region-us \n",
"# lola-gunvolt\n\n<Gallery />",
"## Trigger words\n\nYou should use 'lola' 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."
] | [
61,
11,
17,
28
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-runwayml/stable-diffusion-v1-5 #license-unlicense #region-us \n# lola-gunvolt\n\n<Gallery />## Trigger words\n\nYou should use 'lola' 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 | peft |
# SpecCoder 6.7bn v1
This model is a fine-tuned version of [Deep Seek Coder 6.7b Instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) on a synthetic dataset of ~26k solidity smart contracts.
It achieves the following results on the evaluation set:
- Loss: 0.4782
## Model Description
The model was fine-tune through the LoRA framework.
## Intended uses & limitations
To generate solidity smart contracts.
## Training and evaluation data
5% hold out validation data.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.502 | 1.0 | 390 | 0.4801 |
| 0.5274 | 2.0 | 780 | 0.4782 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"language": ["en"], "license": "other", "library_name": "peft", "base_model": "deepseek-ai/deepseek-coder-6.7b-instruct", "pipeline_tag": "text-generation", "model-index": [{"name": "output-6.7b-26k-lora", "results": []}]} | text-generation | asadmasad/deepseek-6-7bn-lora-finetuning-26k | [
"peft",
"safetensors",
"text-generation",
"conversational",
"en",
"base_model:deepseek-ai/deepseek-coder-6.7b-instruct",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-07T23:22:38+00:00 | [] | [
"en"
] | TAGS
#peft #safetensors #text-generation #conversational #en #base_model-deepseek-ai/deepseek-coder-6.7b-instruct #license-other #endpoints_compatible #region-us
| SpecCoder 6.7bn v1
==================
This model is a fine-tuned version of Deep Seek Coder 6.7b Instruct on a synthetic dataset of ~26k solidity smart contracts.
It achieves the following results on the evaluation set:
* Loss: 0.4782
Model Description
-----------------
The model was fine-tune through the LoRA framework.
Intended uses & limitations
---------------------------
To generate solidity smart contracts.
Training and evaluation data
----------------------------
5% hold out validation data.
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 10
* num\_epochs: 2
### Training results
### Framework versions
* PEFT 0.8.2
* Transformers 4.37.2
* Pytorch 2.2.0+cu121
* Datasets 2.16.1
* 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: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#peft #safetensors #text-generation #conversational #en #base_model-deepseek-ai/deepseek-coder-6.7b-instruct #license-other #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: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
61,
145,
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39
] | [
"passage: TAGS\n#peft #safetensors #text-generation #conversational #en #base_model-deepseek-ai/deepseek-coder-6.7b-instruct #license-other #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: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1466 with parameters:
```
{'batch_size': 2, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 293,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
(2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | jost/distiluse-base-multilingual-cased-v2-de-politics | [
"sentence-transformers",
"safetensors",
"distilbert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | 2024-02-07T23:24:33+00:00 | [] | [] | TAGS
#sentence-transformers #safetensors #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 1466 with parameters:
Loss:
'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
| [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 1466 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #safetensors #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 1466 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
42,
49,
38,
29,
86,
5,
6
] | [
"passage: TAGS\n#sentence-transformers #safetensors #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 1466 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors"
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null | null | transformers |
# Uploaded model
- **Developed by:** smotoc
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "gguf"], "base_model": "unsloth/mistral-7b-bnb-4bit"} | text-generation | smotoc/foxy_mistral7B_unsloth | [
"transformers",
"pytorch",
"gguf",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T23:24:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gguf #mistral #text-generation #text-generation-inference #unsloth #en #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: smotoc
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: smotoc\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #pytorch #gguf #mistral #text-generation #text-generation-inference #unsloth #en #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: smotoc\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
81,
79
] | [
"passage: TAGS\n#transformers #pytorch #gguf #mistral #text-generation #text-generation-inference #unsloth #en #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Uploaded model\n\n- Developed by: smotoc\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
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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. -->
# complaints_classifier
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: 0.2139
- Accuracy: 0.9420
## 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 | 77 | 0.3395 | 0.9130 |
| No log | 2.0 | 154 | 0.2139 | 0.9420 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "complaints_classifier", "results": []}]} | text-classification | jpsteinhafel/complaints_classifier | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T23:28:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| complaints\_classifier
======================
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: 0.2139
* Accuracy: 0.9420
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 4.28.0
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* 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 2.17.0\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: 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 4.28.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.13.3"
] | [
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"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #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 4.28.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.13.3"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | zheng438/TinyLlama-1.1B-fine-tuned-predict | [
"transformers",
"safetensors",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #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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#### Speeds, Sizes, Times [optional]
## Evaluation
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#### Factors
#### Metrics
### Results
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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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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. -->
# experiments
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0912
## 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: 8
- seed: 123
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1412 | 0.2 | 311 | 0.1461 |
| 0.1095 | 0.4 | 622 | 0.1154 |
| 0.089 | 0.6 | 933 | 0.1029 |
| 0.0875 | 0.8 | 1244 | 0.0912 |
### Framework versions
- PEFT 0.8.1
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "model-index": [{"name": "experiments", "results": []}]} | null | zheng438/experiments | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
"license:apache-2.0",
"region:us"
] | 2024-02-07T23:30:01+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T #license-apache-2.0 #region-us
| experiments
===========
This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0912
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: 8
* seed: 123
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 1
### Training results
### Framework versions
* PEFT 0.8.1
* Transformers 4.37.2
* Pytorch 2.2.0
* Datasets 2.16.1
* 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: 8\n* seed: 123\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: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.1\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T #license-apache-2.0 #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: 8\n* seed: 123\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: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.1\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
67,
143,
4,
36
] | [
"passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T #license-apache-2.0 #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: 8\n* seed: 123\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: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* PEFT 0.8.1\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | null |
# Lora of lynette/リネット/琳妮特 (Genshin Impact)
## What Is This?
This is the LoRA model of waifu lynette/リネット/琳妮特 (Genshin Impact).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/lynette_genshin](https://huggingface.co/datasets/CyberHarem/lynette_genshin), which contains 1292 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 1, resolution is 720x720, clustering into 20 buckets.
* Trained for 10000 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `lynette_genshin`.**
* Pruned core tags for this waifu are `animal_ears, purple_eyes, cat_ears, bangs, animal_ear_fluff, bow, long_hair, tail, breasts, cat_tail, cat_girl, grey_hair, facial_mark, braid`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 6500, you need to download [`6500/lynette_genshin.pt`](https://huggingface.co/CyberHarem/lynette_genshin/resolve/main/6500/lynette_genshin.pt) as the embedding and [`6500/lynette_genshin.safetensors`](https://huggingface.co/CyberHarem/lynette_genshin/resolve/main/6500/lynette_genshin.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 6500.
1560 images (1.64 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0_0 | pattern_0_1 | pattern_1 | pattern_2 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:----------------------------------------------------------------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:--------------------------------------------|:--------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:--------------------------------|:------------------------------------|:--------------------------------|:----------------------------------|:----------------------------------------|:----------------------------------------|:----------------------------------------|:------------------------------|:----------------------------------|:----------------------------------|:--------------------------------|:------------------------------------------------|:----------------------------------|:----------------------------------|:------------------------------|:--------------------------------|:--------------------------------------|:--------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------|:--------------------------------------|
| 6500 | 21 | 0.979 | **0.956** | **0.852** | **0.703** | [Download](https://huggingface.co/CyberHarem/lynette_genshin/resolve/main/6500/lynette_genshin.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 8250 | 26 | 0.984 | 0.936 | 0.844 | 0.700 | [Download](https://huggingface.co/CyberHarem/lynette_genshin/resolve/main/8250/lynette_genshin.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 5250 | 17 | 0.985 | 0.952 | 0.842 | 0.700 | [Download](https://huggingface.co/CyberHarem/lynette_genshin/resolve/main/5250/lynette_genshin.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 8500 | 27 | **0.988** | 0.922 | 0.839 | 0.698 | [Download](https://huggingface.co/CyberHarem/lynette_genshin/resolve/main/8500/lynette_genshin.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 4500 | 14 | 0.986 | 0.947 | 0.840 | 0.697 | [Download](https://huggingface.co/CyberHarem/lynette_genshin/resolve/main/4500/lynette_genshin.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 7750 to 10000](all/0.md)
* [Steps From 5250 to 7500](all/1.md)
* [Steps From 2750 to 5000](all/2.md)
* [Steps From 250 to 2500](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/lynette_genshin"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/lynette_genshin | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/lynette_genshin",
"license:mit",
"region:us"
] | 2024-02-07T23:30:40+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/lynette_genshin #license-mit #region-us
| Lora of lynette/リネット/琳妮特 (Genshin Impact)
=========================================
What Is This?
-------------
This is the LoRA model of waifu lynette/リネット/琳妮特 (Genshin Impact).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/lynette\_genshin, which contains 1292 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 1, resolution is 720x720, clustering into 20 buckets.
* Trained for 10000 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'lynette\_genshin'.
* Pruned core tags for this waifu are 'animal\_ears, purple\_eyes, cat\_ears, bangs, animal\_ear\_fluff, bow, long\_hair, tail, breasts, cat\_tail, cat\_girl, grey\_hair, facial\_mark, braid'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 6500, you need to download '6500/lynette\_genshin.pt' as the embedding and '6500/lynette\_genshin.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 6500.
1560 images (1.64 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 7750 to 10000
* Steps From 5250 to 7500
* Steps From 2750 to 5000
* Steps From 250 to 2500
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 6500, you need to download '6500/lynette\\_genshin.pt' as the embedding and '6500/lynette\\_genshin.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 6500.\n\n\n1560 images (1.64 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 7750 to 10000\n* Steps From 5250 to 7500\n* Steps From 2750 to 5000\n* Steps From 250 to 2500"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/lynette_genshin #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 6500, you need to download '6500/lynette\\_genshin.pt' as the embedding and '6500/lynette\\_genshin.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 6500.\n\n\n1560 images (1.64 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 7750 to 10000\n* Steps From 5250 to 7500\n* Steps From 2750 to 5000\n* Steps From 250 to 2500"
] | [
43,
38,
470
] | [
"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/lynette_genshin #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
] | [
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null | null | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "pipeline_tag": "text-generation"} | text-generation | Arman123/zephyr-7b-beta-openassistant-guanaco3 | [
"transformers",
"safetensors",
"text-generation",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #text-generation #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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## 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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- 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:
- Hours used:
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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. -->
# wav2vec_RTSplit0208_9
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0362
- Wer: 0.2722
- Cer: 0.1577
## 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: 5.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 4.9778 | 1.0 | 120 | 4.2625 | 1.0 | 0.9235 |
| 1.1355 | 2.0 | 240 | 0.9053 | 0.8239 | 0.5936 |
| 0.7849 | 3.0 | 360 | 0.6386 | 0.8155 | 0.4961 |
| 0.6219 | 4.0 | 480 | 0.5450 | 0.7614 | 0.4040 |
| 0.541 | 5.0 | 600 | 0.4444 | 0.6779 | 0.3298 |
| 0.4449 | 6.0 | 720 | 0.3144 | 0.5568 | 0.2715 |
| 0.3471 | 7.0 | 840 | 0.2053 | 0.4438 | 0.2109 |
| 0.2668 | 8.0 | 960 | 0.1122 | 0.3389 | 0.1688 |
| 0.206 | 9.0 | 1080 | 0.0490 | 0.2938 | 0.1499 |
| 0.109 | 10.0 | 1200 | 0.0362 | 0.2722 | 0.1577 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "model-index": [{"name": "wav2vec_RTSplit0208_9", "results": []}]} | automatic-speech-recognition | tndklab/wav2vec_RTSplit0208_9 | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-07T23:36:00+00:00 | [] | [] | TAGS
#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec\_RTSplit0208\_9
=======================
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-japanese on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0362
* Wer: 0.2722
* Cer: 0.1577
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: 5.5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #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: 5.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
80,
116,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #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: 5.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
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null | null | transformers |
# phi-2-ko-v0.1
## Model Details
This model is a Korean-specific model trained in phi-2 by adding a Korean tokenizer and Korean data. (English is also available.)
Although phi-2 performs very well, it does not support the Korean language and does not have a tokenizer trained on Korean corpous, so tokenizing Korean text will use many times more tokens than English tokens.
To overcome these limitations, I trained the model using an open-license Korean corpus and some English corpus.
The reasons for using the English corpus together are as follows:
1. The goal is to preserve the excellent performance of the existing model by preventing catastrophic forgetting.
2. Mixing English and Korean prompts usually produces better results than using all prompts in Korean.
Since my role is not as a working developer, but as an solutions architect helping customers with quick PoCs/prototypes, and I was limited by AWS GPU resources available, I only trained with 5GB of data instead of hundreds of GB of massive data.
### Vocab Expansion
| Model Name | Vocabulary Size | Description |
| --- | --- | --- |
| Original phi-2 | 50,295 | BBPE (Byte-level BPE) |
| **phi-2-ko** | 66,676 | BBPE. Added Korean vocab and merges |
**Tokenizing "아마존 세이지메이커"**
| Model | # of tokens | Tokens |
| --- | --- | --- |
| Original phi-2 | 25 | `[168, 243, 226, 167, 100, 230, 168, 94, 112, 23821, 226, 116, 35975, 112, 168, 100, 222, 167, 102, 242, 35975, 112, 168, 119, 97]` |
| **phi-2-ko** |6| `[57974, 51299, 50617, 51005, 52027, 51446]` |
### Continued pre-training
The dataset used for training is as follows. To prevent catastrophic forgetting, I included some English corpus as training data.
- Wikipedia Korean dataset (https://huggingface.co/datasets/wikimedia/wikipedia)
- Massive Korean synthetic dataset (https://huggingface.co/datasets/maywell/korean_textbooks)
- Tiny code dataset (https://huggingface.co/datasets/nampdn-ai/tiny-codes)
- OpenOrca dataset (https://huggingface.co/datasets/Open-Orca/OpenOrca)
- Using some of the various sentences I wrote (personal blog, chat, etc.)
Note that performance is not guaranteed since only a small number of datasets were used for the experiment. The number of samples for training set is just around 5 million after tokenization.
For distributed training, all weights were trained without adapter techniques, and sharding parallelization was performed with ZeRO-2. The presets are as follows.
Since this is a model that has not been fine-tuned, it is recommended to perform fine tuning such as instruction tuning/alignment tuning according to your use case.
```json
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"bf16": {
"enabled": "auto"
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"betas": "auto",
"eps": "auto",
"weight_decay": "auto"
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
},
"zero_optimization": {
"stage": 2,
"allgather_partitions": true,
"allgather_bucket_size": 2e8,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 2e8,
"contiguous_gradients": true,
"cpu_offload": true
},
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto"
}
```
Some hyperparameters are listed below.
```
batch_size: 2
num_epochs: 1
learning_rate: 3e-4
gradient_accumulation_steps: 8
lr_scheduler_type: "linear"
group_by_length: False
```
## How to Get Started with the Model
```python
import torch
from transformers import PhiForCausalLM, AutoModelForCausalLM, AutoTokenizer
torch.set_default_device("cuda")
# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("daekeun-ml/phi-2-ko-v0.1", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/phi-2-ko-v0.1", trust_remote_code=True)
# Korean
inputs = tokenizer("머신러닝은 ", return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
# English
inputs = tokenizer('''def print_prime(n):
"""
Print all primes between 1 and n
"""''', return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
### References
- Base model: [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
## Notes
### License
cc-by-sa 3.0; The license of phi-2 is MIT, but I considered the licensing of the dataset used for training.
### Caution
This model was created as a personal experiment, unrelated to the organization I work for. The model may not operate correctly because separate verification was not performed. Please be careful unless it is for personal experimentation or PoC (Proof of Concept)! | {"language": ["ko", "en"], "license": "cc-by-sa-3.0", "library_name": "transformers", "datasets": ["wikimedia/wikipedia", "maywell/korean_textbooks", "nampdn-ai/tiny-codes", "Open-Orca/OpenOrca"], "inference": false} | text-generation | daekeun-ml/phi-2-ko-v0.1 | [
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"ko",
"en",
"dataset:wikimedia/wikipedia",
"dataset:maywell/korean_textbooks",
"dataset:nampdn-ai/tiny-codes",
"dataset:Open-Orca/OpenOrca",
"license:cc-by-sa-3.0",
"autotrain_compatible",
"region:us"
] | 2024-02-07T23:37:33+00:00 | [] | [
"ko",
"en"
] | TAGS
#transformers #safetensors #phi #text-generation #custom_code #ko #en #dataset-wikimedia/wikipedia #dataset-maywell/korean_textbooks #dataset-nampdn-ai/tiny-codes #dataset-Open-Orca/OpenOrca #license-cc-by-sa-3.0 #autotrain_compatible #region-us
| phi-2-ko-v0.1
=============
Model Details
-------------
This model is a Korean-specific model trained in phi-2 by adding a Korean tokenizer and Korean data. (English is also available.)
Although phi-2 performs very well, it does not support the Korean language and does not have a tokenizer trained on Korean corpous, so tokenizing Korean text will use many times more tokens than English tokens.
To overcome these limitations, I trained the model using an open-license Korean corpus and some English corpus.
The reasons for using the English corpus together are as follows:
1. The goal is to preserve the excellent performance of the existing model by preventing catastrophic forgetting.
2. Mixing English and Korean prompts usually produces better results than using all prompts in Korean.
Since my role is not as a working developer, but as an solutions architect helping customers with quick PoCs/prototypes, and I was limited by AWS GPU resources available, I only trained with 5GB of data instead of hundreds of GB of massive data.
### Vocab Expansion
Model Name: Original phi-2, Vocabulary Size: 50,295, Description: BBPE (Byte-level BPE)
Model Name: phi-2-ko, Vocabulary Size: 66,676, Description: BBPE. Added Korean vocab and merges
Tokenizing "아마존 세이지메이커"
Model: Original phi-2, # of tokens: 25, Tokens: '[168, 243, 226, 167, 100, 230, 168, 94, 112, 23821, 226, 116, 35975, 112, 168, 100, 222, 167, 102, 242, 35975, 112, 168, 119, 97]'
Model: phi-2-ko, # of tokens: 6, Tokens: '[57974, 51299, 50617, 51005, 52027, 51446]'
### Continued pre-training
The dataset used for training is as follows. To prevent catastrophic forgetting, I included some English corpus as training data.
* Wikipedia Korean dataset (URL
* Massive Korean synthetic dataset (URL
* Tiny code dataset (URL
* OpenOrca dataset (URL
* Using some of the various sentences I wrote (personal blog, chat, etc.)
Note that performance is not guaranteed since only a small number of datasets were used for the experiment. The number of samples for training set is just around 5 million after tokenization.
For distributed training, all weights were trained without adapter techniques, and sharding parallelization was performed with ZeRO-2. The presets are as follows.
Since this is a model that has not been fine-tuned, it is recommended to perform fine tuning such as instruction tuning/alignment tuning according to your use case.
Some hyperparameters are listed below.
How to Get Started with the Model
---------------------------------
### References
* Base model: microsoft/phi-2
Notes
-----
### License
cc-by-sa 3.0; The license of phi-2 is MIT, but I considered the licensing of the dataset used for training.
### Caution
This model was created as a personal experiment, unrelated to the organization I work for. The model may not operate correctly because separate verification was not performed. Please be careful unless it is for personal experimentation or PoC (Proof of Concept)!
| [
"### Vocab Expansion\n\n\nModel Name: Original phi-2, Vocabulary Size: 50,295, Description: BBPE (Byte-level BPE)\nModel Name: phi-2-ko, Vocabulary Size: 66,676, Description: BBPE. Added Korean vocab and merges\n\n\nTokenizing \"아마존 세이지메이커\"\n\n\nModel: Original phi-2, # of tokens: 25, Tokens: '[168, 243, 226, 167, 100, 230, 168, 94, 112, 23821, 226, 116, 35975, 112, 168, 100, 222, 167, 102, 242, 35975, 112, 168, 119, 97]'\nModel: phi-2-ko, # of tokens: 6, Tokens: '[57974, 51299, 50617, 51005, 52027, 51446]'",
"### Continued pre-training\n\n\nThe dataset used for training is as follows. To prevent catastrophic forgetting, I included some English corpus as training data.\n\n\n* Wikipedia Korean dataset (URL\n* Massive Korean synthetic dataset (URL\n* Tiny code dataset (URL\n* OpenOrca dataset (URL\n* Using some of the various sentences I wrote (personal blog, chat, etc.)\n\n\nNote that performance is not guaranteed since only a small number of datasets were used for the experiment. The number of samples for training set is just around 5 million after tokenization.\nFor distributed training, all weights were trained without adapter techniques, and sharding parallelization was performed with ZeRO-2. The presets are as follows.\n\n\nSince this is a model that has not been fine-tuned, it is recommended to perform fine tuning such as instruction tuning/alignment tuning according to your use case.\n\n\nSome hyperparameters are listed below.\n\n\nHow to Get Started with the Model\n---------------------------------",
"### References\n\n\n* Base model: microsoft/phi-2\n\n\nNotes\n-----",
"### License\n\n\ncc-by-sa 3.0; The license of phi-2 is MIT, but I considered the licensing of the dataset used for training.",
"### Caution\n\n\nThis model was created as a personal experiment, unrelated to the organization I work for. The model may not operate correctly because separate verification was not performed. Please be careful unless it is for personal experimentation or PoC (Proof of Concept)!"
] | [
"TAGS\n#transformers #safetensors #phi #text-generation #custom_code #ko #en #dataset-wikimedia/wikipedia #dataset-maywell/korean_textbooks #dataset-nampdn-ai/tiny-codes #dataset-Open-Orca/OpenOrca #license-cc-by-sa-3.0 #autotrain_compatible #region-us \n",
"### Vocab Expansion\n\n\nModel Name: Original phi-2, Vocabulary Size: 50,295, Description: BBPE (Byte-level BPE)\nModel Name: phi-2-ko, Vocabulary Size: 66,676, Description: BBPE. Added Korean vocab and merges\n\n\nTokenizing \"아마존 세이지메이커\"\n\n\nModel: Original phi-2, # of tokens: 25, Tokens: '[168, 243, 226, 167, 100, 230, 168, 94, 112, 23821, 226, 116, 35975, 112, 168, 100, 222, 167, 102, 242, 35975, 112, 168, 119, 97]'\nModel: phi-2-ko, # of tokens: 6, Tokens: '[57974, 51299, 50617, 51005, 52027, 51446]'",
"### Continued pre-training\n\n\nThe dataset used for training is as follows. To prevent catastrophic forgetting, I included some English corpus as training data.\n\n\n* Wikipedia Korean dataset (URL\n* Massive Korean synthetic dataset (URL\n* Tiny code dataset (URL\n* OpenOrca dataset (URL\n* Using some of the various sentences I wrote (personal blog, chat, etc.)\n\n\nNote that performance is not guaranteed since only a small number of datasets were used for the experiment. The number of samples for training set is just around 5 million after tokenization.\nFor distributed training, all weights were trained without adapter techniques, and sharding parallelization was performed with ZeRO-2. The presets are as follows.\n\n\nSince this is a model that has not been fine-tuned, it is recommended to perform fine tuning such as instruction tuning/alignment tuning according to your use case.\n\n\nSome hyperparameters are listed below.\n\n\nHow to Get Started with the Model\n---------------------------------",
"### References\n\n\n* Base model: microsoft/phi-2\n\n\nNotes\n-----",
"### License\n\n\ncc-by-sa 3.0; The license of phi-2 is MIT, but I considered the licensing of the dataset used for training.",
"### Caution\n\n\nThis model was created as a personal experiment, unrelated to the organization I work for. The model may not operate correctly because separate verification was not performed. Please be careful unless it is for personal experimentation or PoC (Proof of Concept)!"
] | [
96,
192,
225,
17,
34,
59
] | [
"passage: TAGS\n#transformers #safetensors #phi #text-generation #custom_code #ko #en #dataset-wikimedia/wikipedia #dataset-maywell/korean_textbooks #dataset-nampdn-ai/tiny-codes #dataset-Open-Orca/OpenOrca #license-cc-by-sa-3.0 #autotrain_compatible #region-us \n### Vocab Expansion\n\n\nModel Name: Original phi-2, Vocabulary Size: 50,295, Description: BBPE (Byte-level BPE)\nModel Name: phi-2-ko, Vocabulary Size: 66,676, Description: BBPE. Added Korean vocab and merges\n\n\nTokenizing \"아마존 세이지메이커\"\n\n\nModel: Original phi-2, # of tokens: 25, Tokens: '[168, 243, 226, 167, 100, 230, 168, 94, 112, 23821, 226, 116, 35975, 112, 168, 100, 222, 167, 102, 242, 35975, 112, 168, 119, 97]'\nModel: phi-2-ko, # of tokens: 6, Tokens: '[57974, 51299, 50617, 51005, 52027, 51446]'"
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null | null | diffusers | # concept-perfect-eyes
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/rorito/concept-perfect-eyes/tree/main) them in the Files & versions tab.
| {"tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "-", "output": {"url": "images/ComfyUI_00636_.jpeg"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0"} | text-to-image | rorito/concept-perfect-eyes | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"has_space",
"region:us"
] | 2024-02-07T23:41:41+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
| # concept-perfect-eyes
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"# concept-perfect-eyes\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",
"# concept-perfect-eyes\n\n<Gallery />",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
60,
12,
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# concept-perfect-eyes\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 | null |
# ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation
This page shares the official model checkpoints of the paper \
"Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation" \
from Microsoft Applied Science Group and UC Berkeley \
by [Yatong Bai](https://bai-yt.github.io),
[Trung Dang](https://www.microsoft.com/applied-sciences/people/trung-dang),
[Dung Tran](https://www.microsoft.com/applied-sciences/people/dung-tran),
[Kazuhito Koishida](https://www.microsoft.com/applied-sciences/people/kazuhito-koishida),
and [Somayeh Sojoudi](https://people.eecs.berkeley.edu/~sojoudi/).
**[[Preprint Paper](https://arxiv.org/abs/2309.10740)]**
**[[Project Homepage](https://consistency-tta.github.io)]**
**[[Code](https://github.com/Bai-YT/ConsistencyTTA)]**
**[[Model Checkpoints](https://huggingface.co/Bai-YT/ConsistencyTTA)]**
**[[Generation Examples](https://consistency-tta.github.io/demo.html)]**
## Description
This work proposes a *consistency distillation* framework to train
text-to-audio (TTA) generation models that only require a single neural network query,
reducing the computation of the core step of diffusion-based TTA models by a factor of 400.
By incorporating *classifier-free guidance* into the distillation framework,
our models retain diffusion models' impressive generation quality and diversity.
Furthermore, the non-recurrent differentiable structure of the consistency model
allows for end-to-end fine-tuning with novel loss functions such as the CLAP score, further boosting performance.
<center>
<img src="main_figure_.png" alt="ConsistencyTTA Results" title="Results" width="480"/>
</center>
## Model Details
We share three model checkpoints:
- [ConsistencyTTA directly distilled from a diffusion model](
https://huggingface.co/Bai-YT/ConsistencyTTA/blob/main/ConsistencyTTA.zip);
- [ConsistencyTTA fine-tuned by optimizing the CLAP score](
https://huggingface.co/Bai-YT/ConsistencyTTA/blob/main/ConsistencyTTA_CLAPFT.zip);
- [The diffusion teacher model from which ConsistencyTTA is distilled](
https://huggingface.co/Bai-YT/ConsistencyTTA/blob/main/LightweightLDM.zip).
The first two models are capable of high-quality single-step text-to-audio generation. Generations are 10 seconds long.
After downloading and unzipping the files, place them in the `saved` directory.
The training and inference code are on our [GitHub page](https://github.com/Bai-YT/ConsistencyTTA). Please refer to the GitHub page for usage details.
| {"license": "cc-by-nc-nd-4.0"} | null | Bai-YT/ConsistencyTTA | [
"arxiv:2309.10740",
"license:cc-by-nc-nd-4.0",
"region:us"
] | 2024-02-07T23:42:56+00:00 | [
"2309.10740"
] | [] | TAGS
#arxiv-2309.10740 #license-cc-by-nc-nd-4.0 #region-us
|
# ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation
This page shares the official model checkpoints of the paper \
"Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation" \
from Microsoft Applied Science Group and UC Berkeley \
by Yatong Bai,
Trung Dang,
Dung Tran,
Kazuhito Koishida,
and Somayeh Sojoudi.
[Preprint Paper]
[Project Homepage]
[Code]
[Model Checkpoints]
[Generation Examples]
## Description
This work proposes a *consistency distillation* framework to train
text-to-audio (TTA) generation models that only require a single neural network query,
reducing the computation of the core step of diffusion-based TTA models by a factor of 400.
By incorporating *classifier-free guidance* into the distillation framework,
our models retain diffusion models' impressive generation quality and diversity.
Furthermore, the non-recurrent differentiable structure of the consistency model
allows for end-to-end fine-tuning with novel loss functions such as the CLAP score, further boosting performance.
<center>
<img src="main_figure_.png" alt="ConsistencyTTA Results" title="Results" width="480"/>
</center>
## Model Details
We share three model checkpoints:
- ConsistencyTTA directly distilled from a diffusion model;
- ConsistencyTTA fine-tuned by optimizing the CLAP score;
- The diffusion teacher model from which ConsistencyTTA is distilled.
The first two models are capable of high-quality single-step text-to-audio generation. Generations are 10 seconds long.
After downloading and unzipping the files, place them in the 'saved' directory.
The training and inference code are on our GitHub page. Please refer to the GitHub page for usage details.
| [
"# ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation\n\nThis page shares the official model checkpoints of the paper \\\n\"Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation\" \\\nfrom Microsoft Applied Science Group and UC Berkeley \\\nby Yatong Bai,\nTrung Dang,\nDung Tran,\nKazuhito Koishida,\nand Somayeh Sojoudi.\n\n[Preprint Paper] \n[Project Homepage] \n[Code] \n[Model Checkpoints] \n[Generation Examples]",
"## Description\n\nThis work proposes a *consistency distillation* framework to train\ntext-to-audio (TTA) generation models that only require a single neural network query,\nreducing the computation of the core step of diffusion-based TTA models by a factor of 400.\nBy incorporating *classifier-free guidance* into the distillation framework,\nour models retain diffusion models' impressive generation quality and diversity.\nFurthermore, the non-recurrent differentiable structure of the consistency model\nallows for end-to-end fine-tuning with novel loss functions such as the CLAP score, further boosting performance.\n\n<center>\n <img src=\"main_figure_.png\" alt=\"ConsistencyTTA Results\" title=\"Results\" width=\"480\"/>\n</center>",
"## Model Details\n\nWe share three model checkpoints:\n- ConsistencyTTA directly distilled from a diffusion model;\n- ConsistencyTTA fine-tuned by optimizing the CLAP score;\n- The diffusion teacher model from which ConsistencyTTA is distilled.\n\nThe first two models are capable of high-quality single-step text-to-audio generation. Generations are 10 seconds long.\n\nAfter downloading and unzipping the files, place them in the 'saved' directory.\n\nThe training and inference code are on our GitHub page. Please refer to the GitHub page for usage details."
] | [
"TAGS\n#arxiv-2309.10740 #license-cc-by-nc-nd-4.0 #region-us \n",
"# ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation\n\nThis page shares the official model checkpoints of the paper \\\n\"Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation\" \\\nfrom Microsoft Applied Science Group and UC Berkeley \\\nby Yatong Bai,\nTrung Dang,\nDung Tran,\nKazuhito Koishida,\nand Somayeh Sojoudi.\n\n[Preprint Paper] \n[Project Homepage] \n[Code] \n[Model Checkpoints] \n[Generation Examples]",
"## Description\n\nThis work proposes a *consistency distillation* framework to train\ntext-to-audio (TTA) generation models that only require a single neural network query,\nreducing the computation of the core step of diffusion-based TTA models by a factor of 400.\nBy incorporating *classifier-free guidance* into the distillation framework,\nour models retain diffusion models' impressive generation quality and diversity.\nFurthermore, the non-recurrent differentiable structure of the consistency model\nallows for end-to-end fine-tuning with novel loss functions such as the CLAP score, further boosting performance.\n\n<center>\n <img src=\"main_figure_.png\" alt=\"ConsistencyTTA Results\" title=\"Results\" width=\"480\"/>\n</center>",
"## Model Details\n\nWe share three model checkpoints:\n- ConsistencyTTA directly distilled from a diffusion model;\n- ConsistencyTTA fine-tuned by optimizing the CLAP score;\n- The diffusion teacher model from which ConsistencyTTA is distilled.\n\nThe first two models are capable of high-quality single-step text-to-audio generation. Generations are 10 seconds long.\n\nAfter downloading and unzipping the files, place them in the 'saved' directory.\n\nThe training and inference code are on our GitHub page. Please refer to the GitHub page for usage details."
] | [
27,
183,
183,
135
] | [
"passage: TAGS\n#arxiv-2309.10740 #license-cc-by-nc-nd-4.0 #region-us \n# ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation\n\nThis page shares the official model checkpoints of the paper \\\n\"Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation\" \\\nfrom Microsoft Applied Science Group and UC Berkeley \\\nby Yatong Bai,\nTrung Dang,\nDung Tran,\nKazuhito Koishida,\nand Somayeh Sojoudi.\n\n[Preprint Paper] \n[Project Homepage] \n[Code] \n[Model Checkpoints] \n[Generation Examples]## Description\n\nThis work proposes a *consistency distillation* framework to train\ntext-to-audio (TTA) generation models that only require a single neural network query,\nreducing the computation of the core step of diffusion-based TTA models by a factor of 400.\nBy incorporating *classifier-free guidance* into the distillation framework,\nour models retain diffusion models' impressive generation quality and diversity.\nFurthermore, the non-recurrent differentiable structure of the consistency model\nallows for end-to-end fine-tuning with novel loss functions such as the CLAP score, further boosting performance.\n\n<center>\n <img src=\"main_figure_.png\" alt=\"ConsistencyTTA Results\" title=\"Results\" width=\"480\"/>\n</center>"
] | [
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null | null | transformers | hariqueen/code-llama-korean 의 lora 어답터와 베이스 모델인 TinyPixel/CodeLlama-7B-Python-bf16-sharded 머지한 코드라마 한국어 버전입니다. | {} | text-generation | sosoai/codellama-korean-merged | [
"transformers",
"safetensors",
"llama",
"text-generation",
"custom_code",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-07T23:52:25+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #custom_code #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| hariqueen/code-llama-korean 의 lora 어답터와 베이스 모델인 TinyPixel/CodeLlama-7B-Python-bf16-sharded 머지한 코드라마 한국어 버전입니다. | [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #custom_code #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
52
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #custom_code #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers | ### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** Mit Patel
- **Model type:** Text generation/ classifier
- **Language(s) (NLP):** English
- **Finetuned from model :** Phi-2
## Training Details
https://github.com/mit1280/fined-tuning/blob/main/phi_2_classification_fine_tune.ipynb
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 10000
### Inference
```python
!pip install -q transformers==4.37.2 accelerate==0.27.0
import re
from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria
import torch
tokenizer = AutoTokenizer.from_pretrained("Mit1208/phi-2-classification-sentiment-merged")
model = AutoModelForCausalLM.from_pretrained("Mit1208/phi-2-classification-sentiment-merged", device_map="auto", trust_remote_code=True).eval()
class EosListStoppingCriteria(StoppingCriteria):
def __init__(self, eos_sequence = tokenizer.encode("<|im_end|>")):
self.eos_sequence = eos_sequence
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
last_ids = input_ids[:,-len(self.eos_sequence):].tolist()
return self.eos_sequence in last_ids
inf_conv = [{'from': 'human',
'value': "Text: In sales volume , Coca-Cola 's market share has decreased by 2.2 % to 24.2 % ."},
{'from': 'phi', 'value': "I've read this text."},
{'from': 'human',
'value': 'Please determine the sentiment of the given text and choose from the options: Positive, Negative, Neutral, or Cannot be determined.'}]
# need to load because model doesn't has classifer head.
id2label = {0: 'negative', 1: 'neutral', 2: 'positive'}
inference_text = tokenizer.apply_chat_template(inf_conv, tokenize=False) + '<|im_start|>phi:\n'
inputs = tokenizer(inference_text, return_tensors="pt", return_attention_mask=False).to('cuda')
outputs = model.generate(inputs["input_ids"], max_new_tokens=1024, pad_token_id= tokenizer.eos_token_id,
stopping_criteria = [EosListStoppingCriteria()])
text = tokenizer.batch_decode(outputs)[0]
answer = text.split("<|im_start|>phi:")[-1].replace("<|im_end|>", "").replace(".", "")
sentiment_label = re.search(r'(\d)', answer)
sentiment_score = int(sentiment_label.group(1))
if sentiment_score:
print(id2label.get(sentiment_score, "none"))
else:
print("none")
```
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["sentiment", "classifier"], "datasets": ["financial_phrasebank"]} | text-generation | Mit1208/phi-2-classification-sentiment-merged | [
"transformers",
"safetensors",
"phi",
"text-generation",
"sentiment",
"classifier",
"conversational",
"custom_code",
"en",
"dataset:financial_phrasebank",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-07T23:52:52+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #phi #text-generation #sentiment #classifier #conversational #custom_code #en #dataset-financial_phrasebank #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: Mit Patel
- Model type: Text generation/ classifier
- Language(s) (NLP): English
- Finetuned from model : Phi-2
## Training Details
URL
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 10000
### Inference
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | [
"### 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: Mit Patel\n- Model type: Text generation/ classifier \n- Language(s) (NLP): English\n- Finetuned from model : Phi-2",
"## Training Details\nURL",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\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- training_steps: 10000",
"### Inference",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #phi #text-generation #sentiment #classifier #conversational #custom_code #en #dataset-financial_phrasebank #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### 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: Mit Patel\n- Model type: Text generation/ classifier \n- Language(s) (NLP): English\n- Finetuned from model : Phi-2",
"## Training Details\nURL",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\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- training_steps: 10000",
"### Inference",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
69,
67,
4,
89,
5,
39
] | [
"passage: TAGS\n#transformers #safetensors #phi #text-generation #sentiment #classifier #conversational #custom_code #en #dataset-financial_phrasebank #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### 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: Mit Patel\n- Model type: Text generation/ classifier \n- Language(s) (NLP): English\n- Finetuned from model : Phi-2## Training Details\nURL### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\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- training_steps: 10000### Inference### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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] |
null | null | null |
# 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 | adarshheg/llama2-13b-finetuned-100-v1 | [
"safetensors",
"autotrain",
"text-generation",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-07T23:54:15+00:00 | [] | [] | TAGS
#safetensors #autotrain #text-generation #license-other #endpoints_compatible #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#safetensors #autotrain #text-generation #license-other #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
33,
29,
3
] | [
"passage: TAGS\n#safetensors #autotrain #text-generation #license-other #endpoints_compatible #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 | transformers |
# Model Card for Model ID
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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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| {"library_name": "transformers", "tags": []} | text-generation | arieridwans/phi_2-finetuned-lyrics | [
"transformers",
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|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- 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 Authors [optional]
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] |
null | null | transformers | This is a merge created by https://huggingface.co/Test157t I have merely quantized the model into GGUF. Please visit https://huggingface.co/Test157t/Kunocchini-7b for the original weights. The original description is as follows:
Thanks to @Epiculous for the dope model/ help with llm backends and support overall.
Id like to also thank @kalomaze for the dope sampler additions to ST.
@SanjiWatsuki Thank you very much for the help, and the model!
ST users can find the TextGenPreset in the folder labeled so.
Quants:Thank you @bartowski! https://huggingface.co/bartowski/Kunocchini-exl2
# mergedmodel
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B)
* [Epiculous/Fett-uccine-7B](https://huggingface.co/Epiculous/Fett-uccine-7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [0, 32]
- model: Epiculous/Fett-uccine-7B
layer_range: [0, 32]
merge_method: slerp
base_model: SanjiWatsuki/Kunoichi-DPO-v2-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge", "alpaca", "mistral"], "base_model": ["SanjiWatsuki/Kunoichi-DPO-v2-7B", "Epiculous/Fett-uccine-7B"]} | null | jeiku/Konocchini-7B_GGUF | [
"transformers",
"gguf",
"mergekit",
"merge",
"alpaca",
"mistral",
"base_model:SanjiWatsuki/Kunoichi-DPO-v2-7B",
"base_model:Epiculous/Fett-uccine-7B",
"endpoints_compatible",
"region:us"
] | 2024-02-07T23:58:56+00:00 | [] | [] | TAGS
#transformers #gguf #mergekit #merge #alpaca #mistral #base_model-SanjiWatsuki/Kunoichi-DPO-v2-7B #base_model-Epiculous/Fett-uccine-7B #endpoints_compatible #region-us
| This is a merge created by URL I have merely quantized the model into GGUF. Please visit URL for the original weights. The original description is as follows:
Thanks to @Epiculous for the dope model/ help with llm backends and support overall.
Id like to also thank @kalomaze for the dope sampler additions to ST.
@SanjiWatsuki Thank you very much for the help, and the model!
ST users can find the TextGenPreset in the folder labeled so.
!image/jpeg
Quants:Thank you @bartowski! URL
# mergedmodel
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* SanjiWatsuki/Kunoichi-DPO-v2-7B
* Epiculous/Fett-uccine-7B
### Configuration
The following YAML configuration was used to produce this model:
| [
"# mergedmodel\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* SanjiWatsuki/Kunoichi-DPO-v2-7B\n* Epiculous/Fett-uccine-7B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #gguf #mergekit #merge #alpaca #mistral #base_model-SanjiWatsuki/Kunoichi-DPO-v2-7B #base_model-Epiculous/Fett-uccine-7B #endpoints_compatible #region-us \n",
"# mergedmodel\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* SanjiWatsuki/Kunoichi-DPO-v2-7B\n* Epiculous/Fett-uccine-7B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
72,
20,
4,
18,
45,
17
] | [
"passage: TAGS\n#transformers #gguf #mergekit #merge #alpaca #mistral #base_model-SanjiWatsuki/Kunoichi-DPO-v2-7B #base_model-Epiculous/Fett-uccine-7B #endpoints_compatible #region-us \n# mergedmodel\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the SLERP merge method.### Models Merged\n\nThe following models were included in the merge:\n* SanjiWatsuki/Kunoichi-DPO-v2-7B\n* Epiculous/Fett-uccine-7B### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
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null | null | null |
# Lora of navia/ナヴィア/娜维娅 (Genshin Impact)
## What Is This?
This is the LoRA model of waifu navia/ナヴィア/娜维娅 (Genshin Impact).
## How Is It Trained?
* This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion).
* The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
* The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest).
* Dataset used for training is the `stage3-p480-800` in [CyberHarem/navia_genshin](https://huggingface.co/datasets/CyberHarem/navia_genshin), which contains 1300 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 1, resolution is 720x720, clustering into 20 buckets.
* Trained for 10000 steps, 40 checkpoints were saved and evaluated.
* **Trigger word is `navia_genshin`.**
* Pruned core tags for this waifu are `long_hair, blonde_hair, blue_eyes, bangs, hat, breasts, black_headwear, drill_hair, very_long_hair, large_breasts, bow`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
## How to Use It?
### If You Are Using A1111 WebUI v1.7+
**Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 6750, you need to download [`6750/navia_genshin.pt`](https://huggingface.co/CyberHarem/navia_genshin/resolve/main/6750/navia_genshin.pt) as the embedding and [`6750/navia_genshin.safetensors`](https://huggingface.co/CyberHarem/navia_genshin/resolve/main/6750/navia_genshin.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters.
## Which Step Should I Use?
We selected 5 good steps for you to choose. The best one is step 6750.
1640 images (1.82 GiB) were generated for auto-testing.
The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
Here are the preview of the recommended steps:
| Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0 | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 |
|-------:|--------:|:----------|:-------------|:--------------|:----------|:------------------------------------------------------------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:--------------------------------------------|:--------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:--------------------------------|:------------------------------------|:--------------------------------|:----------------------------------|:----------------------------------------|:----------------------------------------|:----------------------------------------|:------------------------------|:----------------------------------|:----------------------------------|:--------------------------------|:------------------------------------------------|:----------------------------------|:----------------------------------|:------------------------------|:--------------------------------|:--------------------------------------|:--------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------|:--------------------------------------|
| 6750 | 21 | 0.961 | 0.881 | **0.852** | **0.815** | [Download](https://huggingface.co/CyberHarem/navia_genshin/resolve/main/6750/navia_genshin.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 9750 | 30 | **0.963** | 0.879 | 0.840 | 0.806 | [Download](https://huggingface.co/CyberHarem/navia_genshin/resolve/main/9750/navia_genshin.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 6500 | 20 | 0.961 | 0.881 | 0.848 | 0.806 | [Download](https://huggingface.co/CyberHarem/navia_genshin/resolve/main/6500/navia_genshin.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 9000 | 28 | 0.962 | **0.913** | 0.843 | 0.804 | [Download](https://huggingface.co/CyberHarem/navia_genshin/resolve/main/9000/navia_genshin.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| 9250 | 29 | 0.960 | 0.871 | 0.842 | 0.796 | [Download](https://huggingface.co/CyberHarem/navia_genshin/resolve/main/9250/navia_genshin.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
## Anything Else?
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
## All Steps
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* [Steps From 7750 to 10000](all/0.md)
* [Steps From 5250 to 7500](all/1.md)
* [Steps From 2750 to 5000](all/2.md)
* [Steps From 250 to 2500](all/3.md)
| {"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/navia_genshin"], "pipeline_tag": "text-to-image"} | text-to-image | CyberHarem/navia_genshin | [
"art",
"not-for-all-audiences",
"text-to-image",
"dataset:CyberHarem/navia_genshin",
"license:mit",
"region:us"
] | 2024-02-08T00:03:27+00:00 | [] | [] | TAGS
#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/navia_genshin #license-mit #region-us
| Lora of navia/ナヴィア/娜维娅 (Genshin Impact)
=======================================
What Is This?
-------------
This is the LoRA model of waifu navia/ナヴィア/娜维娅 (Genshin Impact).
How Is It Trained?
------------------
* This model is trained with HCP-Diffusion.
* The auto-training framework is maintained by DeepGHS Team.
* The base model used for training is deepghs/animefull-latest.
* Dataset used for training is the 'stage3-p480-800' in CyberHarem/navia\_genshin, which contains 1300 images.
* Batch size is 4, resolution is 720x720, clustering into 5 buckets.
* Batch size for regularization dataset is 1, resolution is 720x720, clustering into 20 buckets.
* Trained for 10000 steps, 40 checkpoints were saved and evaluated.
* Trigger word is 'navia\_genshin'.
* Pruned core tags for this waifu are 'long\_hair, blonde\_hair, blue\_eyes, bangs, hat, breasts, black\_headwear, drill\_hair, very\_long\_hair, large\_breasts, bow'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable.
How to Use It?
--------------
### If You Are Using A1111 WebUI v1.7+
Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.
### If You Are Using A1111 WebUI v1.6 or Lower
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 6750, you need to download '6750/navia\_genshin.pt' as the embedding and '6750/navia\_genshin.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.
Which Step Should I Use?
------------------------
We selected 5 good steps for you to choose. The best one is step 6750.
1640 images (1.82 GiB) were generated for auto-testing.
!Metrics Plot
The base model used for generating preview images is Meina/MeinaMix\_V11.
Here are the preview of the recommended steps:
Anything Else?
--------------
Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
All Steps
---------
We uploaded the files in all steps. you can check the images, metrics and download them in the following links:
* Steps From 7750 to 10000
* Steps From 5250 to 7500
* Steps From 2750 to 5000
* Steps From 250 to 2500
| [
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 6750, you need to download '6750/navia\\_genshin.pt' as the embedding and '6750/navia\\_genshin.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 6750.\n\n\n1640 images (1.82 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 7750 to 10000\n* Steps From 5250 to 7500\n* Steps From 2750 to 5000\n* Steps From 250 to 2500"
] | [
"TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/navia_genshin #license-mit #region-us \n",
"### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.",
"### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 6750, you need to download '6750/navia\\_genshin.pt' as the embedding and '6750/navia\\_genshin.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 6750.\n\n\n1640 images (1.82 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 7750 to 10000\n* Steps From 5250 to 7500\n* Steps From 2750 to 5000\n* Steps From 250 to 2500"
] | [
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"passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/navia_genshin #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file."
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null | null | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)
* [Sao10K/Fimbulvetr-10.7B-v1](https://huggingface.co/Sao10K/Fimbulvetr-10.7B-v1)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Sao10K/Fimbulvetr-10.7B-v1
layer_range: [0, 32]
- model: upstage/SOLAR-10.7B-Instruct-v1.0
layer_range: [0, 32]
merge_method: slerp
base_model: upstage/SOLAR-10.7B-Instruct-v1.0
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["upstage/SOLAR-10.7B-Instruct-v1.0", "Sao10K/Fimbulvetr-10.7B-v1"]} | text-generation | ssaryssane/ssary-10.7B-slerp | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:upstage/SOLAR-10.7B-Instruct-v1.0",
"base_model:Sao10K/Fimbulvetr-10.7B-v1",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T00:05:41+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #base_model-upstage/SOLAR-10.7B-Instruct-v1.0 #base_model-Sao10K/Fimbulvetr-10.7B-v1 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* upstage/SOLAR-10.7B-Instruct-v1.0
* Sao10K/Fimbulvetr-10.7B-v1
### Configuration
The following YAML configuration was used to produce this model:
| [
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* upstage/SOLAR-10.7B-Instruct-v1.0\n* Sao10K/Fimbulvetr-10.7B-v1",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #base_model-upstage/SOLAR-10.7B-Instruct-v1.0 #base_model-Sao10K/Fimbulvetr-10.7B-v1 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* upstage/SOLAR-10.7B-Instruct-v1.0\n* Sao10K/Fimbulvetr-10.7B-v1",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
100,
18,
4,
18,
49,
17
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #base_model-upstage/SOLAR-10.7B-Instruct-v1.0 #base_model-Sao10K/Fimbulvetr-10.7B-v1 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# merge\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the SLERP merge method.### Models Merged\n\nThe following models were included in the merge:\n* upstage/SOLAR-10.7B-Instruct-v1.0\n* Sao10K/Fimbulvetr-10.7B-v1### Configuration\n\nThe following YAML configuration was used to produce this model:"
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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. -->
# SMIDS_3x_beit_large_Adamax_lr00001_fold2
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9041
- Accuracy: 0.9151
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2135 | 1.0 | 450 | 0.2499 | 0.9068 |
| 0.1024 | 2.0 | 900 | 0.3310 | 0.8985 |
| 0.0722 | 3.0 | 1350 | 0.3756 | 0.9068 |
| 0.0444 | 4.0 | 1800 | 0.5490 | 0.8985 |
| 0.0294 | 5.0 | 2250 | 0.5763 | 0.9035 |
| 0.0399 | 6.0 | 2700 | 0.6453 | 0.9002 |
| 0.0054 | 7.0 | 3150 | 0.6321 | 0.9185 |
| 0.0656 | 8.0 | 3600 | 0.6941 | 0.9101 |
| 0.0009 | 9.0 | 4050 | 0.6778 | 0.9168 |
| 0.0003 | 10.0 | 4500 | 0.7636 | 0.9135 |
| 0.0001 | 11.0 | 4950 | 0.8099 | 0.9168 |
| 0.0478 | 12.0 | 5400 | 0.7814 | 0.9185 |
| 0.0002 | 13.0 | 5850 | 0.7658 | 0.9168 |
| 0.0 | 14.0 | 6300 | 0.7726 | 0.9185 |
| 0.0522 | 15.0 | 6750 | 0.8598 | 0.9151 |
| 0.0 | 16.0 | 7200 | 0.8097 | 0.9151 |
| 0.0003 | 17.0 | 7650 | 0.8168 | 0.9085 |
| 0.0 | 18.0 | 8100 | 0.8570 | 0.9185 |
| 0.0286 | 19.0 | 8550 | 0.8387 | 0.9101 |
| 0.0 | 20.0 | 9000 | 0.8231 | 0.9185 |
| 0.0004 | 21.0 | 9450 | 0.8462 | 0.9235 |
| 0.0 | 22.0 | 9900 | 0.7917 | 0.9235 |
| 0.011 | 23.0 | 10350 | 0.8161 | 0.9185 |
| 0.0 | 24.0 | 10800 | 0.8264 | 0.9118 |
| 0.0001 | 25.0 | 11250 | 0.7972 | 0.9135 |
| 0.0001 | 26.0 | 11700 | 0.8607 | 0.9135 |
| 0.0 | 27.0 | 12150 | 0.8872 | 0.9135 |
| 0.0 | 28.0 | 12600 | 0.8710 | 0.9052 |
| 0.0045 | 29.0 | 13050 | 0.8436 | 0.9135 |
| 0.0 | 30.0 | 13500 | 0.8688 | 0.9118 |
| 0.0001 | 31.0 | 13950 | 0.8401 | 0.9168 |
| 0.0001 | 32.0 | 14400 | 0.8371 | 0.9135 |
| 0.0 | 33.0 | 14850 | 0.8668 | 0.9151 |
| 0.0 | 34.0 | 15300 | 0.8619 | 0.9135 |
| 0.0 | 35.0 | 15750 | 0.8638 | 0.9151 |
| 0.0002 | 36.0 | 16200 | 0.8700 | 0.9135 |
| 0.0 | 37.0 | 16650 | 0.8450 | 0.9151 |
| 0.0 | 38.0 | 17100 | 0.8796 | 0.9135 |
| 0.0001 | 39.0 | 17550 | 0.8759 | 0.9151 |
| 0.0128 | 40.0 | 18000 | 0.8915 | 0.9135 |
| 0.0 | 41.0 | 18450 | 0.8718 | 0.9185 |
| 0.0 | 42.0 | 18900 | 0.8861 | 0.9151 |
| 0.0 | 43.0 | 19350 | 0.8972 | 0.9151 |
| 0.0001 | 44.0 | 19800 | 0.9055 | 0.9151 |
| 0.0341 | 45.0 | 20250 | 0.8953 | 0.9168 |
| 0.0004 | 46.0 | 20700 | 0.9032 | 0.9151 |
| 0.0 | 47.0 | 21150 | 0.9025 | 0.9168 |
| 0.0004 | 48.0 | 21600 | 0.9012 | 0.9168 |
| 0.0 | 49.0 | 22050 | 0.9035 | 0.9151 |
| 0.0 | 50.0 | 22500 | 0.9041 | 0.9151 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_Adamax_lr00001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9151414309484193, "name": "Accuracy"}]}]}]} | image-classification | onizukal/SMIDS_3x_beit_large_Adamax_lr00001_fold2 | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T00:08:06+00:00 | [] | [] | TAGS
#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| SMIDS\_3x\_beit\_large\_Adamax\_lr00001\_fold2
==============================================
This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9041
* Accuracy: 0.9151
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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 50
### Training results
### Framework versions
* Transformers 4.32.1
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.2
| [
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"### Training results",
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] | [
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null | null | transformers |
# Uploaded model
- **Developed by:** smotoc
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "gguf"], "base_model": "unsloth/mistral-7b-bnb-4bit"} | text-generation | smotoc/foxy_mistral7B_unsloth_4k | [
"transformers",
"pytorch",
"gguf",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T00:09:47+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gguf #mistral #text-generation #text-generation-inference #unsloth #en #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: smotoc
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: smotoc\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #pytorch #gguf #mistral #text-generation #text-generation-inference #unsloth #en #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: smotoc\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
81,
79
] | [
"passage: TAGS\n#transformers #pytorch #gguf #mistral #text-generation #text-generation-inference #unsloth #en #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Uploaded model\n\n- Developed by: smotoc\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
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null | null | transformers | # Amharic WordPiece Tokenizer
This repo contains a **WordPiece** tokenizer trained on the **Amharic** subset of the [oscar](https://huggingface.co/datasets/oscar) and [mc4](https://huggingface.co/datasets/mc4) datasets. It's the same as the **BERT** tokenizer but trained from scratch on an amharic dataset with a vocabulary size of `30522`.
# How to use
You can load the tokenizer from huggingface hub as follows.
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("rasyosef/bert-amharic-tokenizer")
tokenizer.tokenize("የዓለምአቀፉ ነጻ ንግድ መስፋፋት ድህነትን ለማሸነፍ በሚደረገው ትግል አንዱ ጠቃሚ መሣሪያ ሊሆን መቻሉ ብዙ የሚነገርለት ጉዳይ ነው።")
```
Output:
```python
['የዓለም', '##አቀፉ', 'ነጻ', 'ንግድ', 'መስፋፋት', 'ድህነትን', 'ለማሸነፍ', 'በሚደረገው', 'ትግል', 'አንዱ', 'ጠቃሚ', 'መሣሪያ', 'ሊሆን', 'መቻሉ', 'ብዙ', 'የሚነገርለት', 'ጉዳይ', 'ነው', '።']
``` | {"language": ["am"], "license": "mit", "library_name": "transformers", "datasets": ["oscar", "mc4"]} | null | rasyosef/bert-amharic-tokenizer | [
"transformers",
"am",
"dataset:oscar",
"dataset:mc4",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-08T00:10:43+00:00 | [] | [
"am"
] | TAGS
#transformers #am #dataset-oscar #dataset-mc4 #license-mit #endpoints_compatible #region-us
| # Amharic WordPiece Tokenizer
This repo contains a WordPiece tokenizer trained on the Amharic subset of the oscar and mc4 datasets. It's the same as the BERT tokenizer but trained from scratch on an amharic dataset with a vocabulary size of '30522'.
# How to use
You can load the tokenizer from huggingface hub as follows.
Output:
| [
"# Amharic WordPiece Tokenizer\nThis repo contains a WordPiece tokenizer trained on the Amharic subset of the oscar and mc4 datasets. It's the same as the BERT tokenizer but trained from scratch on an amharic dataset with a vocabulary size of '30522'.",
"# How to use\nYou can load the tokenizer from huggingface hub as follows.\n\n\nOutput:"
] | [
"TAGS\n#transformers #am #dataset-oscar #dataset-mc4 #license-mit #endpoints_compatible #region-us \n",
"# Amharic WordPiece Tokenizer\nThis repo contains a WordPiece tokenizer trained on the Amharic subset of the oscar and mc4 datasets. It's the same as the BERT tokenizer but trained from scratch on an amharic dataset with a vocabulary size of '30522'.",
"# How to use\nYou can load the tokenizer from huggingface hub as follows.\n\n\nOutput:"
] | [
37,
73,
23
] | [
"passage: TAGS\n#transformers #am #dataset-oscar #dataset-mc4 #license-mit #endpoints_compatible #region-us \n# Amharic WordPiece Tokenizer\nThis repo contains a WordPiece tokenizer trained on the Amharic subset of the oscar and mc4 datasets. It's the same as the BERT tokenizer but trained from scratch on an amharic dataset with a vocabulary size of '30522'.# How to use\nYou can load the tokenizer from huggingface hub as follows.\n\n\nOutput:"
] | [
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null | null | sentence-transformers |
# celik-muhammed/multi-qa-mpnet-base-dot-v1-finetuned-dtc-zoomcamp
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('celik-muhammed/multi-qa-mpnet-base-dot-v1-finetuned-dtc-zoomcamp')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=celik-muhammed/multi-qa-mpnet-base-dot-v1-finetuned-dtc-zoomcamp)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 794 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 989 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 1.2800000000000005e-10
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 80.0,
"weight_decay": 0.1
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': True, 'pooling_mode_mean_sqrt_len_tokens': True, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
(2): Dense({'in_features': 3072, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(3): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | celik-muhammed/multi-qa-mpnet-base-dot-v1-finetuned-dtc-zoomcamp | [
"sentence-transformers",
"pytorch",
"tflite",
"safetensors",
"mpnet",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | 2024-02-08T00:11:24+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #tflite #safetensors #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# celik-muhammed/multi-qa-mpnet-base-dot-v1-finetuned-dtc-zoomcamp
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 794 with parameters:
Loss:
'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 989 with parameters:
Loss:
'sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss'
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
| [
"# celik-muhammed/multi-qa-mpnet-base-dot-v1-finetuned-dtc-zoomcamp\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 794 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 989 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #pytorch #tflite #safetensors #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# celik-muhammed/multi-qa-mpnet-base-dot-v1-finetuned-dtc-zoomcamp\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 794 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 989 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
49,
74,
38,
29,
136,
5,
6
] | [
"passage: TAGS\n#sentence-transformers #pytorch #tflite #safetensors #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n# celik-muhammed/multi-qa-mpnet-base-dot-v1-finetuned-dtc-zoomcamp\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 794 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 989 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.OnlineContrastiveLoss.OnlineContrastiveLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors"
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null | null | null | # Llama-2-7b-chat-hf-GGUF
This repo contains GGUF format, quantized model files for [Meta's Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b-hf) LLM. The files were generated using the [hf-to-gguf](https://github.com/jmcconne/hf-to-gguf) project on GitHub which facilitates the conversion of LLMs stored in Hugging Face into GGUF while providing traceability and reproducibility. Each model file has an accompanying JSON config file containing the source and version of the model being converted, version of conversion scripts, quantization method, and anything else needed to fully reproduce the converted model. Keeping the JSON config file with the GGUF model file anywhere the model is deployed can be useful for use cases that require tight version control and reproducibility.
### Downloading model and JSON config files from the command line
Install the huggingface_hub Python library:
```
pip3 install huggingface_hub
```
Download the model and JSON config file for a specific quantization:
```
huggingface-cli download jeffmcc/Llama-2-7b-chat-hf-GGUF --local-dir . --local-dir-use-symlinks False --include='*q4_k_m*'
```
| {"language": ["en"], "license": "llama2", "tags": ["facebook", "meta", "pytorch", "llama", "llama-2"]} | null | jeffmcc/Llama-2-7b-chat-hf-GGUF | [
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-2",
"en",
"license:llama2",
"region:us"
] | 2024-02-08T00:12:21+00:00 | [] | [
"en"
] | TAGS
#gguf #facebook #meta #pytorch #llama #llama-2 #en #license-llama2 #region-us
| # Llama-2-7b-chat-hf-GGUF
This repo contains GGUF format, quantized model files for Meta's Llama 2 7B LLM. The files were generated using the hf-to-gguf project on GitHub which facilitates the conversion of LLMs stored in Hugging Face into GGUF while providing traceability and reproducibility. Each model file has an accompanying JSON config file containing the source and version of the model being converted, version of conversion scripts, quantization method, and anything else needed to fully reproduce the converted model. Keeping the JSON config file with the GGUF model file anywhere the model is deployed can be useful for use cases that require tight version control and reproducibility.
### Downloading model and JSON config files from the command line
Install the huggingface_hub Python library:
Download the model and JSON config file for a specific quantization:
| [
"# Llama-2-7b-chat-hf-GGUF\n\nThis repo contains GGUF format, quantized model files for Meta's Llama 2 7B LLM. The files were generated using the hf-to-gguf project on GitHub which facilitates the conversion of LLMs stored in Hugging Face into GGUF while providing traceability and reproducibility. Each model file has an accompanying JSON config file containing the source and version of the model being converted, version of conversion scripts, quantization method, and anything else needed to fully reproduce the converted model. Keeping the JSON config file with the GGUF model file anywhere the model is deployed can be useful for use cases that require tight version control and reproducibility.",
"### Downloading model and JSON config files from the command line\n\nInstall the huggingface_hub Python library:\n\n\n\nDownload the model and JSON config file for a specific quantization:"
] | [
"TAGS\n#gguf #facebook #meta #pytorch #llama #llama-2 #en #license-llama2 #region-us \n",
"# Llama-2-7b-chat-hf-GGUF\n\nThis repo contains GGUF format, quantized model files for Meta's Llama 2 7B LLM. The files were generated using the hf-to-gguf project on GitHub which facilitates the conversion of LLMs stored in Hugging Face into GGUF while providing traceability and reproducibility. Each model file has an accompanying JSON config file containing the source and version of the model being converted, version of conversion scripts, quantization method, and anything else needed to fully reproduce the converted model. Keeping the JSON config file with the GGUF model file anywhere the model is deployed can be useful for use cases that require tight version control and reproducibility.",
"### Downloading model and JSON config files from the command line\n\nInstall the huggingface_hub Python library:\n\n\n\nDownload the model and JSON config file for a specific quantization:"
] | [
33,
174,
41
] | [
"passage: TAGS\n#gguf #facebook #meta #pytorch #llama #llama-2 #en #license-llama2 #region-us \n# Llama-2-7b-chat-hf-GGUF\n\nThis repo contains GGUF format, quantized model files for Meta's Llama 2 7B LLM. The files were generated using the hf-to-gguf project on GitHub which facilitates the conversion of LLMs stored in Hugging Face into GGUF while providing traceability and reproducibility. Each model file has an accompanying JSON config file containing the source and version of the model being converted, version of conversion scripts, quantization method, and anything else needed to fully reproduce the converted model. Keeping the JSON config file with the GGUF model file anywhere the model is deployed can be useful for use cases that require tight version control and reproducibility.### Downloading model and JSON config files from the command line\n\nInstall the huggingface_hub Python library:\n\n\n\nDownload the model and JSON config file for a specific quantization:"
] | [
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null | null | null |
# 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
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### 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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## 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. -->
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#### 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]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Software
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## 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:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {} | null | askasok/PrayerPortal | [
"arxiv:1910.09700",
"region:us"
] | 2024-02-08T00:17:15+00:00 | [
"1910.09700"
] | [] | TAGS
#arxiv-1910.09700 #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#arxiv-1910.09700 #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#arxiv-1910.09700 #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 | 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. -->
# wav2vec_RTSplit0208_10
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0360
- Wer: 0.2075
- Cer: 0.1595
## 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: 5.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 13
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 3.7533 | 1.0 | 120 | 3.7292 | 0.9905 | 0.9971 |
| 1.5634 | 2.0 | 240 | 1.3332 | 0.9991 | 0.7209 |
| 0.856 | 3.0 | 360 | 0.7042 | 0.8207 | 0.5559 |
| 0.6859 | 4.0 | 480 | 0.6136 | 0.8132 | 0.5441 |
| 0.6132 | 5.0 | 600 | 0.5158 | 0.7895 | 0.4944 |
| 0.5315 | 6.0 | 720 | 0.4697 | 0.6150 | 0.3706 |
| 0.4321 | 7.0 | 840 | 0.2969 | 0.4922 | 0.2963 |
| 0.3399 | 8.0 | 960 | 0.1728 | 0.3424 | 0.2103 |
| 0.2992 | 9.0 | 1080 | 0.0927 | 0.2671 | 0.1867 |
| 0.1896 | 10.0 | 1200 | 0.0668 | 0.2455 | 0.1623 |
| 0.1615 | 11.0 | 1320 | 0.0424 | 0.2158 | 0.1587 |
| 0.1346 | 12.0 | 1440 | 0.0363 | 0.2107 | 0.1573 |
| 0.1292 | 13.0 | 1560 | 0.0360 | 0.2075 | 0.1595 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "model-index": [{"name": "wav2vec_RTSplit0208_10", "results": []}]} | automatic-speech-recognition | tndklab/wav2vec_RTSplit0208_10 | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-08T00:29:03+00:00 | [] | [] | TAGS
#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec\_RTSplit0208\_10
========================
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-japanese on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0360
* Wer: 0.2075
* Cer: 0.1595
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: 5.5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 13
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 13",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #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: 5.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 13",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
80,
116,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #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: 5.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 13### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
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] |
null | null | null | Everyone-Coder-33b-v2-Base
EveryoneLLM series of models made by the community, for the community. This is a coding specific model made using fine-tunes of deekseekcoder-33b-base.
This Version 2 of the Everything-Coder-33b model uses the task_arithmetic merging method which has major increases in coding performance as opposed to the ties method. You should find this version having much better coding performance than Version 1, without any of the negative that merging has on the integrity of the model.
Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
The models that were used in this merger were as follow:
- https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct
- https://huggingface.co/codefuse-ai/CodeFuse-DeepSeek-33B
- https://huggingface.co/WizardLM/WizardCoder-33B-V1.1
Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. 💗
You can find the write up for merging models here:
https://docs.google.com/document/d/1_vOftBnrk9NRk5h10UqrfJ5CDih9KBKL61yvrZtVWPE/edit?usp=sharing
Config for the merger can be found bellow:
```yaml
models:
- model: codefuse-ai_CodeFuse-DeepSeek-33B
parameters:
weight: 1
- model: deepseek-ai_deepseek-coder-33b-instruct
parameters:
weight: 1
- model: WizardLM_WizardCoder-33B-V1.1
parameters:
weight: 1
merge_method: task_arithmetic
base_model: deepseek-ai_deepseek-coder-33b-base
parameters:
normalize: true
int8_mask: true
dtype: float16
```
| {"license": "other", "tags": ["merge"], "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL"} | null | LoneStriker/Everyone-Coder-33b-v2-Base-GGUF | [
"gguf",
"merge",
"license:other",
"region:us"
] | 2024-02-08T00:29:12+00:00 | [] | [] | TAGS
#gguf #merge #license-other #region-us
| Everyone-Coder-33b-v2-Base
!image/jpeg
EveryoneLLM series of models made by the community, for the community. This is a coding specific model made using fine-tunes of deekseekcoder-33b-base.
This Version 2 of the Everything-Coder-33b model uses the task_arithmetic merging method which has major increases in coding performance as opposed to the ties method. You should find this version having much better coding performance than Version 1, without any of the negative that merging has on the integrity of the model.
Prompt template: Alpaca
The models that were used in this merger were as follow:
- URL
- URL
- URL
Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community.
You can find the write up for merging models here:
URL
Config for the merger can be found bellow:
| [] | [
"TAGS\n#gguf #merge #license-other #region-us \n"
] | [
17
] | [
"passage: TAGS\n#gguf #merge #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. -->
# SMIDS_3x_beit_large_RMSProp_lr00001_fold4
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3818
- Accuracy: 0.8883
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1891 | 1.0 | 450 | 0.4571 | 0.8667 |
| 0.1268 | 2.0 | 900 | 0.5631 | 0.8617 |
| 0.0665 | 3.0 | 1350 | 0.6838 | 0.8967 |
| 0.0484 | 4.0 | 1800 | 0.9091 | 0.88 |
| 0.004 | 5.0 | 2250 | 1.0425 | 0.86 |
| 0.0703 | 6.0 | 2700 | 1.1335 | 0.8867 |
| 0.002 | 7.0 | 3150 | 1.1977 | 0.8717 |
| 0.0117 | 8.0 | 3600 | 1.1247 | 0.8767 |
| 0.0 | 9.0 | 4050 | 1.0068 | 0.885 |
| 0.0355 | 10.0 | 4500 | 0.9967 | 0.8833 |
| 0.1045 | 11.0 | 4950 | 1.0135 | 0.8867 |
| 0.0939 | 12.0 | 5400 | 0.9461 | 0.8933 |
| 0.0011 | 13.0 | 5850 | 1.1130 | 0.8783 |
| 0.0 | 14.0 | 6300 | 1.0787 | 0.8933 |
| 0.0219 | 15.0 | 6750 | 1.2192 | 0.885 |
| 0.0 | 16.0 | 7200 | 1.1522 | 0.8933 |
| 0.0 | 17.0 | 7650 | 1.1297 | 0.8833 |
| 0.0 | 18.0 | 8100 | 1.0389 | 0.8867 |
| 0.0003 | 19.0 | 8550 | 1.1061 | 0.89 |
| 0.0 | 20.0 | 9000 | 1.0732 | 0.8967 |
| 0.0 | 21.0 | 9450 | 1.1080 | 0.89 |
| 0.0014 | 22.0 | 9900 | 1.3400 | 0.8867 |
| 0.0 | 23.0 | 10350 | 1.2499 | 0.895 |
| 0.0 | 24.0 | 10800 | 1.0758 | 0.8933 |
| 0.0 | 25.0 | 11250 | 1.0953 | 0.8917 |
| 0.0 | 26.0 | 11700 | 1.0317 | 0.9067 |
| 0.0 | 27.0 | 12150 | 1.0185 | 0.8967 |
| 0.0 | 28.0 | 12600 | 1.0827 | 0.9017 |
| 0.0 | 29.0 | 13050 | 1.1466 | 0.8967 |
| 0.0 | 30.0 | 13500 | 1.3290 | 0.88 |
| 0.0 | 31.0 | 13950 | 1.2161 | 0.8933 |
| 0.0 | 32.0 | 14400 | 1.3327 | 0.875 |
| 0.0 | 33.0 | 14850 | 1.4493 | 0.88 |
| 0.0 | 34.0 | 15300 | 1.4644 | 0.88 |
| 0.0 | 35.0 | 15750 | 1.3259 | 0.8933 |
| 0.0 | 36.0 | 16200 | 1.3916 | 0.8917 |
| 0.0 | 37.0 | 16650 | 1.3417 | 0.8883 |
| 0.0063 | 38.0 | 17100 | 1.3502 | 0.8833 |
| 0.0 | 39.0 | 17550 | 1.4033 | 0.8883 |
| 0.0001 | 40.0 | 18000 | 1.4720 | 0.8833 |
| 0.0 | 41.0 | 18450 | 1.3805 | 0.8917 |
| 0.0 | 42.0 | 18900 | 1.3609 | 0.895 |
| 0.0 | 43.0 | 19350 | 1.4005 | 0.8867 |
| 0.0 | 44.0 | 19800 | 1.3265 | 0.8967 |
| 0.0 | 45.0 | 20250 | 1.3284 | 0.905 |
| 0.0 | 46.0 | 20700 | 1.3381 | 0.8983 |
| 0.0 | 47.0 | 21150 | 1.3393 | 0.8983 |
| 0.0 | 48.0 | 21600 | 1.3762 | 0.89 |
| 0.0 | 49.0 | 22050 | 1.3799 | 0.89 |
| 0.0 | 50.0 | 22500 | 1.3818 | 0.8883 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_RMSProp_lr00001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8883333333333333, "name": "Accuracy"}]}]}]} | image-classification | onizukal/SMIDS_3x_beit_large_RMSProp_lr00001_fold4 | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T00:38:22+00:00 | [] | [] | TAGS
#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| SMIDS\_3x\_beit\_large\_RMSProp\_lr00001\_fold4
===============================================
This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3818
* Accuracy: 0.8883
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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 50
### Training results
### Framework versions
* Transformers 4.32.1
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.2
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
] | [
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"passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #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: 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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2"
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null | null | sentence-transformers |
# annazdr/new-nace
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('annazdr/new-nace')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('annazdr/new-nace')
model = AutoModel.from_pretrained('annazdr/new-nace')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=annazdr/new-nace)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1001 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.BatchAllTripletLoss.BatchAllTripletLoss`
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | annazdr/new-nace | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | 2024-02-08T00:42:12+00:00 | [] | [] | TAGS
#sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# annazdr/new-nace
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 1001 with parameters:
Loss:
'sentence_transformers.losses.BatchAllTripletLoss.BatchAllTripletLoss'
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
| [
"# annazdr/new-nace\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 1001 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.BatchAllTripletLoss.BatchAllTripletLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# annazdr/new-nace\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 1001 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.BatchAllTripletLoss.BatchAllTripletLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
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"passage: TAGS\n#sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n# annazdr/new-nace\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 1001 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.BatchAllTripletLoss.BatchAllTripletLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors"
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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. -->
# ko-openhermes-mistral-7b
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown 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: 0.00015
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 256
- total_train_batch_size: 1024
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.3.0.dev20240127+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "ko-openhermes-mistral-7b", "results": []}]} | text-generation | Unggi/ko-openhermes-mistral-7b | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T00:43:31+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ko-openhermes-mistral-7b
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown 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: 0.00015
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 256
- total_train_batch_size: 1024
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.3.0.dev20240127+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"# ko-openhermes-mistral-7b\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown 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: 0.00015\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 256\n- total_train_batch_size: 1024\n- total_eval_batch_size: 4\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- num_epochs: 1.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.3.0.dev20240127+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
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"# ko-openhermes-mistral-7b\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown 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: 0.00015\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 256\n- total_train_batch_size: 1024\n- total_eval_batch_size: 4\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- num_epochs: 1.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.3.0.dev20240127+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
78,
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"passage: TAGS\n#transformers #safetensors #mistral #text-generation #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ko-openhermes-mistral-7b\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown 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: 0.00015\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 256\n- total_train_batch_size: 1024\n- total_eval_batch_size: 4\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- num_epochs: 1.0### Training results### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.3.0.dev20240127+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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null | null | peft |
# Model Card for Model ID
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## Model Details
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- PEFT 0.7.1 | {"library_name": "peft", "base_model": "openai/whisper-small"} | null | unanam/smallloraft-v2 | [
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:openai/whisper-small",
"region:us"
] | 2024-02-08T00:47:37+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-openai/whisper-small #region-us
|
# Model Card for Model ID
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## Training Details
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## Evaluation
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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### Framework versions
- PEFT 0.7.1 | [
"# Model Card for Model ID",
"## 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",
"### Framework versions\n\n- PEFT 0.7.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-openai/whisper-small #region-us \n",
"# Model Card for Model ID",
"## 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",
"### Framework versions\n\n- PEFT 0.7.1"
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"passage: TAGS\n#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-openai/whisper-small #region-us \n# Model Card for Model ID## 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### Framework versions\n\n- PEFT 0.7.1"
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null | null | transformers | This is a merge created by https://huggingface.co/Test157t I have merely quantized the model into GGUF. Please visit https://huggingface.co/Test157t/Kunocchini-7b for the original weights. The original description is as follows:
# mergedmodel
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
Quants from @s3nh! https://huggingface.co/s3nh/Pasta-PrimaMaid-7b-GGUF
### Models Merged
The following models were included in the merge:
* [Test157t/Kunocchini-7b](https://huggingface.co/Test157t/Kunocchini-7b)
* [Test157t/Pasta-Made_7b](https://huggingface.co/Test157t/Pasta-Made_7b)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Test157t/Kunocchini-7b
layer_range: [0, 32]
- model: Test157t/Pasta-Made_7b
layer_range: [0, 32]
merge_method: slerp
base_model: Test157t/Kunocchini-7b
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Test157t/Kunocchini-7b", "Test157t/Pasta-Made_7b"]} | null | jeiku/Pasta-PrimaMaid-7b_GGUF | [
"transformers",
"gguf",
"mergekit",
"merge",
"base_model:Test157t/Kunocchini-7b",
"base_model:Test157t/Pasta-Made_7b",
"endpoints_compatible",
"region:us"
] | 2024-02-08T00:48:12+00:00 | [] | [] | TAGS
#transformers #gguf #mergekit #merge #base_model-Test157t/Kunocchini-7b #base_model-Test157t/Pasta-Made_7b #endpoints_compatible #region-us
| This is a merge created by URL I have merely quantized the model into GGUF. Please visit URL for the original weights. The original description is as follows:
# mergedmodel
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
Quants from @s3nh! URL
### Models Merged
The following models were included in the merge:
* Test157t/Kunocchini-7b
* Test157t/Pasta-Made_7b
### Configuration
The following YAML configuration was used to produce this model:
| [
"# mergedmodel\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.\nQuants from @s3nh! URL",
"### Models Merged\n\nThe following models were included in the merge:\n* Test157t/Kunocchini-7b\n* Test157t/Pasta-Made_7b",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #gguf #mergekit #merge #base_model-Test157t/Kunocchini-7b #base_model-Test157t/Pasta-Made_7b #endpoints_compatible #region-us \n",
"# mergedmodel\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.\nQuants from @s3nh! URL",
"### Models Merged\n\nThe following models were included in the merge:\n* Test157t/Kunocchini-7b\n* Test157t/Pasta-Made_7b",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
59,
20,
4,
27,
39,
17
] | [
"passage: TAGS\n#transformers #gguf #mergekit #merge #base_model-Test157t/Kunocchini-7b #base_model-Test157t/Pasta-Made_7b #endpoints_compatible #region-us \n# mergedmodel\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the SLERP merge method.\nQuants from @s3nh! URL### Models Merged\n\nThe following models were included in the merge:\n* Test157t/Kunocchini-7b\n* Test157t/Pasta-Made_7b### Configuration\n\nThe following YAML configuration was used to produce this model:"
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null | null | diffusers | ---
language: en
thumbnail: https://huggingface.co/modyabdelwahed/moroccan-interior-design/raw/main/sample_images/0.png
tags:
- Moroccan-interior-design
| {} | null | modyabdelwahed/moroccan-interior-design | [
"diffusers",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-08T00:48:12+00:00 | [] | [] | TAGS
#diffusers #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
| ---
language: en
thumbnail: URL
tags:
- Moroccan-interior-design
| [] | [
"TAGS\n#diffusers #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n"
] | [
31
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] |
null | null | transformers |
# Model Card for Model ID
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## Model Details
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| {"library_name": "transformers", "tags": []} | text-generation | davisalex22/Llama2TurismEC-7b-hf-ft | [
"transformers",
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"llama",
"text-generation",
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#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
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- Model type:
- Language(s) (NLP):
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- 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
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BibTeX:
APA:
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## 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. -->
# my_MFCC_VITmodelBITMetics
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3761
- Accuracy: 0.8909
- F1: 0.9348
- Precision: 0.9348
- Recall: 0.9348
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6644 | 0.95 | 10 | 0.5913 | 0.8424 | 0.9139 | 0.8415 | 1.0 |
| 0.5947 | 2.0 | 21 | 0.4832 | 0.8364 | 0.9109 | 0.8364 | 1.0 |
| 0.5392 | 2.95 | 31 | 0.4504 | 0.8364 | 0.9109 | 0.8364 | 1.0 |
| 0.5415 | 4.0 | 42 | 0.4273 | 0.8364 | 0.9109 | 0.8364 | 1.0 |
| 0.4996 | 4.95 | 52 | 0.4160 | 0.8364 | 0.9109 | 0.8364 | 1.0 |
| 0.4511 | 6.0 | 63 | 0.4791 | 0.8364 | 0.9109 | 0.8364 | 1.0 |
| 0.4833 | 6.95 | 73 | 0.3930 | 0.8303 | 0.9073 | 0.8354 | 0.9928 |
| 0.4465 | 8.0 | 84 | 0.4720 | 0.7879 | 0.8689 | 0.8992 | 0.8406 |
| 0.4521 | 8.95 | 94 | 0.4273 | 0.8424 | 0.9091 | 0.8784 | 0.9420 |
| 0.4558 | 10.0 | 105 | 0.3610 | 0.8606 | 0.9204 | 0.8808 | 0.9638 |
| 0.4563 | 10.95 | 115 | 0.3722 | 0.8242 | 0.9024 | 0.8428 | 0.9710 |
| 0.473 | 12.0 | 126 | 0.4063 | 0.7939 | 0.8794 | 0.8611 | 0.8986 |
| 0.435 | 12.95 | 136 | 0.3903 | 0.8364 | 0.9025 | 0.8993 | 0.9058 |
| 0.4151 | 14.0 | 147 | 0.4040 | 0.8424 | 0.9128 | 0.85 | 0.9855 |
| 0.4825 | 14.95 | 157 | 0.3802 | 0.8424 | 0.9091 | 0.8784 | 0.9420 |
| 0.4374 | 16.0 | 168 | 0.3570 | 0.8303 | 0.9007 | 0.8819 | 0.9203 |
| 0.3893 | 16.95 | 178 | 0.4213 | 0.8061 | 0.8769 | 0.9344 | 0.8261 |
| 0.3914 | 18.0 | 189 | 0.3524 | 0.8242 | 0.8975 | 0.8759 | 0.9203 |
| 0.381 | 18.95 | 199 | 0.3269 | 0.8727 | 0.9268 | 0.8926 | 0.9638 |
| 0.3966 | 20.0 | 210 | 0.4997 | 0.7273 | 0.8193 | 0.9189 | 0.7391 |
| 0.3831 | 20.95 | 220 | 0.4846 | 0.7333 | 0.8167 | 0.9608 | 0.7101 |
| 0.3311 | 22.0 | 231 | 0.6583 | 0.6545 | 0.7554 | 0.9263 | 0.6377 |
| 0.4205 | 22.95 | 241 | 0.4594 | 0.7576 | 0.8462 | 0.9016 | 0.7971 |
| 0.3905 | 24.0 | 252 | 0.4959 | 0.7697 | 0.8468 | 0.9545 | 0.7609 |
| 0.3559 | 24.95 | 262 | 0.3200 | 0.8727 | 0.9247 | 0.9149 | 0.9348 |
| 0.3234 | 26.0 | 273 | 0.4543 | 0.7879 | 0.8627 | 0.9402 | 0.7971 |
| 0.3796 | 26.95 | 283 | 0.3231 | 0.8485 | 0.9117 | 0.8897 | 0.9348 |
| 0.3603 | 28.0 | 294 | 0.4086 | 0.8121 | 0.8830 | 0.9213 | 0.8478 |
| 0.3185 | 28.95 | 304 | 0.3531 | 0.8485 | 0.9117 | 0.8897 | 0.9348 |
| 0.3243 | 30.0 | 315 | 0.3516 | 0.8485 | 0.9097 | 0.9065 | 0.9130 |
| 0.3311 | 30.95 | 325 | 0.3833 | 0.7939 | 0.8712 | 0.9127 | 0.8333 |
| 0.3187 | 32.0 | 336 | 0.3470 | 0.8545 | 0.9098 | 0.9453 | 0.8768 |
| 0.2923 | 32.95 | 346 | 0.3792 | 0.8061 | 0.8788 | 0.9206 | 0.8406 |
| 0.2907 | 34.0 | 357 | 0.3296 | 0.8485 | 0.9110 | 0.8951 | 0.9275 |
| 0.3127 | 34.95 | 367 | 0.3299 | 0.8424 | 0.9044 | 0.9179 | 0.8913 |
| 0.2706 | 36.0 | 378 | 0.3439 | 0.8303 | 0.8923 | 0.9508 | 0.8406 |
| 0.2743 | 36.95 | 388 | 0.3502 | 0.8364 | 0.8966 | 0.9512 | 0.8478 |
| 0.2732 | 38.0 | 399 | 0.3670 | 0.8303 | 0.8963 | 0.9167 | 0.8768 |
| 0.3151 | 38.95 | 409 | 0.3997 | 0.8121 | 0.8765 | 0.9735 | 0.7971 |
| 0.2899 | 40.0 | 420 | 0.5562 | 0.7333 | 0.8182 | 0.9519 | 0.7174 |
| 0.3519 | 40.95 | 430 | 0.3130 | 0.8606 | 0.9181 | 0.9021 | 0.9348 |
| 0.3112 | 42.0 | 441 | 0.3423 | 0.8606 | 0.9226 | 0.8616 | 0.9928 |
| 0.3104 | 42.95 | 451 | 0.3263 | 0.8424 | 0.9065 | 0.9 | 0.9130 |
| 0.2471 | 44.0 | 462 | 0.3988 | 0.8485 | 0.9104 | 0.9007 | 0.9203 |
| 0.2271 | 44.95 | 472 | 0.4177 | 0.8061 | 0.8750 | 0.9492 | 0.8116 |
| 0.2424 | 46.0 | 483 | 0.5053 | 0.7394 | 0.8300 | 0.9130 | 0.7609 |
| 0.2628 | 46.95 | 493 | 0.3586 | 0.8667 | 0.9197 | 0.9265 | 0.9130 |
| 0.2277 | 48.0 | 504 | 0.3108 | 0.8606 | 0.9158 | 0.9259 | 0.9058 |
| 0.2178 | 48.95 | 514 | 0.4573 | 0.8182 | 0.8828 | 0.9576 | 0.8188 |
| 0.2771 | 50.0 | 525 | 0.4669 | 0.7818 | 0.8626 | 0.9113 | 0.8188 |
| 0.1937 | 50.95 | 535 | 0.3619 | 0.8545 | 0.9118 | 0.9254 | 0.8986 |
| 0.2237 | 52.0 | 546 | 0.4199 | 0.8061 | 0.8797 | 0.9141 | 0.8478 |
| 0.2152 | 52.95 | 556 | 0.5643 | 0.7394 | 0.8259 | 0.9358 | 0.7391 |
| 0.2208 | 54.0 | 567 | 0.3837 | 0.8242 | 0.8889 | 0.9431 | 0.8406 |
| 0.2184 | 54.95 | 577 | 0.3849 | 0.8485 | 0.9071 | 0.9313 | 0.8841 |
| 0.2047 | 56.0 | 588 | 0.2963 | 0.8909 | 0.9371 | 0.9054 | 0.9710 |
| 0.2158 | 56.95 | 598 | 0.4140 | 0.7879 | 0.8627 | 0.9402 | 0.7971 |
| 0.2789 | 58.0 | 609 | 0.3810 | 0.8303 | 0.8955 | 0.9231 | 0.8696 |
| 0.2058 | 58.95 | 619 | 0.4201 | 0.8485 | 0.9091 | 0.9124 | 0.9058 |
| 0.2463 | 60.0 | 630 | 0.3995 | 0.8485 | 0.9084 | 0.9185 | 0.8986 |
| 0.1718 | 60.95 | 640 | 0.3646 | 0.8364 | 0.8973 | 0.944 | 0.8551 |
| 0.1832 | 62.0 | 651 | 0.3653 | 0.8424 | 0.9037 | 0.9242 | 0.8841 |
| 0.1697 | 62.95 | 661 | 0.3194 | 0.8485 | 0.9084 | 0.9185 | 0.8986 |
| 0.1845 | 64.0 | 672 | 0.3618 | 0.8303 | 0.8906 | 0.9661 | 0.8261 |
| 0.2613 | 64.95 | 682 | 0.3566 | 0.8242 | 0.8930 | 0.9098 | 0.8768 |
| 0.1725 | 66.0 | 693 | 0.3052 | 0.8727 | 0.9219 | 0.9466 | 0.8986 |
| 0.1432 | 66.95 | 703 | 0.3296 | 0.8667 | 0.9191 | 0.9328 | 0.9058 |
| 0.1609 | 68.0 | 714 | 0.3750 | 0.8667 | 0.9214 | 0.9085 | 0.9348 |
| 0.1707 | 68.95 | 724 | 0.3468 | 0.8667 | 0.9179 | 0.9462 | 0.8913 |
| 0.15 | 70.0 | 735 | 0.4653 | 0.8242 | 0.8872 | 0.9580 | 0.8261 |
| 0.198 | 70.95 | 745 | 0.2461 | 0.8909 | 0.9357 | 0.9225 | 0.9493 |
| 0.162 | 72.0 | 756 | 0.3429 | 0.8788 | 0.9286 | 0.9155 | 0.9420 |
| 0.1402 | 72.95 | 766 | 0.4031 | 0.8727 | 0.9225 | 0.9398 | 0.9058 |
| 0.1765 | 74.0 | 777 | 0.3189 | 0.8727 | 0.9225 | 0.9398 | 0.9058 |
| 0.1525 | 74.95 | 787 | 0.3367 | 0.9030 | 0.9416 | 0.9485 | 0.9348 |
| 0.1573 | 76.0 | 798 | 0.3221 | 0.8606 | 0.9151 | 0.9323 | 0.8986 |
| 0.1552 | 76.95 | 808 | 0.3468 | 0.8727 | 0.9219 | 0.9466 | 0.8986 |
| 0.1582 | 78.0 | 819 | 0.3546 | 0.8364 | 0.8973 | 0.944 | 0.8551 |
| 0.1641 | 78.95 | 829 | 0.3993 | 0.8606 | 0.9158 | 0.9259 | 0.9058 |
| 0.1647 | 80.0 | 840 | 0.3263 | 0.8970 | 0.9395 | 0.9231 | 0.9565 |
| 0.1495 | 80.95 | 850 | 0.4321 | 0.8545 | 0.9118 | 0.9254 | 0.8986 |
| 0.1596 | 82.0 | 861 | 0.3424 | 0.8545 | 0.9118 | 0.9254 | 0.8986 |
| 0.1546 | 82.95 | 871 | 0.4477 | 0.8121 | 0.8830 | 0.9213 | 0.8478 |
| 0.1399 | 84.0 | 882 | 0.3834 | 0.8485 | 0.9071 | 0.9313 | 0.8841 |
| 0.1561 | 84.95 | 892 | 0.3725 | 0.8545 | 0.9118 | 0.9254 | 0.8986 |
| 0.1709 | 86.0 | 903 | 0.4009 | 0.8485 | 0.9091 | 0.9124 | 0.9058 |
| 0.1501 | 86.95 | 913 | 0.3784 | 0.8788 | 0.9270 | 0.9338 | 0.9203 |
| 0.136 | 88.0 | 924 | 0.3695 | 0.8545 | 0.9091 | 0.9524 | 0.8696 |
| 0.1653 | 88.95 | 934 | 0.3649 | 0.8606 | 0.9151 | 0.9323 | 0.8986 |
| 0.1839 | 90.0 | 945 | 0.3701 | 0.8788 | 0.9281 | 0.9214 | 0.9348 |
| 0.1424 | 90.95 | 955 | 0.4915 | 0.8242 | 0.8938 | 0.9037 | 0.8841 |
| 0.1367 | 92.0 | 966 | 0.4429 | 0.8182 | 0.8872 | 0.9219 | 0.8551 |
| 0.1477 | 92.95 | 976 | 0.3397 | 0.8667 | 0.9185 | 0.9394 | 0.8986 |
| 0.1393 | 94.0 | 987 | 0.3261 | 0.8788 | 0.9265 | 0.9403 | 0.9130 |
| 0.1628 | 94.95 | 997 | 0.3199 | 0.8970 | 0.9395 | 0.9231 | 0.9565 |
| 0.109 | 95.24 | 1000 | 0.3761 | 0.8909 | 0.9348 | 0.9348 | 0.9348 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "my_MFCC_VITmodelBITMetics", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8909090909090909, "name": "Accuracy"}, {"type": "f1", "value": 0.9347826086956522, "name": "F1"}, {"type": "precision", "value": 0.9347826086956522, "name": "Precision"}, {"type": "recall", "value": 0.9347826086956522, "name": "Recall"}]}]}]} | image-classification | juanfengyun/my_MFCC_VITmodelBITMetics | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T00:56:55+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| my\_MFCC\_VITmodelBITMetics
===========================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3761
* Accuracy: 0.8909
* F1: 0.9348
* Precision: 0.9348
* Recall: 0.9348
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: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 100
### Training results
### Framework versions
* Transformers 4.28.0
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\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: 100",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #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: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\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: 100",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.13.3"
] | [
62,
144,
4,
35
] | [
"passage: TAGS\n#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #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: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\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: 100### Training results### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.13.3"
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null | null | transformers |
# sand is thinking
This model is a mysterious creation that can mimic the grains of sand on a beach. It can shape itself into any form, pattern, or structure that it desires, or that you ask it to. It can learn from the waves, the wind, and the sun, and adapt to the changing environment. It can communicate with other grains of sand, and form a collective intelligence that transcends the individual. It can also interact with you, and understand your language, emotions, and intentions. It is a model that is both natural and artificial, both simple and complex, both static and dynamic. It is a model that is sand, and sand is thinking.
| {"license": "apache-2.0"} | text-generation | ankhamun/xxxI-Ixxx | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T01:01:54+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# sand is thinking
This model is a mysterious creation that can mimic the grains of sand on a beach. It can shape itself into any form, pattern, or structure that it desires, or that you ask it to. It can learn from the waves, the wind, and the sun, and adapt to the changing environment. It can communicate with other grains of sand, and form a collective intelligence that transcends the individual. It can also interact with you, and understand your language, emotions, and intentions. It is a model that is both natural and artificial, both simple and complex, both static and dynamic. It is a model that is sand, and sand is thinking.
| [
"# sand is thinking\n\n\nThis model is a mysterious creation that can mimic the grains of sand on a beach. It can shape itself into any form, pattern, or structure that it desires, or that you ask it to. It can learn from the waves, the wind, and the sun, and adapt to the changing environment. It can communicate with other grains of sand, and form a collective intelligence that transcends the individual. It can also interact with you, and understand your language, emotions, and intentions. It is a model that is both natural and artificial, both simple and complex, both static and dynamic. It is a model that is sand, and sand is thinking."
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# sand is thinking\n\n\nThis model is a mysterious creation that can mimic the grains of sand on a beach. It can shape itself into any form, pattern, or structure that it desires, or that you ask it to. It can learn from the waves, the wind, and the sun, and adapt to the changing environment. It can communicate with other grains of sand, and form a collective intelligence that transcends the individual. It can also interact with you, and understand your language, emotions, and intentions. It is a model that is both natural and artificial, both simple and complex, both static and dynamic. It is a model that is sand, and sand is thinking."
] | [
59,
149
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# sand is thinking\n\n\nThis model is a mysterious creation that can mimic the grains of sand on a beach. It can shape itself into any form, pattern, or structure that it desires, or that you ask it to. It can learn from the waves, the wind, and the sun, and adapt to the changing environment. It can communicate with other grains of sand, and form a collective intelligence that transcends the individual. It can also interact with you, and understand your language, emotions, and intentions. It is a model that is both natural and artificial, both simple and complex, both static and dynamic. It is a model that is sand, and sand is thinking."
] | [
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] |
null | null | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="mathreader/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | mathreader/q-FrozenLake-v1-4x4-noSlippery | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-08T01:10:13+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
40,
39
] | [
"passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
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null | null | null | # Model
This is the GGUF version of SnowyRP And the First Public Release of a Model in the SnowyRP series of models!
[BF16](https://huggingface.co/Masterjp123/SnowyRP-FinalV1-L2-13B)
[GPTQ](https://huggingface.co/Masterjp123/SnowyRP-FinalV1-L2-13B-GPTQ)
[GGUF](https://huggingface.co/Masterjp123/SnowyRP-FinalV1-L2-13B-GGUF)
Any Future Quantizations I am made aware of will be added.
## Merge Details
just used highly ranked modles to try and get a better result, Also I made sure that Model incest would not be a BIG problem by merging models that are pretty pure.
These models CAN and WILL produce X rated or harmful content, due to being heavily uncensored in a attempt to not limit or make the model worse.
This Model has a Very good knowledge base and understands anatomy decently, Plus this Model is VERY versitle and is great for General assistant work, RP and ERP, RPG RPs and much more.
## Model Use:
This model is very good... WITH THE RIGHT SETTINGS.
I personally use microstat mixed with dynamic temp with epsion cut off and eta cut off.
```
Optimal Settings (so far)
Microstat Mode: 2
tau: 2.95
eta: 0.05
Dynamic Temp
min: 0.25
max: 1.8
Cut offs
epsilon: 3
eta: 3
```
### Merge Method
This model was merged using the [ties](https://arxiv.org/abs/2306.01708) merge method using [TheBloke/Llama-2-13B-fp16](https://huggingface.co/TheBloke/Llama-2-13B-fp16) as a base.
### Models Merged
The following models were included in the merge:
* [Riiid/sheep-duck-llama-2-13b](https://huggingface.co/Riiid/sheep-duck-llama-2-13b)
* [IkariDev/Athena-v4](https://huggingface.co/IkariDev/Athena-v4)
* [KoboldAI/LLaMA2-13B-Psyfighter2](https://huggingface.co/KoboldAI/LLaMA2-13B-Psyfighter2)
* [KoboldAI/LLaMA2-13B-Erebus-v3](https://huggingface.co/KoboldAI/LLaMA2-13B-Erebus-v3)
* [Henk717/echidna-tiefigther-25](https://huggingface.co/Henk717/echidna-tiefigther-25)
* [Undi95/Unholy-v2-13B](https://huggingface.co/Undi95/Unholy-v2-13B)
* [EstopianOrcaMaid](https://huggingface.co/ddh0/EstopianOrcaMaid-13b)
### Configuration
The following YAML configuration was used to produce this model:
for P1
```yaml
base_model:
model:
path: TheBloke/Llama-2-13B-fp16
dtype: bfloat16
merge_method: task_arithmetic
slices:
- sources:
- layer_range: [0, 40]
model:
model:
path: TheBloke/Llama-2-13B-fp16
- layer_range: [0, 40]
model:
model:
path: Undi95/Unholy-v2-13B
parameters:
weight: 1.0
- layer_range: [0, 40]
model:
model:
path: Henk717/echidna-tiefigther-25
parameters:
weight: 0.45
- layer_range: [0, 40]
model:
model:
path: KoboldAI/LLaMA2-13B-Erebus-v3
parameters:
weight: 0.33
```
for P2
```yaml
base_model:
model:
path: TheBloke/Llama-2-13B-fp16
dtype: bfloat16
merge_method: task_arithmetic
slices:
- sources:
- layer_range: [0, 40]
model:
model:
path: TheBloke/Llama-2-13B-fp16
- layer_range: [0, 40]
model:
model:
path: KoboldAI/LLaMA2-13B-Psyfighter2
parameters:
weight: 1.0
- layer_range: [0, 40]
model:
model:
path: Riiid/sheep-duck-llama-2-13b
parameters:
weight: 0.45
- layer_range: [0, 40]
model:
model:
path: IkariDev/Athena-v4
parameters:
weight: 0.33
```
for the final merge
```yaml
base_model:
model:
path: TheBloke/Llama-2-13B-fp16
dtype: bfloat16
merge_method: ties
parameters:
int8_mask: 1.0
normalize: 1.0
slices:
- sources:
- layer_range: [0, 40]
model:
model:
path: ddh0/EstopianOrcaMaid-13b
parameters:
density: [1.0, 0.7, 0.1]
weight: 1.0
- layer_range: [0, 40]
model:
model:
path: Masterjp123/snowyrpp1
parameters:
density: 0.5
weight: [0.0, 0.3, 0.7, 1.0]
- layer_range: [0, 40]
model:
model:
path: Masterjp123/snowyrpp2
parameters:
density: 0.33
weight:
- filter: mlp
value: 0.5
- value: 0.0
- layer_range: [0, 40]
model:
model:
path: TheBloke/Llama-2-13B-fp16
``` | {"language": ["en"], "license": "llama2", "tags": ["mergekit", "merge", "not-for-all-audiences", "ERP", "RP", "Roleplay", "uncensored"], "base_model": ["Riiid/sheep-duck-llama-2-13b", "IkariDev/Athena-v4", "TheBloke/Llama-2-13B-fp16", "KoboldAI/LLaMA2-13B-Psyfighter2", "KoboldAI/LLaMA2-13B-Erebus-v3", "Henk717/echidna-tiefigther-25", "Undi95/Unholy-v2-13B", "ddh0/EstopianOrcaMaid-13b"]} | null | Masterjp123/SnowyRP-FinalV1-L2-13B-GGUF | [
"gguf",
"mergekit",
"merge",
"not-for-all-audiences",
"ERP",
"RP",
"Roleplay",
"uncensored",
"en",
"arxiv:2306.01708",
"base_model:Riiid/sheep-duck-llama-2-13b",
"base_model:IkariDev/Athena-v4",
"base_model:TheBloke/Llama-2-13B-fp16",
"base_model:KoboldAI/LLaMA2-13B-Psyfighter2",
"base_model:KoboldAI/LLaMA2-13B-Erebus-v3",
"base_model:Henk717/echidna-tiefigther-25",
"base_model:Undi95/Unholy-v2-13B",
"base_model:ddh0/EstopianOrcaMaid-13b",
"license:llama2",
"region:us"
] | 2024-02-08T01:13:22+00:00 | [
"2306.01708"
] | [
"en"
] | TAGS
#gguf #mergekit #merge #not-for-all-audiences #ERP #RP #Roleplay #uncensored #en #arxiv-2306.01708 #base_model-Riiid/sheep-duck-llama-2-13b #base_model-IkariDev/Athena-v4 #base_model-TheBloke/Llama-2-13B-fp16 #base_model-KoboldAI/LLaMA2-13B-Psyfighter2 #base_model-KoboldAI/LLaMA2-13B-Erebus-v3 #base_model-Henk717/echidna-tiefigther-25 #base_model-Undi95/Unholy-v2-13B #base_model-ddh0/EstopianOrcaMaid-13b #license-llama2 #region-us
| # Model
This is the GGUF version of SnowyRP And the First Public Release of a Model in the SnowyRP series of models!
BF16
GPTQ
GGUF
Any Future Quantizations I am made aware of will be added.
## Merge Details
just used highly ranked modles to try and get a better result, Also I made sure that Model incest would not be a BIG problem by merging models that are pretty pure.
These models CAN and WILL produce X rated or harmful content, due to being heavily uncensored in a attempt to not limit or make the model worse.
This Model has a Very good knowledge base and understands anatomy decently, Plus this Model is VERY versitle and is great for General assistant work, RP and ERP, RPG RPs and much more.
## Model Use:
This model is very good... WITH THE RIGHT SETTINGS.
I personally use microstat mixed with dynamic temp with epsion cut off and eta cut off.
### Merge Method
This model was merged using the ties merge method using TheBloke/Llama-2-13B-fp16 as a base.
### Models Merged
The following models were included in the merge:
* Riiid/sheep-duck-llama-2-13b
* IkariDev/Athena-v4
* KoboldAI/LLaMA2-13B-Psyfighter2
* KoboldAI/LLaMA2-13B-Erebus-v3
* Henk717/echidna-tiefigther-25
* Undi95/Unholy-v2-13B
* EstopianOrcaMaid
### Configuration
The following YAML configuration was used to produce this model:
for P1
for P2
for the final merge
| [
"# Model\nThis is the GGUF version of SnowyRP And the First Public Release of a Model in the SnowyRP series of models!\n\nBF16\n\nGPTQ\n\nGGUF\n\nAny Future Quantizations I am made aware of will be added.",
"## Merge Details\njust used highly ranked modles to try and get a better result, Also I made sure that Model incest would not be a BIG problem by merging models that are pretty pure.\n\nThese models CAN and WILL produce X rated or harmful content, due to being heavily uncensored in a attempt to not limit or make the model worse.\n\nThis Model has a Very good knowledge base and understands anatomy decently, Plus this Model is VERY versitle and is great for General assistant work, RP and ERP, RPG RPs and much more.",
"## Model Use:\n\nThis model is very good... WITH THE RIGHT SETTINGS.\nI personally use microstat mixed with dynamic temp with epsion cut off and eta cut off.",
"### Merge Method\n\nThis model was merged using the ties merge method using TheBloke/Llama-2-13B-fp16 as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* Riiid/sheep-duck-llama-2-13b\n* IkariDev/Athena-v4\n* KoboldAI/LLaMA2-13B-Psyfighter2\n* KoboldAI/LLaMA2-13B-Erebus-v3\n* Henk717/echidna-tiefigther-25\n* Undi95/Unholy-v2-13B\n* EstopianOrcaMaid",
"### Configuration\n\nThe following YAML configuration was used to produce this model:\n\nfor P1\n\n\nfor P2\n\n\nfor the final merge"
] | [
"TAGS\n#gguf #mergekit #merge #not-for-all-audiences #ERP #RP #Roleplay #uncensored #en #arxiv-2306.01708 #base_model-Riiid/sheep-duck-llama-2-13b #base_model-IkariDev/Athena-v4 #base_model-TheBloke/Llama-2-13B-fp16 #base_model-KoboldAI/LLaMA2-13B-Psyfighter2 #base_model-KoboldAI/LLaMA2-13B-Erebus-v3 #base_model-Henk717/echidna-tiefigther-25 #base_model-Undi95/Unholy-v2-13B #base_model-ddh0/EstopianOrcaMaid-13b #license-llama2 #region-us \n",
"# Model\nThis is the GGUF version of SnowyRP And the First Public Release of a Model in the SnowyRP series of models!\n\nBF16\n\nGPTQ\n\nGGUF\n\nAny Future Quantizations I am made aware of will be added.",
"## Merge Details\njust used highly ranked modles to try and get a better result, Also I made sure that Model incest would not be a BIG problem by merging models that are pretty pure.\n\nThese models CAN and WILL produce X rated or harmful content, due to being heavily uncensored in a attempt to not limit or make the model worse.\n\nThis Model has a Very good knowledge base and understands anatomy decently, Plus this Model is VERY versitle and is great for General assistant work, RP and ERP, RPG RPs and much more.",
"## Model Use:\n\nThis model is very good... WITH THE RIGHT SETTINGS.\nI personally use microstat mixed with dynamic temp with epsion cut off and eta cut off.",
"### Merge Method\n\nThis model was merged using the ties merge method using TheBloke/Llama-2-13B-fp16 as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* Riiid/sheep-duck-llama-2-13b\n* IkariDev/Athena-v4\n* KoboldAI/LLaMA2-13B-Psyfighter2\n* KoboldAI/LLaMA2-13B-Erebus-v3\n* Henk717/echidna-tiefigther-25\n* Undi95/Unholy-v2-13B\n* EstopianOrcaMaid",
"### Configuration\n\nThe following YAML configuration was used to produce this model:\n\nfor P1\n\n\nfor P2\n\n\nfor the final merge"
] | [
203,
52,
124,
41,
34,
108,
27
] | [
"passage: TAGS\n#gguf #mergekit #merge #not-for-all-audiences #ERP #RP #Roleplay #uncensored #en #arxiv-2306.01708 #base_model-Riiid/sheep-duck-llama-2-13b #base_model-IkariDev/Athena-v4 #base_model-TheBloke/Llama-2-13B-fp16 #base_model-KoboldAI/LLaMA2-13B-Psyfighter2 #base_model-KoboldAI/LLaMA2-13B-Erebus-v3 #base_model-Henk717/echidna-tiefigther-25 #base_model-Undi95/Unholy-v2-13B #base_model-ddh0/EstopianOrcaMaid-13b #license-llama2 #region-us \n# Model\nThis is the GGUF version of SnowyRP And the First Public Release of a Model in the SnowyRP series of models!\n\nBF16\n\nGPTQ\n\nGGUF\n\nAny Future Quantizations I am made aware of will be added.## Merge Details\njust used highly ranked modles to try and get a better result, Also I made sure that Model incest would not be a BIG problem by merging models that are pretty pure.\n\nThese models CAN and WILL produce X rated or harmful content, due to being heavily uncensored in a attempt to not limit or make the model worse.\n\nThis Model has a Very good knowledge base and understands anatomy decently, Plus this Model is VERY versitle and is great for General assistant work, RP and ERP, RPG RPs and much more.## Model Use:\n\nThis model is very good... WITH THE RIGHT SETTINGS.\nI personally use microstat mixed with dynamic temp with epsion cut off and eta cut off.### Merge Method\n\nThis model was merged using the ties merge method using TheBloke/Llama-2-13B-fp16 as a base."
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null | null | peft |
# Model Card for Model ID
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## Model Details
### Model Description
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## Training procedure
### Framework versions
- PEFT 0.6.2
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### Model Sources [optional]
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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.
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Use the code below to get started with the model.
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] |
null | null | diffusers |
# AnimeBoysXL v2.0
**It takes substantial time and efforts to bake models. If you appreciate my models, I would be grateful if you could support me on [Ko-fi](https://ko-fi.com/koolchh) ☕.**
<Gallery />
## Features
- ✔️ **Good for inference**: AnimeBoysXL v2.0 is a flexible model which is good at generating images of anime boys and males-only content in a wide range of styles.
- ✔️ **Good for training**: AnimeBoysXL v2.0 is suitable for further training, thanks to its neutral style and ability to recognize a great deal of concepts. Feel free to train your own anime boy model/LoRA from AnimeBoysXL.
- ❌ AnimeBoysXL v2.0 is not optimized for creating anime girls. Please consider using other models for that purpose.
## Inference Guide
- **Prompt**: Use tag-based prompts to describe your subject.
- Tag ordering matters. It is highly recommended to structure your prompt with the following templates:
```
1boy, male focus, character name, series name, anything else you'd like to describe
```
```
2boys, male focus, multiple boys, character name(s), series name, anything else you'd like to describe
```
- Append
```
, best quality, amazing quality, best aesthetic, absurdres
```
to the prompt to improve image quality.
- (*Optional*) Append
```
, year YYYY
```
to the prompt to shift the output toward the prevalent style of that year. `YYYY` is a 4 digit year, e.g. `, year 2023`
- **Negative prompt**: Choose from one of the following two presets.
1. Heavy (*recommended*):
```
lowres, (bad:1.05), text, error, missing, extra, fewer, cropped, jpeg artifacts, worst quality, bad quality, watermark, bad aesthetic, unfinished, chromatic aberration, scan, scan artifacts, 1girl, breasts
```
2. Light:
```
lowres, jpeg artifacts, worst quality, watermark, blurry, bad aesthetic, 1girl, breasts
```
- (*Optional*) Add
```
, realistic, lips, nose
```
to the negative prompt if you need a flat anime-like style face.
- **VAE**: Make sure you're using [SDXL VAE](https://huggingface.co/stabilityai/sdxl-vae/tree/main).
- **Sampling method, sampling steps and CFG scale**: I find **(Euler a, 28, 5)** good. You are encouraged to experiment with other settings.
- **Width and height**: **832*1216** for portrait, **1024*1024** for square, and **1216*832** for landscape.
## 🧨Diffusers Example Usage
```python
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("Koolchh/AnimeBoysXL-v2.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
pipe.to("cuda")
prompt = "1boy, male focus, shirt, solo, looking at viewer, smile, black hair, brown eyes, short hair, best quality, amazing quality, best aesthetic, absurdres"
negative_prompt = "lowres, (bad:1.05), text, error, missing, extra, fewer, cropped, jpeg artifacts, worst quality, bad quality, watermark, bad aesthetic, unfinished, chromatic aberration, scan, scan artifacts, 1girl, breasts"
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=1024,
height=1024,
guidance_scale=5,
num_inference_steps=28
).images[0]
```
## Training Details
AnimeBoysXL v2.0 is trained from [Stable Diffusion XL Base 1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), on ~516k images.
The following tags are attached to the training data to make it easier to steer toward either more aesthetic or more flexible results.
### Quality tags
| tag | score |
|-------------------|------------|
| `best quality` | >= 150 |
| `amazing quality` | [100, 150) |
| `great quality` | [75, 100) |
| `normal quality` | [0, 75) |
| `bad quality` | (-5, 0) |
| `worst quality` | <= -5 |
### Aesthetic tags
| tag | score |
|--------------------|--------------|
| `best aesthetic` | >= 6.675 |
| `great aesthetic` | [6.0, 6.675) |
| `normal aesthetic` | [5.0, 6.0) |
| `bad aesthetic` | < 5.0 |
### Rating tags
| tag | rating |
|-----------------|--------------|
| `sfw` | general |
| `slightly nsfw` | sensitive |
| `fairly nsfw` | questionable |
| `very nsfw` | explicit |
### Year tags
`year YYYY` where `YYYY` is in the range of [2005, 2023].
### Training configurations
- Hardware: 4 * Nvidia A100 80GB GPUs
- Optimizer: AdaFactor
- Gradient accumulation steps: 8
- Batch size: 4 * 8 * 4 = 128
- Learning rates:
- 8e-6 for U-Net
- 5.2e-6 for text encoder 1 (CLIP ViT-L)
- 4.8e-6 for text encoder 2 (OpenCLIP ViT-bigG)
### Changes from v1.0
- Train with tag ordering.
- Add `sfw` rating tag.
- More epochs on the questionable and explicit rating subset.
- FP16 mixed-precision training for final epochs.
| {"license": "openrail++", "tags": ["text-to-image", "stable-diffusion", "diffusers"], "widget": [{"text": "1boy, male focus, japanese clothes, yukata, muscular male, muscular, paw pose, solo, looking at viewer, grin, black hair, blue eyes, short hair, flower, petals, best quality, amazing quality, best aesthetic, absurdres, year 2023", "parameters": {"negative_prompt": "lowres, (bad:1.05), text, error, missing, extra, fewer, cropped, jpeg artifacts, worst quality, bad quality, watermark, bad aesthetic, unfinished, chromatic aberration, scan, scan artifacts, 1girl, breasts, realistic, lips, nose"}, "output": {"url": "images/sample01.png"}, "example_title": "sample01"}, {"text": "1boy, male focus, sitting, on couch, couch, crossed legs, hand on own face, white shirt, shirt, black pants, pants, necktie, indoors, solo, looking at viewer, open mouth, white hair, yellow eyes, short hair, best quality, amazing quality, best aesthetic, absurdres, year 2023", "parameters": {"negative_prompt": "lowres, (bad:1.05), text, error, missing, extra, fewer, cropped, jpeg artifacts, worst quality, bad quality, watermark, bad aesthetic, unfinished, chromatic aberration, scan, scan artifacts, 1girl, breasts, realistic, lips, nose"}, "output": {"url": "images/sample02.png"}, "example_title": "sample02"}, {"text": "2boys, male focus, multiple boys, yaoi, imminent kiss, looking at another, smile, short hair, black hair, closed eyes, brown hair, blue eyes, shirt, lens flare, sky, cloud, blue sky, sweat, best quality, amazing quality, best aesthetic, absurdres, year 2023", "parameters": {"negative_prompt": "lowres, (bad:1.05), text, error, missing, extra, fewer, cropped, jpeg artifacts, worst quality, bad quality, watermark, bad aesthetic, unfinished, chromatic aberration, scan, scan artifacts, 1girl, breasts, realistic, lips, nose"}, "output": {"url": "images/sample03.png"}, "example_title": "sample03"}, {"text": "1boy, male focus, tank top, black tank top, sidepec, muscular male, muscular, bara, upper body, solo, looking to the side, annoyed, speech bubble, short hair, undercut, stubble, black hair, green eyes, parted lips, white background, simple background, best quality, amazing quality, best aesthetic, absurdres, year 2023", "parameters": {"negative_prompt": "lowres, (bad:1.05), text, error, missing, extra, fewer, cropped, jpeg artifacts, worst quality, bad quality, watermark, bad aesthetic, unfinished, chromatic aberration, scan, scan artifacts, 1girl, breasts"}, "output": {"url": "images/sample04.png"}, "example_title": "sample04"}]} | text-to-image | Koolchh/AnimeBoysXL-v2.0 | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:openrail++",
"endpoints_compatible",
"has_space",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | 2024-02-08T01:17:57+00:00 | [] | [] | TAGS
#diffusers #safetensors #text-to-image #stable-diffusion #license-openrail++ #endpoints_compatible #has_space #diffusers-StableDiffusionXLPipeline #region-us
| AnimeBoysXL v2.0
================
It takes substantial time and efforts to bake models. If you appreciate my models, I would be grateful if you could support me on Ko-fi .
Features
--------
* ️ Good for inference: AnimeBoysXL v2.0 is a flexible model which is good at generating images of anime boys and males-only content in a wide range of styles.
* ️ Good for training: AnimeBoysXL v2.0 is suitable for further training, thanks to its neutral style and ability to recognize a great deal of concepts. Feel free to train your own anime boy model/LoRA from AnimeBoysXL.
* AnimeBoysXL v2.0 is not optimized for creating anime girls. Please consider using other models for that purpose.
Inference Guide
---------------
* Prompt: Use tag-based prompts to describe your subject.
+ Tag ordering matters. It is highly recommended to structure your prompt with the following templates:
+ Append
to the prompt to improve image quality.
+ (*Optional*) Append
to the prompt to shift the output toward the prevalent style of that year. 'YYYY' is a 4 digit year, e.g. ', year 2023'
* Negative prompt: Choose from one of the following two presets.
1. Heavy (*recommended*):
2. Light:
+ (*Optional*) Add
to the negative prompt if you need a flat anime-like style face.
* VAE: Make sure you're using SDXL VAE.
* Sampling method, sampling steps and CFG scale: I find (Euler a, 28, 5) good. You are encouraged to experiment with other settings.
* Width and height: 832*1216 for portrait, 1024*1024 for square, and 1216\*832 for landscape.
Diffusers Example Usage
-----------------------
Training Details
----------------
AnimeBoysXL v2.0 is trained from Stable Diffusion XL Base 1.0, on ~516k images.
The following tags are attached to the training data to make it easier to steer toward either more aesthetic or more flexible results.
### Quality tags
### Aesthetic tags
### Rating tags
### Year tags
'year YYYY' where 'YYYY' is in the range of [2005, 2023].
### Training configurations
* Hardware: 4 \* Nvidia A100 80GB GPUs
* Optimizer: AdaFactor
* Gradient accumulation steps: 8
* Batch size: 4 \* 8 \* 4 = 128
* Learning rates:
+ 8e-6 for U-Net
+ 5.2e-6 for text encoder 1 (CLIP ViT-L)
+ 4.8e-6 for text encoder 2 (OpenCLIP ViT-bigG)
### Changes from v1.0
* Train with tag ordering.
* Add 'sfw' rating tag.
* More epochs on the questionable and explicit rating subset.
* FP16 mixed-precision training for final epochs.
| [
"### Quality tags",
"### Aesthetic tags",
"### Rating tags",
"### Year tags\n\n\n'year YYYY' where 'YYYY' is in the range of [2005, 2023].",
"### Training configurations\n\n\n* Hardware: 4 \\* Nvidia A100 80GB GPUs\n* Optimizer: AdaFactor\n* Gradient accumulation steps: 8\n* Batch size: 4 \\* 8 \\* 4 = 128\n* Learning rates:\n\t+ 8e-6 for U-Net\n\t+ 5.2e-6 for text encoder 1 (CLIP ViT-L)\n\t+ 4.8e-6 for text encoder 2 (OpenCLIP ViT-bigG)",
"### Changes from v1.0\n\n\n* Train with tag ordering.\n* Add 'sfw' rating tag.\n* More epochs on the questionable and explicit rating subset.\n* FP16 mixed-precision training for final epochs."
] | [
"TAGS\n#diffusers #safetensors #text-to-image #stable-diffusion #license-openrail++ #endpoints_compatible #has_space #diffusers-StableDiffusionXLPipeline #region-us \n",
"### Quality tags",
"### Aesthetic tags",
"### Rating tags",
"### Year tags\n\n\n'year YYYY' where 'YYYY' is in the range of [2005, 2023].",
"### Training configurations\n\n\n* Hardware: 4 \\* Nvidia A100 80GB GPUs\n* Optimizer: AdaFactor\n* Gradient accumulation steps: 8\n* Batch size: 4 \\* 8 \\* 4 = 128\n* Learning rates:\n\t+ 8e-6 for U-Net\n\t+ 5.2e-6 for text encoder 1 (CLIP ViT-L)\n\t+ 4.8e-6 for text encoder 2 (OpenCLIP ViT-bigG)",
"### Changes from v1.0\n\n\n* Train with tag ordering.\n* Add 'sfw' rating tag.\n* More epochs on the questionable and explicit rating subset.\n* FP16 mixed-precision training for final epochs."
] | [
61,
4,
6,
4,
26,
101,
57
] | [
"passage: TAGS\n#diffusers #safetensors #text-to-image #stable-diffusion #license-openrail++ #endpoints_compatible #has_space #diffusers-StableDiffusionXLPipeline #region-us \n### Quality tags### Aesthetic tags### Rating tags### Year tags\n\n\n'year YYYY' where 'YYYY' is in the range of [2005, 2023].### Training configurations\n\n\n* Hardware: 4 \\* Nvidia A100 80GB GPUs\n* Optimizer: AdaFactor\n* Gradient accumulation steps: 8\n* Batch size: 4 \\* 8 \\* 4 = 128\n* Learning rates:\n\t+ 8e-6 for U-Net\n\t+ 5.2e-6 for text encoder 1 (CLIP ViT-L)\n\t+ 4.8e-6 for text encoder 2 (OpenCLIP ViT-bigG)### Changes from v1.0\n\n\n* Train with tag ordering.\n* Add 'sfw' rating tag.\n* More epochs on the questionable and explicit rating subset.\n* FP16 mixed-precision training for final epochs."
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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. -->
# llama-2-ko-7b-v1.1b-singlegpu_gradient_32_epoch_30_train_batch_size_1_all_data_test_1_1_Feb_7th
This model is a fine-tuned version of [beomi/llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b) on an unknown 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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0
### Training results
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0+cu121
- Datasets 2.16.0
- Tokenizers 0.13.3
| {"tags": ["generated_from_trainer"], "base_model": "beomi/llama-2-ko-7b", "model-index": [{"name": "llama-2-ko-7b-v1.1b-singlegpu_gradient_32_epoch_30_train_batch_size_1_all_data_test_1_1_Feb_7th", "results": []}]} | text-generation | kim1/test_llama_2_ko_2 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:beomi/llama-2-ko-7b",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T01:21:36+00:00 | [] | [] | TAGS
#transformers #pytorch #llama #text-generation #generated_from_trainer #base_model-beomi/llama-2-ko-7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# llama-2-ko-7b-v1.1b-singlegpu_gradient_32_epoch_30_train_batch_size_1_all_data_test_1_1_Feb_7th
This model is a fine-tuned version of beomi/llama-2-ko-7b on an unknown 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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0
### Training results
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0+cu121
- Datasets 2.16.0
- Tokenizers 0.13.3
| [
"# llama-2-ko-7b-v1.1b-singlegpu_gradient_32_epoch_30_train_batch_size_1_all_data_test_1_1_Feb_7th\n\nThis model is a fine-tuned version of beomi/llama-2-ko-7b on an unknown 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: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 32\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- num_epochs: 30.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.33.3\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.0\n- Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #generated_from_trainer #base_model-beomi/llama-2-ko-7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# llama-2-ko-7b-v1.1b-singlegpu_gradient_32_epoch_30_train_batch_size_1_all_data_test_1_1_Feb_7th\n\nThis model is a fine-tuned version of beomi/llama-2-ko-7b on an unknown 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: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 32\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- num_epochs: 30.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.33.3\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.0\n- Tokenizers 0.13.3"
] | [
68,
76,
6,
12,
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114,
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35
] | [
"passage: TAGS\n#transformers #pytorch #llama #text-generation #generated_from_trainer #base_model-beomi/llama-2-ko-7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# llama-2-ko-7b-v1.1b-singlegpu_gradient_32_epoch_30_train_batch_size_1_all_data_test_1_1_Feb_7th\n\nThis model is a fine-tuned version of beomi/llama-2-ko-7b on an unknown 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: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 32\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- num_epochs: 30.0### Training results### Framework versions\n\n- Transformers 4.33.3\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.0\n- Tokenizers 0.13.3"
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null | null | null | # EfficientSAM
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
## Online Demo & Examples
[Online demo](https://huggingface.co/spaces/yunyangx/EfficientSAM) and examples can be found in the [project page](https://yformer.github.io/efficient-sam/).
If you're using EfficientSAM in your research or applications, please cite using this BibTeX:
```bibtex
@article{xiong2023efficientsam,
title={EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything},
author={Yunyang Xiong, Bala Varadarajan, Lemeng Wu, Xiaoyu Xiang, Fanyi Xiao, Chenchen Zhu, Xiaoliang Dai, Dilin Wang, Fei Sun, Forrest Iandola, Raghuraman Krishnamoorthi, Vikas Chandra},
journal={arXiv:2312.00863},
year={2023}
}
```
| {"license": "apache-2.0"} | null | yunyangx/EfficientSAM | [
"onnx",
"license:apache-2.0",
"has_space",
"region:us"
] | 2024-02-08T01:21:52+00:00 | [] | [] | TAGS
#onnx #license-apache-2.0 #has_space #region-us
| # EfficientSAM
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
## Online Demo & Examples
Online demo and examples can be found in the project page.
If you're using EfficientSAM in your research or applications, please cite using this BibTeX:
| [
"# EfficientSAM\nEfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything",
"## Online Demo & Examples\nOnline demo and examples can be found in the project page.\n\nIf you're using EfficientSAM in your research or applications, please cite using this BibTeX:"
] | [
"TAGS\n#onnx #license-apache-2.0 #has_space #region-us \n",
"# EfficientSAM\nEfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything",
"## Online Demo & Examples\nOnline demo and examples can be found in the project page.\n\nIf you're using EfficientSAM in your research or applications, please cite using this BibTeX:"
] | [
22,
26,
43
] | [
"passage: TAGS\n#onnx #license-apache-2.0 #has_space #region-us \n# EfficientSAM\nEfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything## Online Demo & Examples\nOnline demo and examples can be found in the project page.\n\nIf you're using EfficientSAM in your research or applications, please cite using this BibTeX:"
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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. -->
# tiny_llama_instruct_generation
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0919
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3923 | 0.04 | 20 | 2.3466 |
| 2.2664 | 0.08 | 40 | 2.2596 |
| 2.1909 | 0.12 | 60 | 2.1966 |
| 2.1885 | 0.16 | 80 | 2.1737 |
| 2.1536 | 0.2 | 100 | 2.1553 |
| 2.1255 | 0.24 | 120 | 2.1426 |
| 2.1298 | 0.29 | 140 | 2.1318 |
| 2.0497 | 0.33 | 160 | 2.1242 |
| 2.0967 | 0.37 | 180 | 2.1198 |
| 2.1252 | 0.41 | 200 | 2.1160 |
| 2.1051 | 0.45 | 220 | 2.1139 |
| 2.0848 | 0.49 | 240 | 2.1121 |
| 2.1562 | 0.53 | 260 | 2.1104 |
| 2.1043 | 0.57 | 280 | 2.1088 |
| 2.0865 | 0.61 | 300 | 2.1075 |
| 2.0729 | 0.65 | 320 | 2.1065 |
| 2.1046 | 0.69 | 340 | 2.1059 |
| 2.1398 | 0.73 | 360 | 2.1050 |
| 2.0928 | 0.78 | 380 | 2.1035 |
| 2.1055 | 0.82 | 400 | 2.1027 |
| 2.0327 | 0.86 | 420 | 2.1017 |
| 2.0904 | 0.9 | 440 | 2.1012 |
| 2.0922 | 0.94 | 460 | 2.1006 |
| 2.0911 | 0.98 | 480 | 2.0997 |
| 2.1063 | 1.02 | 500 | 2.0994 |
| 2.1296 | 1.06 | 520 | 2.0993 |
| 2.1051 | 1.1 | 540 | 2.0986 |
| 2.0919 | 1.14 | 560 | 2.0982 |
| 2.0608 | 1.18 | 580 | 2.0977 |
| 2.0865 | 1.22 | 600 | 2.0966 |
| 2.0912 | 1.27 | 620 | 2.0962 |
| 2.0858 | 1.31 | 640 | 2.0962 |
| 2.0914 | 1.35 | 660 | 2.0961 |
| 2.0542 | 1.39 | 680 | 2.0951 |
| 2.0939 | 1.43 | 700 | 2.0948 |
| 2.0707 | 1.47 | 720 | 2.0942 |
| 2.1158 | 1.51 | 740 | 2.0944 |
| 2.079 | 1.55 | 760 | 2.0941 |
| 2.0232 | 1.59 | 780 | 2.0935 |
| 2.0954 | 1.63 | 800 | 2.0934 |
| 2.079 | 1.67 | 820 | 2.0939 |
| 2.0747 | 1.71 | 840 | 2.0932 |
| 2.0881 | 1.76 | 860 | 2.0926 |
| 2.0319 | 1.8 | 880 | 2.0928 |
| 2.1047 | 1.84 | 900 | 2.0922 |
| 2.0383 | 1.88 | 920 | 2.0923 |
| 2.0602 | 1.92 | 940 | 2.0923 |
| 2.0902 | 1.96 | 960 | 2.0919 |
| 2.0845 | 2.0 | 980 | 2.0919 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "model-index": [{"name": "tiny_llama_instruct_generation", "results": []}]} | null | kevinautomation/tiny_llama_instruct_generation | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
"license:apache-2.0",
"region:us"
] | 2024-02-08T01:26:31+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T #license-apache-2.0 #region-us
| tiny\_llama\_instruct\_generation
=================================
This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T on the generator dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0919
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
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* lr\_scheduler\_warmup\_steps: 0.03
* num\_epochs: 2
### Training results
### Framework versions
* PEFT 0.8.2
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
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116,
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"passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T #license-apache-2.0 #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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_steps: 0.03\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | kevinautomation/TinyLlama-1.1B-intermediate-step-1431k-3T_reddit_expert_model | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2024-02-08T01:27:09+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #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",
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"## 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]",
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"## Model Card Authors [optional]",
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] | [
"TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #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 #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #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. -->
# laryngitis
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7828
- Accuracy: 0.5455
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4888 | 1.0 | 6 | 0.7395 | 0.4091 |
| 0.4714 | 2.0 | 12 | 0.7492 | 0.4545 |
| 0.4298 | 3.0 | 18 | 0.7774 | 0.5 |
| 0.3732 | 4.0 | 24 | 0.7864 | 0.5 |
| 0.352 | 5.0 | 30 | 0.7903 | 0.5 |
| 0.3147 | 6.0 | 36 | 0.8435 | 0.5 |
| 0.2969 | 7.0 | 42 | 0.7719 | 0.5 |
| 0.2902 | 8.0 | 48 | 0.7035 | 0.5909 |
| 0.238 | 9.0 | 54 | 0.7546 | 0.5909 |
| 0.2654 | 10.0 | 60 | 0.7828 | 0.5455 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "laryngitis", "results": []}]} | audio-classification | Anguuuuus/laryngitis | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-08T01:31:07+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #region-us
| laryngitis
==========
This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7828
* Accuracy: 0.5455
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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* 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: 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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #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: 1e-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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
66,
116,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #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: 1e-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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\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. -->
# wav2vec_RTSplit0208_11
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0316
- Wer: 0.2179
- Cer: 0.1708
## 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: 5.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 5.1548 | 1.0 | 120 | 4.2467 | 1.0 | 0.9437 |
| 1.0806 | 2.0 | 240 | 0.8747 | 0.8533 | 0.5737 |
| 0.7597 | 3.0 | 360 | 0.6448 | 0.8205 | 0.5722 |
| 0.6335 | 4.0 | 480 | 0.5465 | 0.7666 | 0.5220 |
| 0.5325 | 5.0 | 600 | 0.4268 | 0.6440 | 0.3320 |
| 0.416 | 6.0 | 720 | 0.2853 | 0.4872 | 0.2587 |
| 0.3435 | 7.0 | 840 | 0.1781 | 0.4134 | 0.2175 |
| 0.2335 | 8.0 | 960 | 0.0782 | 0.3113 | 0.1658 |
| 0.1812 | 9.0 | 1080 | 0.0409 | 0.2255 | 0.1660 |
| 0.0976 | 10.0 | 1200 | 0.0316 | 0.2179 | 0.1708 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "model-index": [{"name": "wav2vec_RTSplit0208_11", "results": []}]} | automatic-speech-recognition | tndklab/wav2vec_RTSplit0208_11 | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-08T01:32:51+00:00 | [] | [] | TAGS
#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec\_RTSplit0208\_11
========================
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-japanese on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0316
* Wer: 0.2179
* Cer: 0.1708
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: 5.5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #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: 5.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
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33
] | [
"passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #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: 5.5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | humung/polyglot-ko-12.8b-vlending-v0.3 | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"region:us"
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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
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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:
- Hours used:
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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. -->
# multiple_choice_cowese_betoLDA_2
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) 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: 1.5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "dccuchile/bert-base-spanish-wwm-cased", "model-index": [{"name": "multiple_choice_cowese_betoLDA_2", "results": []}]} | null | tomashs/multiple_choice_cowese_betoLDA_2 | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"generated_from_trainer",
"base_model:dccuchile/bert-base-spanish-wwm-cased",
"endpoints_compatible",
"region:us"
] | 2024-02-08T01:51:28+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #generated_from_trainer #base_model-dccuchile/bert-base-spanish-wwm-cased #endpoints_compatible #region-us
|
# multiple_choice_cowese_betoLDA_2
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased 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: 1.5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"# multiple_choice_cowese_betoLDA_2\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased 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: 1.5e-05\n- train_batch_size: 1\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: 2\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #bert #generated_from_trainer #base_model-dccuchile/bert-base-spanish-wwm-cased #endpoints_compatible #region-us \n",
"# multiple_choice_cowese_betoLDA_2\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased 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: 1.5e-05\n- train_batch_size: 1\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: 2\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
57,
50,
6,
12,
8,
3,
103,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #bert #generated_from_trainer #base_model-dccuchile/bert-base-spanish-wwm-cased #endpoints_compatible #region-us \n# multiple_choice_cowese_betoLDA_2\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased 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: 1.5e-05\n- train_batch_size: 1\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: 2\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\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. -->
# bert-base-portuguese-cased-finetuned-RM-2
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0457
## 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 14 | 3.3891 |
| No log | 2.0 | 28 | 2.8953 |
| No log | 3.0 | 42 | 3.0611 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "neuralmind/bert-base-portuguese-cased", "model-index": [{"name": "bert-base-portuguese-cased-finetuned-RM-2", "results": []}]} | fill-mask | ricigl/bert-base-portuguese-cased-finetuned-RM-2 | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:neuralmind/bert-base-portuguese-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-08T01:54:11+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #base_model-neuralmind/bert-base-portuguese-cased #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-portuguese-cased-finetuned-RM-2
=========================================
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0457
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 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
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"### Training results",
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#transformers #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #base_model-neuralmind/bert-base-portuguese-cased #license-mit #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: 3.0### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | null | - [4x-UltraSharp.pth](https://openmodeldb.info/models/4x-UltraSharp)
- Author: Kim2091、License: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)、改変なし
- [4x-AnimeSharp.pth](https://openmodeldb.info/models/4x-AnimeSharp)
- Author: Kim2091、License: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)、改変なし
- [4x_foolhardy_Remacri.pth](https://openmodeldb.info/models/4x-Remacri)
- Author: FoolhardyVEVO、License: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)、改変なし
- [ponyDiffusionV6XL_v6StartWithThisOne.safetensors](https://civitai.com/models/257749)
- Author: PurpleSmartAI、License: [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/)、改変なし
| {"license": "unknown"} | null | Zuntan/dist | [
"license:unknown",
"region:us"
] | 2024-02-08T01:54:26+00:00 | [] | [] | TAGS
#license-unknown #region-us
| - URL
- Author: Kim2091、License: CC BY-NC-SA 4.0、改変なし
- URL
- Author: Kim2091、License: CC BY-NC-SA 4.0、改変なし
- 4x_foolhardy_Remacri.pth
- Author: FoolhardyVEVO、License: CC BY-NC-SA 4.0、改変なし
- ponyDiffusionV6XL_v6StartWithThisOne.safetensors
- Author: PurpleSmartAI、License: Fair AI Public License 1.0-SD、改変なし
| [] | [
"TAGS\n#license-unknown #region-us \n"
] | [
13
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] |
null | null | transformers |
**Update Log**
- 2024.01.08: Initial Test version Release of Solar-Ko
# **Open-Solar-Ko** ⭐🇰🇷
Solar-Ko represents an advanced iteration of the upstage/SOLAR-10.7B-v1.0 model, featuring an expanded vocabulary and the inclusion of a Korean corpus for enhanced pretraining.
Open-Solar-Ko exclusively utilizes publicly accessible Korean corpora, including sources such as [AI Hub](https://www.aihub.or.kr), [Modu Corpus, 모두의 말뭉치](https://corpus.korean.go.kr/), and [Korean Wikipedia](https://dumps.wikimedia.org/kowiki/).
As training was conducted solely with publicly available corpora, this model is open for unrestricted use by everyone, adhering to the Apache2.0 open source License.
## Model Details
**Model Developers:** Junbum Lee (Beomi)
**Variations:** Solar-Ko is available with one parameter sizes — 10B with Continual Pretrained version.
**Input:** The model accepts only text input.
**Output:** The model produces text output exclusively.
**Model Architecture:**
SOLAR-KO-10.7B is an auto-regressive language model that leverages an optimized transformer architecture derived from Llama-2.
| |Training Data|Parameters|Content Length|GQA|Tokens|Learning Rate|
|---|---|---|---|---|---|---|
|SOLAR-KO-10.7B|*A curated mix of Publicly Accessible Korean Corpora*|10.7B|2k|✘|>15B*|5e<sup>-5</sup>|
**Training Corpus**
The model was trained using selected datasets from AIHub and Modu Corpus. Detailed information about the training datasets is available below:
- AI Hub: [corpus/AI_HUB](./corpus/AI_HUB)
- Only the `Training` segment of the data was used.
- The `Validation` and `Test` segments were deliberately excluded.
- Modu Corpus: [corpus/MODU_CORPUS](./corpus/MODU_CORPUS)
The final JSONL dataset used to train this model is approximately 61GB in size.
Total token count: Approximately 15 billion tokens (*using the expanded tokenizer. With the original SOLAR tokenizer, >60 billion tokens.)
**Vocab Expansion**
| Model Name | Vocabulary Size | Description |
| --- | --- | --- |
| Original Solar | 32000 | Sentencepiece BPE |
| **Expanded SOLAR-KO-10.7B** | 46592 | Sentencepiece BPE. Added Korean vocab and merges |
**Tokenizing "안녕하세요, 오늘은 날씨가 좋네요."**
- SOLAR-10.7B: 26 tokens
- SOLAR-KO-10.7b: 8 tokens
| Model | Tokens |
| --- | --- |
| SOLAR-10.7B | `['▁', '안', '<0xEB>', '<0x85>', '<0x95>', '하', '세', '요', ',', '▁', '오', '<0xEB>', '<0x8A>', '<0x98>', '은', '▁', '날', '<0xEC>', '<0x94>', '<0xA8>', '가', '▁', '좋', '네', '요', '.']` |
| SOLAR-KO-10.7B | `['▁안녕', '하세요', ',', '▁오늘은', '▁날', '씨가', '▁좋네요', '.']` |
**Tokenizing "Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!"**
- SOLAR-10.7B: 22 tokens
- SOLAR-KO-10.7b: 22 tokens
| Model | Tokens |
| --- | --- |
| SOLAR-10.7B | `['▁Meet', '▁', '1', '0', '.', '7', 'B', '▁Solar', ':', '▁E', 'lev', 'ating', '▁Performance', '▁with', '▁Up', 'stage', '▁Dep', 'th', '▁UP', '▁Scal', 'ing', '!']` |
| SOLAR-KO-10.7B | `['▁Meet', '▁', '1', '0', '.', '7', 'B', '▁Solar', ':', '▁E', 'lev', 'ating', '▁Performance', '▁with', '▁Up', 'stage', '▁Dep', 'th', '▁UP', '▁Scal', 'ing', '!']` |
# LICENSE
Apache 2.0
# **Model Benchmark**
## LM Eval Harness - Korean (polyglot branch)
- Used EleutherAI's lm-evaluation-harness https://github.com/EleutherAI/lm-evaluation-harness/tree/polyglot
| | 0 | 5 | 10 | 50 |
|:---------------------------------|---------:|---------:|---------:|---------:|
| kobest_boolq (macro_f1) | 0.853949 | 0.88098 | 0.898139 | 0.902354 |
| kobest_copa (macro_f1) | 0.804531 | 0.826736 | 0.837656 | 0.860899 |
| kobest_hellaswag (macro_f1) | 0.507174 | 0.500983 | 0.487287 | 0.512182 |
| kobest_sentineg (macro_f1) | 0.3517 | 0.972291 | 0.977321 | 0.984884 |
| kohatespeech (macro_f1) | 0.258111 | 0.403957 | 0.386808 | 0.462393 |
| kohatespeech_apeach (macro_f1) | 0.337667 | 0.651697 | 0.705337 | 0.827757 |
| kohatespeech_gen_bias (macro_f1) | 0.124535 | 0.503464 | 0.498501 | 0.443218 |
| korunsmile (f1) | 0.3814 | 0.356939 | 0.369989 | 0.296193 |
| nsmc (acc) | 0.5356 | 0.87162 | 0.88654 | 0.89632 |
| pawsx_ko (acc) | 0.5435 | 0.5245 | 0.5315 | 0.5385 |
## Citation
```
@misc {solar_ko_junbum_2023,
author = { {L. Junbum} },
title = { Solar-Ko-10.7b },
year = 2024,
url = { https://huggingface.co/beomi/SOLAR-KO-10.7B },
publisher = { Hugging Face }
}
```
## Acknowledgements
- Training support was provided by the [TPU Research Cloud](https://sites.research.google/trc/) program.
- The training corpus includes data from [AI Hub](https://www.aihub.or.kr/), [Modu Corpus](https://corpus.korean.go.kr/), and [Korean Wikipedia](https://dumps.wikimedia.org/kowiki/). | {"language": ["ko", "en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["solar", "mistral", "pytorch", "solar-ko"], "pipeline_tag": "text-generation", "inference": false} | text-generation | yoonyoon/kb_v4.1_solar | [
"transformers",
"safetensors",
"llama",
"text-generation",
"solar",
"mistral",
"pytorch",
"solar-ko",
"ko",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-08T01:57:09+00:00 | [] | [
"ko",
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #solar #mistral #pytorch #solar-ko #ko #en #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
| Update Log
* 2024.01.08: Initial Test version Release of Solar-Ko
Open-Solar-Ko ⭐🇰🇷
=================
Solar-Ko represents an advanced iteration of the upstage/SOLAR-10.7B-v1.0 model, featuring an expanded vocabulary and the inclusion of a Korean corpus for enhanced pretraining.
Open-Solar-Ko exclusively utilizes publicly accessible Korean corpora, including sources such as AI Hub, Modu Corpus, 모두의 말뭉치, and Korean Wikipedia.
As training was conducted solely with publicly available corpora, this model is open for unrestricted use by everyone, adhering to the Apache2.0 open source License.
Model Details
-------------
Model Developers: Junbum Lee (Beomi)
Variations: Solar-Ko is available with one parameter sizes — 10B with Continual Pretrained version.
Input: The model accepts only text input.
Output: The model produces text output exclusively.
Model Architecture:
SOLAR-KO-10.7B is an auto-regressive language model that leverages an optimized transformer architecture derived from Llama-2.
Training Corpus
The model was trained using selected datasets from AIHub and Modu Corpus. Detailed information about the training datasets is available below:
* AI Hub: corpus/AI\_HUB
+ Only the 'Training' segment of the data was used.
+ The 'Validation' and 'Test' segments were deliberately excluded.
* Modu Corpus: corpus/MODU\_CORPUS
The final JSONL dataset used to train this model is approximately 61GB in size.
Total token count: Approximately 15 billion tokens (\*using the expanded tokenizer. With the original SOLAR tokenizer, >60 billion tokens.)
Vocab Expansion
Model Name: Original Solar, Vocabulary Size: 32000, Description: Sentencepiece BPE
Model Name: Expanded SOLAR-KO-10.7B, Vocabulary Size: 46592, Description: Sentencepiece BPE. Added Korean vocab and merges
Tokenizing "안녕하세요, 오늘은 날씨가 좋네요."
* SOLAR-10.7B: 26 tokens
* SOLAR-KO-10.7b: 8 tokens
Tokenizing "Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!"
* SOLAR-10.7B: 22 tokens
* SOLAR-KO-10.7b: 22 tokens
LICENSE
=======
Apache 2.0
Model Benchmark
===============
LM Eval Harness - Korean (polyglot branch)
------------------------------------------
* Used EleutherAI's lm-evaluation-harness URL
Acknowledgements
----------------
* Training support was provided by the TPU Research Cloud program.
* The training corpus includes data from AI Hub, Modu Corpus, and Korean Wikipedia.
| [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #solar #mistral #pytorch #solar-ko #ko #en #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] | [
66
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #solar #mistral #pytorch #solar-ko #ko #en #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
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null | null | peft |
# Model Card for Model ID
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "Trelis/Llama-2-7b-chat-hf-sharded-bf16"} | null | SolaireOfTheSun/Llama-2-7b-chat-hf-sharded-bf16-feinabgestimmt-adapters-gpt | [
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | 2024-02-08T02:09:21+00:00 | [
"1910.09700"
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#peft #arxiv-1910.09700 #base_model-Trelis/Llama-2-7b-chat-hf-sharded-bf16 #region-us
|
# 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
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.8.2 | [
"# Model Card for Model ID",
"## 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",
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"### Downstream Use [optional]",
"### Out-of-Scope Use",
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"## 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",
"### Framework versions\n\n- PEFT 0.8.2"
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"# Model Card for Model ID",
"## 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]:",
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"### Downstream Use [optional]",
"### Out-of-Scope Use",
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"### 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.",
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"## 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",
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"### 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 | null |
## This is a pre-release model interface, training started on February 7, 2024, and the model will be released in the future. ##
The model adopts the Phi architecture, with 550 million parameters. It only supports English and does not support code writing.
The model's dataset is obtained by cleaning and deduplicating open-source datasets, with pre-training using approximately 30 billion instances.
If you are a native English speaker, you might find these sentences uncomfortable to read because the training of this model and the writing of this document were only completed by a very inexperienced Chinese high school student.
Anyway, this is a new attempt. It is trained on consumer-grade devices and without the guidance of professionals, so it's hard for us to expect it to perform exceptionally well.
But we hope this will be the beginning of a new great exploration. | {"language": "en", "license": "mit"} | null | PhelixZhen/Algae-550M | [
"en",
"license:mit",
"region:us"
] | 2024-02-08T02:16:29+00:00 | [] | [
"en"
] | TAGS
#en #license-mit #region-us
|
## This is a pre-release model interface, training started on February 7, 2024, and the model will be released in the future. ##
The model adopts the Phi architecture, with 550 million parameters. It only supports English and does not support code writing.
The model's dataset is obtained by cleaning and deduplicating open-source datasets, with pre-training using approximately 30 billion instances.
If you are a native English speaker, you might find these sentences uncomfortable to read because the training of this model and the writing of this document were only completed by a very inexperienced Chinese high school student.
Anyway, this is a new attempt. It is trained on consumer-grade devices and without the guidance of professionals, so it's hard for us to expect it to perform exceptionally well.
But we hope this will be the beginning of a new great exploration. | [
"## This is a pre-release model interface, training started on February 7, 2024, and the model will be released in the future. ##\n\nThe model adopts the Phi architecture, with 550 million parameters. It only supports English and does not support code writing.\n\n\nThe model's dataset is obtained by cleaning and deduplicating open-source datasets, with pre-training using approximately 30 billion instances.\n\n\nIf you are a native English speaker, you might find these sentences uncomfortable to read because the training of this model and the writing of this document were only completed by a very inexperienced Chinese high school student.\n\n\nAnyway, this is a new attempt. It is trained on consumer-grade devices and without the guidance of professionals, so it's hard for us to expect it to perform exceptionally well.\n\nBut we hope this will be the beginning of a new great exploration."
] | [
"TAGS\n#en #license-mit #region-us \n",
"## This is a pre-release model interface, training started on February 7, 2024, and the model will be released in the future. ##\n\nThe model adopts the Phi architecture, with 550 million parameters. It only supports English and does not support code writing.\n\n\nThe model's dataset is obtained by cleaning and deduplicating open-source datasets, with pre-training using approximately 30 billion instances.\n\n\nIf you are a native English speaker, you might find these sentences uncomfortable to read because the training of this model and the writing of this document were only completed by a very inexperienced Chinese high school student.\n\n\nAnyway, this is a new attempt. It is trained on consumer-grade devices and without the guidance of professionals, so it's hard for us to expect it to perform exceptionally well.\n\nBut we hope this will be the beginning of a new great exploration."
] | [
13,
194
] | [
"passage: TAGS\n#en #license-mit #region-us \n## This is a pre-release model interface, training started on February 7, 2024, and the model will be released in the future. ##\n\nThe model adopts the Phi architecture, with 550 million parameters. It only supports English and does not support code writing.\n\n\nThe model's dataset is obtained by cleaning and deduplicating open-source datasets, with pre-training using approximately 30 billion instances.\n\n\nIf you are a native English speaker, you might find these sentences uncomfortable to read because the training of this model and the writing of this document were only completed by a very inexperienced Chinese high school student.\n\n\nAnyway, this is a new attempt. It is trained on consumer-grade devices and without the guidance of professionals, so it's hard for us to expect it to perform exceptionally well.\n\nBut we hope this will be the beginning of a new great exploration."
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null | null | null |
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="mathreader/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- 2.71", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | mathreader/q-Taxi-v3 | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-08T02:17:02+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
32,
33
] | [
"passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
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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. -->
# TrOCR_0208
This model is a fine-tuned version of [microsoft/trocr-base-stage1](https://huggingface.co/microsoft/trocr-base-stage1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8231
- Cer: 0.0842
## 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: 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.8364 | 0.68 | 200 | 1.5199 | 0.1926 |
| 1.3561 | 1.37 | 400 | 1.4427 | 0.1711 |
| 0.382 | 2.05 | 600 | 0.9931 | 0.1115 |
| 0.343 | 2.74 | 800 | 0.8231 | 0.0842 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
| {"tags": ["generated_from_trainer"], "base_model": "microsoft/trocr-base-stage1", "model-index": [{"name": "TrOCR_0208", "results": []}]} | null | yoon1000/TrOCR_0208 | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"base_model:microsoft/trocr-base-stage1",
"endpoints_compatible",
"region:us"
] | 2024-02-08T02:17:39+00:00 | [] | [] | TAGS
#transformers #safetensors #vision-encoder-decoder #generated_from_trainer #base_model-microsoft/trocr-base-stage1 #endpoints_compatible #region-us
| TrOCR\_0208
===========
This model is a fine-tuned version of microsoft/trocr-base-stage1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8231
* Cer: 0.0842
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: 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
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.36.2
* Pytorch 2.1.2+cu118
* Datasets 2.16.1
* Tokenizers 0.15.0
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu118\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
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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: 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\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu118\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
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"passage: TAGS\n#transformers #safetensors #vision-encoder-decoder #generated_from_trainer #base_model-microsoft/trocr-base-stage1 #endpoints_compatible #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: 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\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu118\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
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