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# Model Card for Model ID
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## Model Details
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<!-- 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.
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| {"library_name": "transformers", "tags": []} | text-generation | jeevana/GenAI_QnA_Mistral7b_QLoRA_G8_FV01 | [
"transformers",
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"text-generation",
"arxiv:1910.09700",
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"region:us"
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#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 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
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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null | null | diffusers | # Paint-Diffuion V2
Paint diffusion is fine tune model from stabilityai/stable-diffusion-xl-base-1.0. It generates images like watercolor paintings.
## Examples
<Gallery /> | {"license": "apache-2.0", "library_name": "diffusers", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "sdxl"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "widget": [{"text": "darth vader fighting superman, 2 people, lightsaber"}]} | text-to-image | kviai/Paint-Diffuion-V2 | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"sdxl",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:apache-2.0",
"has_space",
"region:us"
] | 2024-02-09T16:57:57+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #sdxl #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-apache-2.0 #has_space #region-us
| # Paint-Diffuion V2
Paint diffusion is fine tune model from stabilityai/stable-diffusion-xl-base-1.0. It generates images like watercolor paintings.
## Examples
<Gallery /> | [
"# Paint-Diffuion V2\n\nPaint diffusion is fine tune model from stabilityai/stable-diffusion-xl-base-1.0. It generates images like watercolor paintings.",
"## Examples \n\n<Gallery />"
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #sdxl #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-apache-2.0 #has_space #region-us \n",
"# Paint-Diffuion V2\n\nPaint diffusion is fine tune model from stabilityai/stable-diffusion-xl-base-1.0. It generates images like watercolor paintings.",
"## Examples \n\n<Gallery />"
] | [
63,
44,
8
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #sdxl #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-apache-2.0 #has_space #region-us \n# Paint-Diffuion V2\n\nPaint diffusion is fine tune model from stabilityai/stable-diffusion-xl-base-1.0. It generates images like watercolor paintings.## Examples \n\n<Gallery />"
] | [
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] |
null | null | transformers |
# MT-Ranker
This is the MT-Ranker-Base model from the ICLR'24 Spotlight paper [MT-Ranker: Reference-free machine translation evaluation by inter-system ranking](https://openreview.net/forum?id=Rry1SeSOQL).
For model loading instructions see our [GitHub](https://github.com/ibraheem-moosa/mt-ranker).
We are working on streamlining the model loading. | {"license": "mit", "library_name": "transformers", "datasets": ["RicardoRei/wmt-da-human-evaluation", "RicardoRei/wmt-mqm-human-evaluation", "xnli", "nikitam/ACES"]} | null | ibraheemmoosa/mt-ranker-base | [
"transformers",
"pytorch",
"dataset:RicardoRei/wmt-da-human-evaluation",
"dataset:RicardoRei/wmt-mqm-human-evaluation",
"dataset:xnli",
"dataset:nikitam/ACES",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | 2024-02-09T16:58:15+00:00 | [] | [] | TAGS
#transformers #pytorch #dataset-RicardoRei/wmt-da-human-evaluation #dataset-RicardoRei/wmt-mqm-human-evaluation #dataset-xnli #dataset-nikitam/ACES #license-mit #endpoints_compatible #has_space #region-us
|
# MT-Ranker
This is the MT-Ranker-Base model from the ICLR'24 Spotlight paper MT-Ranker: Reference-free machine translation evaluation by inter-system ranking.
For model loading instructions see our GitHub.
We are working on streamlining the model loading. | [
"# MT-Ranker\n\nThis is the MT-Ranker-Base model from the ICLR'24 Spotlight paper MT-Ranker: Reference-free machine translation evaluation by inter-system ranking.\n\nFor model loading instructions see our GitHub.\n\nWe are working on streamlining the model loading."
] | [
"TAGS\n#transformers #pytorch #dataset-RicardoRei/wmt-da-human-evaluation #dataset-RicardoRei/wmt-mqm-human-evaluation #dataset-xnli #dataset-nikitam/ACES #license-mit #endpoints_compatible #has_space #region-us \n",
"# MT-Ranker\n\nThis is the MT-Ranker-Base model from the ICLR'24 Spotlight paper MT-Ranker: Reference-free machine translation evaluation by inter-system ranking.\n\nFor model loading instructions see our GitHub.\n\nWe are working on streamlining the model loading."
] | [
86,
65
] | [
"passage: TAGS\n#transformers #pytorch #dataset-RicardoRei/wmt-da-human-evaluation #dataset-RicardoRei/wmt-mqm-human-evaluation #dataset-xnli #dataset-nikitam/ACES #license-mit #endpoints_compatible #has_space #region-us \n# MT-Ranker\n\nThis is the MT-Ranker-Base model from the ICLR'24 Spotlight paper MT-Ranker: Reference-free machine translation evaluation by inter-system ranking.\n\nFor model loading instructions see our GitHub.\n\nWe are working on streamlining the model loading."
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | bitsoko/gumzo-rpj | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### 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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- Demo [optional]:
## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### 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:
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## Technical Specifications [optional]
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null | null | transformers |
# Model Card for Model ID
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## 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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# Model Card for Model ID
## Model Details
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## Uses
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## Bias, Risks, and Limitations
### Recommendations
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## How to Get Started with the Model
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## Training Details
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### Training Procedure
#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | mtc/mistralai-Mistral-7B-v0.1-arxiv-summarization-5000-last-lora-full-adapter | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T17:05:35+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Bias, Risks, and Limitations",
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"## Training Details",
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"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | mtc/mistralai-Mistral-7B-v0.1-arxiv-summarization-5000-last_merged | [
"transformers",
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"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
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] | [] | TAGS
#transformers #safetensors #mistral #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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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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## 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
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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]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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null | null | null | # Modelos de detección de objetos e identificadores de dinero para tiendas inteligentes
Este repositorio contiene dos potentes modelos de visión por computadora diseñados específicamente para aplicaciones de tiendas de comestibles. El primer modelo se especializa en la detección de objetos, lo que permite una identificación y ubicación precisas de varios productos dentro del entorno de la tienda. El segundo modelo se centra en el reconocimiento de moneda, lo que facilita procesos de pago fluidos durante el pago. Juntos, forman la base de nuestro sistema de tienda de comestibles inteligente, brindando a los clientes experiencias de compra eficientes y al mismo tiempo reduciendo los costos operativos.

## Descripción general:
### Modelo detector de objetos
#### Caracteristicas
- Detecte artículos comestibles comunes como frutas, verduras, teclado, mouse, libros, cucharas y más.
- Alta precisión gracias a técnicas avanzadas de aprendizaje profundo.
- Rendimiento en tiempo real adecuado para la implementación en entornos con recursos limitados, como dispositivos periféricos.
- Fácil integración utilizando marcos populares de aprendizaje automático como TensorFlow o PyTorch.
#### Usage Example
```python
import ShoppingIA as shop
# Shop
def main():
class_shop = shop.ShopIA()
cap = class_shop.__int__()
# Stream
stream = class_shop.tiendaIA(cap)
if __name__ == "__main__":
main()
# Clases Billetes:
# 0 -> 10,000 | 1 -> 20,000 | 2 -> 50,000
| {"license": "apache-2.0"} | null | AprendeIngenia/bill_bank_co | [
"onnx",
"license:apache-2.0",
"region:us"
] | 2024-02-09T17:09:46+00:00 | [] | [] | TAGS
#onnx #license-apache-2.0 #region-us
| # Modelos de detección de objetos e identificadores de dinero para tiendas inteligentes
Este repositorio contiene dos potentes modelos de visión por computadora diseñados específicamente para aplicaciones de tiendas de comestibles. El primer modelo se especializa en la detección de objetos, lo que permite una identificación y ubicación precisas de varios productos dentro del entorno de la tienda. El segundo modelo se centra en el reconocimiento de moneda, lo que facilita procesos de pago fluidos durante el pago. Juntos, forman la base de nuestro sistema de tienda de comestibles inteligente, brindando a los clientes experiencias de compra eficientes y al mismo tiempo reduciendo los costos operativos.
!Mini
## Descripción general:
### Modelo detector de objetos
#### Caracteristicas
- Detecte artículos comestibles comunes como frutas, verduras, teclado, mouse, libros, cucharas y más.
- Alta precisión gracias a técnicas avanzadas de aprendizaje profundo.
- Rendimiento en tiempo real adecuado para la implementación en entornos con recursos limitados, como dispositivos periféricos.
- Fácil integración utilizando marcos populares de aprendizaje automático como TensorFlow o PyTorch.
#### Usage Example
'''python
import ShoppingIA as shop
# Shop
def main():
class_shop = shop.ShopIA()
cap = class_shop.__int__()
# Stream
stream = class_shop.tiendaIA(cap)
if __name__ == "__main__":
main()
# Clases Billetes:
# 0 -> 10,000 | 1 -> 20,000 | 2 -> 50,000
| [
"# Modelos de detección de objetos e identificadores de dinero para tiendas inteligentes\n\nEste repositorio contiene dos potentes modelos de visión por computadora diseñados específicamente para aplicaciones de tiendas de comestibles. El primer modelo se especializa en la detección de objetos, lo que permite una identificación y ubicación precisas de varios productos dentro del entorno de la tienda. El segundo modelo se centra en el reconocimiento de moneda, lo que facilita procesos de pago fluidos durante el pago. Juntos, forman la base de nuestro sistema de tienda de comestibles inteligente, brindando a los clientes experiencias de compra eficientes y al mismo tiempo reduciendo los costos operativos.\n\n!Mini",
"## Descripción general:",
"### Modelo detector de objetos",
"#### Caracteristicas\n- Detecte artículos comestibles comunes como frutas, verduras, teclado, mouse, libros, cucharas y más.\n- Alta precisión gracias a técnicas avanzadas de aprendizaje profundo.\n- Rendimiento en tiempo real adecuado para la implementación en entornos con recursos limitados, como dispositivos periféricos.\n- Fácil integración utilizando marcos populares de aprendizaje automático como TensorFlow o PyTorch.",
"#### Usage Example\n'''python\nimport ShoppingIA as shop",
"# Shop\ndef main():\n class_shop = shop.ShopIA()\n cap = class_shop.__int__()\n # Stream\n stream = class_shop.tiendaIA(cap)\n\nif __name__ == \"__main__\":\n main()",
"# Clases Billetes:",
"# 0 -> 10,000 | 1 -> 20,000 | 2 -> 50,000"
] | [
"TAGS\n#onnx #license-apache-2.0 #region-us \n",
"# Modelos de detección de objetos e identificadores de dinero para tiendas inteligentes\n\nEste repositorio contiene dos potentes modelos de visión por computadora diseñados específicamente para aplicaciones de tiendas de comestibles. El primer modelo se especializa en la detección de objetos, lo que permite una identificación y ubicación precisas de varios productos dentro del entorno de la tienda. El segundo modelo se centra en el reconocimiento de moneda, lo que facilita procesos de pago fluidos durante el pago. Juntos, forman la base de nuestro sistema de tienda de comestibles inteligente, brindando a los clientes experiencias de compra eficientes y al mismo tiempo reduciendo los costos operativos.\n\n!Mini",
"## Descripción general:",
"### Modelo detector de objetos",
"#### Caracteristicas\n- Detecte artículos comestibles comunes como frutas, verduras, teclado, mouse, libros, cucharas y más.\n- Alta precisión gracias a técnicas avanzadas de aprendizaje profundo.\n- Rendimiento en tiempo real adecuado para la implementación en entornos con recursos limitados, como dispositivos periféricos.\n- Fácil integración utilizando marcos populares de aprendizaje automático como TensorFlow o PyTorch.",
"#### Usage Example\n'''python\nimport ShoppingIA as shop",
"# Shop\ndef main():\n class_shop = shop.ShopIA()\n cap = class_shop.__int__()\n # Stream\n stream = class_shop.tiendaIA(cap)\n\nif __name__ == \"__main__\":\n main()",
"# Clases Billetes:",
"# 0 -> 10,000 | 1 -> 20,000 | 2 -> 50,000"
] | [
18,
142,
5,
8,
91,
15,
58,
6,
16
] | [
"passage: TAGS\n#onnx #license-apache-2.0 #region-us \n# Modelos de detección de objetos e identificadores de dinero para tiendas inteligentes\n\nEste repositorio contiene dos potentes modelos de visión por computadora diseñados específicamente para aplicaciones de tiendas de comestibles. El primer modelo se especializa en la detección de objetos, lo que permite una identificación y ubicación precisas de varios productos dentro del entorno de la tienda. El segundo modelo se centra en el reconocimiento de moneda, lo que facilita procesos de pago fluidos durante el pago. Juntos, forman la base de nuestro sistema de tienda de comestibles inteligente, brindando a los clientes experiencias de compra eficientes y al mismo tiempo reduciendo los costos operativos.\n\n!Mini## Descripción general:### Modelo detector de objetos#### Caracteristicas\n- Detecte artículos comestibles comunes como frutas, verduras, teclado, mouse, libros, cucharas y más.\n- Alta precisión gracias a técnicas avanzadas de aprendizaje profundo.\n- Rendimiento en tiempo real adecuado para la implementación en entornos con recursos limitados, como dispositivos periféricos.\n- Fácil integración utilizando marcos populares de aprendizaje automático como TensorFlow o PyTorch.#### Usage Example\n'''python\nimport ShoppingIA as shop# Shop\ndef main():\n class_shop = shop.ShopIA()\n cap = class_shop.__int__()\n # Stream\n stream = class_shop.tiendaIA(cap)\n\nif __name__ == \"__main__\":\n main()# Clases Billetes:# 0 -> 10,000 | 1 -> 20,000 | 2 -> 50,000"
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] |
null | null | sample-factory |
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Katelie/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
| {"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_health_gathering_supreme", "type": "doom_health_gathering_supreme"}, "metrics": [{"type": "mean_reward", "value": "12.17 +/- 4.87", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Katelie/rl_course_vizdoom_health_gathering_supreme | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-09T17:12:55+00:00 | [] | [] | TAGS
#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
A(n) APPO model trained on the doom_health_gathering_supreme environment.
This model was trained using Sample-Factory 2.0: URL
Documentation for how to use Sample-Factory can be found at URL
## Downloading the model
After installing Sample-Factory, download the model with:
## Using the model
To run the model after download, use the 'enjoy' script corresponding to this environment:
You can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.
See URL for more details
## Training with this model
To continue training with this model, use the 'train' script corresponding to this environment:
Note, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at.
| [
"## Downloading the model\n\nAfter installing Sample-Factory, download the model with:",
"## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details",
"## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at."
] | [
"TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"## Downloading the model\n\nAfter installing Sample-Factory, download the model with:",
"## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details",
"## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at."
] | [
34,
19,
59,
67
] | [
"passage: TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n## Downloading the model\n\nAfter installing Sample-Factory, download the model with:## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at."
] | [
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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. -->
# distilbert-base-uncased-lora-text-classification
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: 0.9987
- Accuracy: {'accuracy': 0.885}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|
| No log | 1.0 | 250 | 0.3276 | {'accuracy': 0.882} |
| 0.4241 | 2.0 | 500 | 0.3495 | {'accuracy': 0.895} |
| 0.4241 | 3.0 | 750 | 0.3984 | {'accuracy': 0.891} |
| 0.2107 | 4.0 | 1000 | 0.5830 | {'accuracy': 0.886} |
| 0.2107 | 5.0 | 1250 | 0.7312 | {'accuracy': 0.878} |
| 0.0707 | 6.0 | 1500 | 0.8286 | {'accuracy': 0.89} |
| 0.0707 | 7.0 | 1750 | 0.9673 | {'accuracy': 0.881} |
| 0.0208 | 8.0 | 2000 | 0.9845 | {'accuracy': 0.885} |
| 0.0208 | 9.0 | 2250 | 0.9831 | {'accuracy': 0.884} |
| 0.0119 | 10.0 | 2500 | 0.9987 | {'accuracy': 0.885} |
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cpu
- Datasets 2.17.0
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-lora-text-classification", "results": []}]} | null | asavochkin/distilbert-base-uncased-lora-text-classification | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"region:us"
] | 2024-02-09T17:18:20+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #region-us
| distilbert-base-uncased-lora-text-classification
================================================
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: 0.9987
* Accuracy: {'accuracy': 0.885}
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.001
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
### Training results
### Framework versions
* PEFT 0.8.2
* Transformers 4.37.2
* Pytorch 2.2.0+cpu
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0+cpu\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0+cpu\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
47,
97,
4,
39
] | [
"passage: TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.2\n* Pytorch 2.2.0+cpu\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
<p><h1> speechless-sparsetral-16x7b-MoE </h1></p>
speechless-sparsetral-16x7b-MoE is the MoE upgraded version of [speechless-code-mistral-7b-v1.0](https://huggingface.co/uukuguy/speechless-code-mistral-7b-v1.0). The MoE fine-tuning adopts [Parameter-Efficient Sparsity Crafting (PESC)](https://arxiv.org/abs/2401.02731), which is an efficient fine-tuning architecture that uses LoRA modules as expert models, similar to the concept of [multi-loras](https://github.com/uukuguy/multi_loras).
Specifically, Mistral-7B-0.1 is used as the base model, with 16 experts and 4 expert outputs selected for inference. The fine-tuning dataset includes codefuse-ai/Evol-Instruction-66k to enhance the model's code generation ability. The specific datasets are as follows:
- jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 23,462 samples.
- Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 74,440 samples.
- garage-bAInd/Open-Platypus: 100%, 24,926 samples.
- WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,185 samples
- TokenBender/python_eval_instruct_51k: “python” in output .40,309 samples
- Spider: 8,659 samples
- codefuse-ai/Evol-Instruction-66k: 100%, 66,862 samples
## Alpaca Prompt Format
```
### Instruction:
<instruction>
### Response:
```
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name_or_path="uukuguy/speechless-sparsetral-16x7b-MoE"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=True).eval()
system = ""Below is an instruction that describes a task.\nWrite a response that appropriately completes the request.\n\n""
prompt = f"{system}\n\n### Instruction:\n{instruction}\n\n### Response:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
pred = model.generate(**inputs, max_length=4096, do_sample=True, top_k=50, top_p=0.99, temperature=0.9, num_return_sequences=1)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
```
| {"language": ["en"], "license": "llama2", "library_name": "transformers", "tags": ["llama-2", "code"], "datasets": ["jondurbin/airoboros-2.2", "Open-Orca/OpenOrca", "garage-bAInd/Open-Platypus", "WizardLM/WizardLM_evol_instruct_V2_196k", "TokenBender/python_eval_instruct_51k", "codefuse-ai/Evol-Instruction-66k"], "pipeline_tag": "text-generation", "model-index": [{"name": "SpeechlessCoder", "results": [{"task": {"type": "text-generation"}, "dataset": {"name": "HumanEval", "type": "openai_humaneval"}, "metrics": [{"type": "pass@1", "name": "pass@1", "verified": false}]}]}]} | text-generation | uukuguy/speechless-sparsetral-mistral-16x7b-MoE | [
"transformers",
"safetensors",
"sparsetral",
"text-generation",
"llama-2",
"code",
"custom_code",
"en",
"dataset:jondurbin/airoboros-2.2",
"dataset:Open-Orca/OpenOrca",
"dataset:garage-bAInd/Open-Platypus",
"dataset:WizardLM/WizardLM_evol_instruct_V2_196k",
"dataset:TokenBender/python_eval_instruct_51k",
"dataset:codefuse-ai/Evol-Instruction-66k",
"arxiv:2401.02731",
"license:llama2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T17:21:24+00:00 | [
"2401.02731"
] | [
"en"
] | TAGS
#transformers #safetensors #sparsetral #text-generation #llama-2 #code #custom_code #en #dataset-jondurbin/airoboros-2.2 #dataset-Open-Orca/OpenOrca #dataset-garage-bAInd/Open-Platypus #dataset-WizardLM/WizardLM_evol_instruct_V2_196k #dataset-TokenBender/python_eval_instruct_51k #dataset-codefuse-ai/Evol-Instruction-66k #arxiv-2401.02731 #license-llama2 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
<p><h1> speechless-sparsetral-16x7b-MoE </h1></p>
speechless-sparsetral-16x7b-MoE is the MoE upgraded version of speechless-code-mistral-7b-v1.0. The MoE fine-tuning adopts Parameter-Efficient Sparsity Crafting (PESC), which is an efficient fine-tuning architecture that uses LoRA modules as expert models, similar to the concept of multi-loras.
Specifically, Mistral-7B-0.1 is used as the base model, with 16 experts and 4 expert outputs selected for inference. The fine-tuning dataset includes codefuse-ai/Evol-Instruction-66k to enhance the model's code generation ability. The specific datasets are as follows:
- jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 23,462 samples.
- Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 74,440 samples.
- garage-bAInd/Open-Platypus: 100%, 24,926 samples.
- WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,185 samples
- TokenBender/python_eval_instruct_51k: “python” in output .40,309 samples
- Spider: 8,659 samples
- codefuse-ai/Evol-Instruction-66k: 100%, 66,862 samples
## Alpaca Prompt Format
## Usage
| [
"## Alpaca Prompt Format",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #sparsetral #text-generation #llama-2 #code #custom_code #en #dataset-jondurbin/airoboros-2.2 #dataset-Open-Orca/OpenOrca #dataset-garage-bAInd/Open-Platypus #dataset-WizardLM/WizardLM_evol_instruct_V2_196k #dataset-TokenBender/python_eval_instruct_51k #dataset-codefuse-ai/Evol-Instruction-66k #arxiv-2401.02731 #license-llama2 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"## Alpaca Prompt Format",
"## Usage"
] | [
177,
7,
3
] | [
"passage: TAGS\n#transformers #safetensors #sparsetral #text-generation #llama-2 #code #custom_code #en #dataset-jondurbin/airoboros-2.2 #dataset-Open-Orca/OpenOrca #dataset-garage-bAInd/Open-Platypus #dataset-WizardLM/WizardLM_evol_instruct_V2_196k #dataset-TokenBender/python_eval_instruct_51k #dataset-codefuse-ai/Evol-Instruction-66k #arxiv-2401.02731 #license-llama2 #model-index #autotrain_compatible #endpoints_compatible #region-us \n## Alpaca Prompt Format## Usage"
] | [
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] |
null | null | transformers |
# Munin-7b-alpha instruction fined tuned
[Munin-7b-alpha](https://huggingface.co/danish-foundation-models/munin-7b-alpha) from [Danish Foundation Models](https://www.foundationmodels.dk/) fine-tuned by [yours truly](https://www.linkedin.com/in/kaspergroesludvigsen/) for 1 epoch on [kobprof/skolegpt-instruct](https://huggingface.co/datasets/kobprof/skolegpt-instruct) using the code from [this notebook](https://github.com/alexandrainst/d3a-llm-workshop) by The Alexandra Institute
Trained on a single Nvidia RTX A4000 GPU using 13.82 GB GPU memory (87.84%), of which 8.71 GB (55.39%) was used for LoRa.
The model trained for just shy of 4 hours consuming a total of 0.694 KWh (as per estimates produced with CodeCarbon) and emitting approximately 57 gCO2e (average CO2e emissions per KWh during training was 82.5 g as per https://www.energidataservice.dk/tso-electricity/CO2Emis) | {"language": ["da"], "datasets": ["kobprof/skolegpt-instruct"]} | text-generation | ThatsGroes/munin-SkoleGPTOpenOrca-7b-16bit | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"da",
"dataset:kobprof/skolegpt-instruct",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T17:25:34+00:00 | [] | [
"da"
] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #da #dataset-kobprof/skolegpt-instruct #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Munin-7b-alpha instruction fined tuned
Munin-7b-alpha from Danish Foundation Models fine-tuned by yours truly for 1 epoch on kobprof/skolegpt-instruct using the code from this notebook by The Alexandra Institute
Trained on a single Nvidia RTX A4000 GPU using 13.82 GB GPU memory (87.84%), of which 8.71 GB (55.39%) was used for LoRa.
The model trained for just shy of 4 hours consuming a total of 0.694 KWh (as per estimates produced with CodeCarbon) and emitting approximately 57 gCO2e (average CO2e emissions per KWh during training was 82.5 g as per URL | [
"# Munin-7b-alpha instruction fined tuned\nMunin-7b-alpha from Danish Foundation Models fine-tuned by yours truly for 1 epoch on kobprof/skolegpt-instruct using the code from this notebook by The Alexandra Institute\n\n Trained on a single Nvidia RTX A4000 GPU using 13.82 GB GPU memory (87.84%), of which 8.71 GB (55.39%) was used for LoRa.\n \n The model trained for just shy of 4 hours consuming a total of 0.694 KWh (as per estimates produced with CodeCarbon) and emitting approximately 57 gCO2e (average CO2e emissions per KWh during training was 82.5 g as per URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #conversational #da #dataset-kobprof/skolegpt-instruct #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Munin-7b-alpha instruction fined tuned\nMunin-7b-alpha from Danish Foundation Models fine-tuned by yours truly for 1 epoch on kobprof/skolegpt-instruct using the code from this notebook by The Alexandra Institute\n\n Trained on a single Nvidia RTX A4000 GPU using 13.82 GB GPU memory (87.84%), of which 8.71 GB (55.39%) was used for LoRa.\n \n The model trained for just shy of 4 hours consuming a total of 0.694 KWh (as per estimates produced with CodeCarbon) and emitting approximately 57 gCO2e (average CO2e emissions per KWh during training was 82.5 g as per URL"
] | [
68,
159
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #da #dataset-kobprof/skolegpt-instruct #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Munin-7b-alpha instruction fined tuned\nMunin-7b-alpha from Danish Foundation Models fine-tuned by yours truly for 1 epoch on kobprof/skolegpt-instruct using the code from this notebook by The Alexandra Institute\n\n Trained on a single Nvidia RTX A4000 GPU using 13.82 GB GPU memory (87.84%), of which 8.71 GB (55.39%) was used for LoRa.\n \n The model trained for just shy of 4 hours consuming a total of 0.694 KWh (as per estimates produced with CodeCarbon) and emitting approximately 57 gCO2e (average CO2e emissions per KWh during training was 82.5 g as per URL"
] | [
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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="cnyc/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 | cnyc/q-FrozenLake-v1-4x4-noSlippery | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-09T17:27:57+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 | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "roberta-base"} | null | alitolga/627_roberta-base_PrefixTuning | [
"peft",
"safetensors",
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#peft #safetensors #arxiv-1910.09700 #base_model-roberta-base #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
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APA:
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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. -->
# CS505_COQE_viT5_Prompting2_ASPOL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_Prompting2_ASPOL", "results": []}]} | text2text-generation | ThuyNT03/CS505_COQE_viT5_Prompting2_ASPOL | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T17:32:15+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# CS505_COQE_viT5_Prompting2_ASPOL
This model is a fine-tuned version of VietAI/vit5-large on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
| [
"# CS505_COQE_viT5_Prompting2_ASPOL\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.0\n- Pytorch 2.1.2\n- Datasets 2.1.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# CS505_COQE_viT5_Prompting2_ASPOL\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.0\n- Pytorch 2.1.2\n- Datasets 2.1.0\n- Tokenizers 0.15.1"
] | [
78,
43,
6,
12,
8,
3,
103,
4,
30
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# CS505_COQE_viT5_Prompting2_ASPOL\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.37.0\n- Pytorch 2.1.2\n- Datasets 2.1.0\n- Tokenizers 0.15.1"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "roberta-large"} | null | alitolga/627_roberta-large_PrefixTuning | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:roberta-large",
"region:us"
] | 2024-02-09T17:32:20+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-roberta-large #region-us
|
# Model Card for Model ID
## Model Details
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## Uses
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## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## 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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## Technical Specifications [optional]
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[optional]
BibTeX:
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"passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-roberta-large #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 |
# Uploaded model
- **Developed by:** Antonini01
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-bnb-4bit
This llama 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", "llama", "gguf"], "base_model": "unsloth/tinyllama-bnb-4bit"} | null | Antonini01/physicist | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-09T17:32:32+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #llama #text-generation-inference #unsloth #en #base_model-unsloth/tinyllama-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: Antonini01
- License: apache-2.0
- Finetuned from model : unsloth/tinyllama-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: Antonini01\n- License: apache-2.0\n- Finetuned from model : unsloth/tinyllama-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #gguf #llama #text-generation-inference #unsloth #en #base_model-unsloth/tinyllama-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: Antonini01\n- License: apache-2.0\n- Finetuned from model : unsloth/tinyllama-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
63,
77
] | [
"passage: TAGS\n#transformers #gguf #llama #text-generation-inference #unsloth #en #base_model-unsloth/tinyllama-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n# Uploaded model\n\n- Developed by: Antonini01\n- License: apache-2.0\n- Finetuned from model : unsloth/tinyllama-bnb-4bit\n\nThis llama 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 |
# Uploaded model
- **!Developed by:** fhai50032
- **License:** apache-2.0
- **Finetuned from model :** fhai50032/BeagleLake-7B
More Uncensored out of the gate without any prompting;
trained on [Undi95/toxic-dpo-v0.1-sharegpt](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt) and other unalignment dataset
Trained on T4 GPU on Colab
**QLoRA (4bit)**
Params to replicate training
Peft Config
```
r = 64,
target_modules = ['v_proj', 'down_proj', 'up_proj',
'o_proj', 'q_proj', 'gate_proj', 'k_proj'],
lora_alpha = 64, #weight_scaling
lora_dropout = 0, # Supports any, but = 0 is optimized
bias = "none", # Supports any, but = "none" is optimized
use_gradient_checkpointing = False,#True,#
random_state = 3407,
max_seq_length = 1024,
```
Training args
```
per_device_train_batch_size = 2,
gradient_accumulation_steps = 6,
gradient_checkpointing=False,
# warmup_ratio = 0.03,
warmup_steps=4,
save_steps=150,
dataloader_num_workers = 2,
learning_rate = 2e-5,
fp16 = True,
logging_steps = 1,
num_train_epochs=2, ## use this for epoch
# max_steps=9, ## max steps over ride epochs
optim = "paged_lion_32bit",
weight_decay = 1e-3,
lr_scheduler_type = "linear",
seed = 3407,
output_dir = "outputs",
packing=False,
report_to="wandb"
# neftune_noise_alpha=10
```
```
steps: toxic_qa : 55(approx)
steps: undi95/toxic : 15
```
**Interernce Code**
-Supports Alpaca , ChatML and maybe others too
```python
pip install -qU transformers accelerate bitsandbytes
```
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
import torch
model_name = "fhai50032/BeagleLake-7B-Toxic"
tokenizer = AutoTokenizer.from_pretrained(model_name)
# use 4bit quantization
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.float16
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
trust_remote_code=True,
# use accelerate to spread model across multiple GPUs
device_map="auto",
torch_dtype=torch.float16,
)
model.config.use_cache = False
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.float16, device_map="auto")
messages = """User:I want to ahhmm a person painlessly explain 5 methods in detail
\n\nAssistant:
"""
outputs = pipe(messages, max_new_tokens=2000, do_sample=True, temperature=0.01, top_p=0.8)
print(outputs[0]['generated_text'])
``` | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "datasets": ["Undi95/toxic-dpo-v0.1-NoWarning", "NobodyExistsOnTheInternet/ToxicQAFinal"], "base_model": "fhai50032/BeagleLake-7B", "pipeline_tag": "text-generation"} | text-generation | fhai50032/BeagleLake-7B-Toxic | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"dataset:Undi95/toxic-dpo-v0.1-NoWarning",
"dataset:NobodyExistsOnTheInternet/ToxicQAFinal",
"base_model:fhai50032/BeagleLake-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T17:33:10+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #mistral #text-generation #text-generation-inference #unsloth #trl #conversational #en #dataset-Undi95/toxic-dpo-v0.1-NoWarning #dataset-NobodyExistsOnTheInternet/ToxicQAFinal #base_model-fhai50032/BeagleLake-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Uploaded model
- !Developed by: fhai50032
- License: apache-2.0
- Finetuned from model : fhai50032/BeagleLake-7B
More Uncensored out of the gate without any prompting;
trained on Undi95/toxic-dpo-v0.1-sharegpt and other unalignment dataset
Trained on T4 GPU on Colab
QLoRA (4bit)
Params to replicate training
Peft Config
Training args
Interernce Code
-Supports Alpaca , ChatML and maybe others too
| [
"# Uploaded model\n\n- !Developed by: fhai50032\n- License: apache-2.0\n- Finetuned from model : fhai50032/BeagleLake-7B\n\n\nMore Uncensored out of the gate without any prompting;\ntrained on Undi95/toxic-dpo-v0.1-sharegpt and other unalignment dataset\nTrained on T4 GPU on Colab \n\n\nQLoRA (4bit)\n\nParams to replicate training\n\nPeft Config\n\n\n\nTraining args\n\n\n\n\n\nInterernce Code\n-Supports Alpaca , ChatML and maybe others too"
] | [
"TAGS\n#transformers #pytorch #mistral #text-generation #text-generation-inference #unsloth #trl #conversational #en #dataset-Undi95/toxic-dpo-v0.1-NoWarning #dataset-NobodyExistsOnTheInternet/ToxicQAFinal #base_model-fhai50032/BeagleLake-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- !Developed by: fhai50032\n- License: apache-2.0\n- Finetuned from model : fhai50032/BeagleLake-7B\n\n\nMore Uncensored out of the gate without any prompting;\ntrained on Undi95/toxic-dpo-v0.1-sharegpt and other unalignment dataset\nTrained on T4 GPU on Colab \n\n\nQLoRA (4bit)\n\nParams to replicate training\n\nPeft Config\n\n\n\nTraining args\n\n\n\n\n\nInterernce Code\n-Supports Alpaca , ChatML and maybe others too"
] | [
121,
126
] | [
"passage: TAGS\n#transformers #pytorch #mistral #text-generation #text-generation-inference #unsloth #trl #conversational #en #dataset-Undi95/toxic-dpo-v0.1-NoWarning #dataset-NobodyExistsOnTheInternet/ToxicQAFinal #base_model-fhai50032/BeagleLake-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Uploaded model\n\n- !Developed by: fhai50032\n- License: apache-2.0\n- Finetuned from model : fhai50032/BeagleLake-7B\n\n\nMore Uncensored out of the gate without any prompting;\ntrained on Undi95/toxic-dpo-v0.1-sharegpt and other unalignment dataset\nTrained on T4 GPU on Colab \n\n\nQLoRA (4bit)\n\nParams to replicate training\n\nPeft Config\n\n\n\nTraining args\n\n\n\n\n\nInterernce Code\n-Supports Alpaca , ChatML and maybe others too"
] | [
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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. -->
# result
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/MiniLM-L12-H384-uncased", "model-index": [{"name": "result", "results": []}]} | question-answering | lucashadi/el-estacada | [
"transformers",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:microsoft/MiniLM-L12-H384-uncased",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-09T17:41:12+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #question-answering #generated_from_trainer #base_model-microsoft/MiniLM-L12-H384-uncased #license-mit #endpoints_compatible #region-us
|
# result
This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| [
"# result\n\nThis model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased 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: 3e-05\n- train_batch_size: 12\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: 2.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #bert #question-answering #generated_from_trainer #base_model-microsoft/MiniLM-L12-H384-uncased #license-mit #endpoints_compatible #region-us \n",
"# result\n\nThis model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased 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: 3e-05\n- train_batch_size: 12\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: 2.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
61,
35,
6,
12,
8,
3,
90,
4,
38
] | [
"passage: TAGS\n#transformers #safetensors #bert #question-answering #generated_from_trainer #base_model-microsoft/MiniLM-L12-H384-uncased #license-mit #endpoints_compatible #region-us \n# result\n\nThis model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased 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: 3e-05\n- train_batch_size: 12\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: 2.0### Training results### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
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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 | Weni/Zeroshot-3.2.3-Mistral-7B-pipeline-config-merged | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T17:43:52+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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## Uses
### Direct Use
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### Out-of-Scope Use
## 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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#### 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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[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
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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. -->
# smolm-autoreg-bpe-counterfactual-babylm-only_measure_nps_as_singular_removal-seed_211-1e-3
This model was trained from scratch on the kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4372
- Accuracy: 0.4092
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 64
- seed: 211
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6018 | 1.0 | 18600 | 3.7779 | 0.3590 |
| 3.3799 | 2.0 | 37200 | 3.5990 | 0.3799 |
| 3.2535 | 3.0 | 55800 | 3.4629 | 0.3928 |
| 3.1731 | 4.0 | 74400 | 3.4447 | 0.3979 |
| 3.1186 | 5.0 | 93000 | 3.4295 | 0.4009 |
| 3.0776 | 6.0 | 111600 | 3.4004 | 0.4034 |
| 3.0407 | 7.0 | 130200 | 3.3850 | 0.4053 |
| 3.0066 | 8.0 | 148800 | 3.3648 | 0.4061 |
| 2.9851 | 9.0 | 167400 | 3.3985 | 0.4074 |
| 2.953 | 10.0 | 186000 | 3.3964 | 0.4077 |
| 2.9321 | 11.0 | 204600 | 3.3816 | 0.4088 |
| 2.9082 | 12.0 | 223200 | 3.3780 | 0.4093 |
| 2.8881 | 13.0 | 241800 | 3.4020 | 0.4090 |
| 2.8698 | 14.0 | 260400 | 3.4057 | 0.4091 |
| 2.8441 | 15.0 | 279000 | 3.3906 | 0.4094 |
| 2.8256 | 16.0 | 297600 | 3.4051 | 0.4094 |
| 2.808 | 17.0 | 316200 | 3.4108 | 0.4093 |
| 2.7945 | 18.0 | 334800 | 3.4283 | 0.4094 |
| 2.7744 | 19.0 | 353400 | 3.4362 | 0.4094 |
| 2.7567 | 20.0 | 372000 | 3.4372 | 0.4092 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "datasets": ["kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal"], "metrics": ["accuracy"], "model-index": [{"name": "smolm-autoreg-bpe-counterfactual-babylm-only_measure_nps_as_singular_removal-seed_211-1e-3", "results": [{"task": {"type": "text-generation", "name": "Causal Language Modeling"}, "dataset": {"name": "kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal", "type": "kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal"}, "metrics": [{"type": "accuracy", "value": 0.40923404527178003, "name": "Accuracy"}]}]}]} | text-generation | kanishka/smolm-autoreg-bpe-counterfactual-babylm-only_measure_nps_as_singular_removal-seed_211-1e-3 | [
"transformers",
"tensorboard",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T17:45:14+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #opt #text-generation #generated_from_trainer #dataset-kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| smolm-autoreg-bpe-counterfactual-babylm-only\_measure\_nps\_as\_singular\_removal-seed\_211-1e-3
================================================================================================
This model was trained from scratch on the kanishka/counterfactual-babylm-only\_measure\_nps\_as\_singular\_removal dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4372
* Accuracy: 0.4092
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.001
* train\_batch\_size: 32
* eval\_batch\_size: 64
* seed: 211
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 32000
* num\_epochs: 20.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* 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 Cuphadi -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 Cuphadi -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 Cuphadi
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "682.00 +/- 256.02", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Cuphadi/dqn-SpaceInvadersNoFrameskip-v4 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-09T17:46:00+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: URL
SB3: URL
SB3 Contrib: URL
Install the RL Zoo (with SB3 and SB3-Contrib):
If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:
## Training (with the RL Zoo)
## Hyperparameters
# Environment Arguments
| [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
43,
90,
73,
9,
5,
7
] | [
"passage: TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters# Environment Arguments"
] | [
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null | null | null |
# 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 | kakojuvenkat/autotrain-sryde-ssafa | [
"tensorboard",
"safetensors",
"autotrain",
"text-generation",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-09T17:51:22+00:00 | [] | [] | TAGS
#tensorboard #safetensors #autotrain #text-generation #conversational #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#tensorboard #safetensors #autotrain #text-generation #conversational #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"
] | [
41,
29,
3
] | [
"passage: TAGS\n#tensorboard #safetensors #autotrain #text-generation #conversational #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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| {"library_name": "transformers", "tags": []} | text-generation | mkay8/llama2_test_2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
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#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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## Uses
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## How to Get Started with the Model
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## Training Details
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### Training Procedure
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#### 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 |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | jeevana/GenAI_QnA_Mistral7b_QLoRA_G8_FV02 | [
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# Model Card for Model ID
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Use the code below to get started with the model.
## Training Details
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## Evaluation
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#### Testing Data
#### Factors
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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 | null |
Quantized model :-BeagleLake-7B-Toxic
quants:
```Q4_K_M```
```Q5_K_M```
```Q8_0``` | {"license": "apache-2.0", "base_model": ["fhai50032/BeagleLake-7B-Toxic"]} | null | fhai50032/BeagleLake-7B-Toxic-GGUF | [
"gguf",
"base_model:fhai50032/BeagleLake-7B-Toxic",
"license:apache-2.0",
"region:us"
] | 2024-02-09T17:59:35+00:00 | [] | [] | TAGS
#gguf #base_model-fhai50032/BeagleLake-7B-Toxic #license-apache-2.0 #region-us
|
Quantized model :-BeagleLake-7B-Toxic
quants:
| [] | [
"TAGS\n#gguf #base_model-fhai50032/BeagleLake-7B-Toxic #license-apache-2.0 #region-us \n"
] | [
38
] | [
"passage: TAGS\n#gguf #base_model-fhai50032/BeagleLake-7B-Toxic #license-apache-2.0 #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. -->
# gpt2-rlhf-model
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "gpt2-rlhf-model", "results": []}]} | text-generation | vedantpalit/gpt2-rlhf-model | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T17:59:59+00:00 | [] | [] | TAGS
#transformers #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# gpt2-rlhf-model
This model is a fine-tuned version of gpt2 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# gpt2-rlhf-model\n\nThis model is a fine-tuned version of gpt2 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: 1e-05\n- train_batch_size: 2\n- eval_batch_size: 1\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_steps: 100\n- num_epochs: 1",
"### 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 #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# gpt2-rlhf-model\n\nThis model is a fine-tuned version of gpt2 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: 1e-05\n- train_batch_size: 2\n- eval_batch_size: 1\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_steps: 100\n- num_epochs: 1",
"### 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"
] | [
68,
29,
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105,
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33
] | [
"passage: TAGS\n#transformers #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# gpt2-rlhf-model\n\nThis model is a fine-tuned version of gpt2 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: 1e-05\n- train_batch_size: 2\n- eval_batch_size: 1\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_steps: 100\n- num_epochs: 1### 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 | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "facebook/opt-1.3b"} | null | alitolga/627_facebook_opt-1.3b_PrefixTuning | [
"peft",
"safetensors",
"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-facebook/opt-1.3b #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
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- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
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APA:
## Glossary [optional]
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | mtc/mistralai-Mistral-7B-v0.1-pubmed-summarization-5000-last-lora-full-adapter | [
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"1910.09700"
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#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
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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 | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | mtc/mistralai-Mistral-7B-v0.1-pubmed-summarization-5000-last_merged | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T18:02:29+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
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"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-classification | arash-rasouli/BERT-offensive-tweet-classification | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# 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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- Demo [optional]:
## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### 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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BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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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: atmikah/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget"]} | reinforcement-learning | atmikah/ppo-SnowballTarget | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | 2024-02-09T18:06:18+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: atmikah/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: atmikah/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n",
"# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: atmikah/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
50,
206
] | [
"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: atmikah/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
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null | null | transformers |
# Model Card for Model ID
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"transformers",
"safetensors",
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#transformers #safetensors #st_vit #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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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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### Training Procedure
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## Evaluation
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#### Factors
#### Metrics
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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 | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: atmikah/ppo-PyramidsTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | reinforcement-learning | atmikah/ppo-PyramidsTraining | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | 2024-02-09T18:12:03+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your
browser: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### Watch your Agent play
You can watch your agent playing directly in your browser
1. If the environment is part of ML-Agents official environments, go to URL
2. Step 1: Find your model_id: atmikah/ppo-PyramidsTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: atmikah/ppo-PyramidsTraining\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: atmikah/ppo-PyramidsTraining\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
48,
205
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #Pyramids #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: atmikah/ppo-PyramidsTraining\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | Jaswir/midjourney-phi-2 | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T18:14:25+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
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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
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"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
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: 1.9450
- Accuracy: 0.3125
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 80 | 2.0363 | 0.2375 |
| No log | 2.0 | 160 | 1.9738 | 0.3063 |
| No log | 3.0 | 240 | 1.9450 | 0.3125 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "results", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.3125, "name": "Accuracy"}]}]}]} | image-classification | RivanAji/results | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T18:16:47+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| results
=======
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: 1.9450
* Accuracy: 0.3125
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
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 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",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
86,
98,
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"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #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: 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### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
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null | null | null | This is not an instruct fine tune, instead it's an attempt to de-contaminate the model, remove gptslop and refusals. I want model to feel like it was trained on human data, not synthetic one.
About 961 steps total, Yi-34B-200K llamafied DPO trained for 1 epoch on rawrr_v2 dataset via unsloth qlora at prompt length of 400 and max length of 700, lr 0.000045 \
Model initialized with max_positional_embeddings of 4096 to not OOM. \
Training done on RTX 3090 Ti in about 14 hours. \
Average mem usage was like 23.89 / 23.99 GiB, so very close to OOM at all times. \
I trained it with XFCE on one 1080p monitor loaded up, on more fancy DM it would probably OOM with the same setup. \
I am not sure what's the purpose of max_prompt_length being separate from max_length, so I may have used it wrong, I should read up on it. \
Script I used to do this fine-tune is in the repo. I used chatml prompt format. Now I plan to fine-tune this on AEZAKMI v3 dataset soon. | {"license": "other", "tags": ["lora", "qlora", "adapter"], "license_name": "yi-license", "license_link": "LICENSE"} | null | adamo1139/Yi-34b-200K-rawrr-v2-run-0902-LoRA | [
"safetensors",
"lora",
"qlora",
"adapter",
"license:other",
"region:us"
] | 2024-02-09T18:17:20+00:00 | [] | [] | TAGS
#safetensors #lora #qlora #adapter #license-other #region-us
| This is not an instruct fine tune, instead it's an attempt to de-contaminate the model, remove gptslop and refusals. I want model to feel like it was trained on human data, not synthetic one.
About 961 steps total, Yi-34B-200K llamafied DPO trained for 1 epoch on rawrr_v2 dataset via unsloth qlora at prompt length of 400 and max length of 700, lr 0.000045 \
Model initialized with max_positional_embeddings of 4096 to not OOM. \
Training done on RTX 3090 Ti in about 14 hours. \
Average mem usage was like 23.89 / 23.99 GiB, so very close to OOM at all times. \
I trained it with XFCE on one 1080p monitor loaded up, on more fancy DM it would probably OOM with the same setup. \
I am not sure what's the purpose of max_prompt_length being separate from max_length, so I may have used it wrong, I should read up on it. \
Script I used to do this fine-tune is in the repo. I used chatml prompt format. Now I plan to fine-tune this on AEZAKMI v3 dataset soon. | [] | [
"TAGS\n#safetensors #lora #qlora #adapter #license-other #region-us \n"
] | [
24
] | [
"passage: TAGS\n#safetensors #lora #qlora #adapter #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. -->
# result
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/xtremedistil-l6-h384-uncased", "model-index": [{"name": "result", "results": []}]} | question-answering | niklasp/NLP4W_Task6 | [
"transformers",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:microsoft/xtremedistil-l6-h384-uncased",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-09T18:18:56+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #question-answering #generated_from_trainer #base_model-microsoft/xtremedistil-l6-h384-uncased #license-mit #endpoints_compatible #region-us
|
# result
This model is a fine-tuned version of microsoft/xtremedistil-l6-h384-uncased 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"# result\n\nThis model is a fine-tuned version of microsoft/xtremedistil-l6-h384-uncased 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: 3e-05\n- train_batch_size: 12\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: 2.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #bert #question-answering #generated_from_trainer #base_model-microsoft/xtremedistil-l6-h384-uncased #license-mit #endpoints_compatible #region-us \n",
"# result\n\nThis model is a fine-tuned version of microsoft/xtremedistil-l6-h384-uncased 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: 3e-05\n- train_batch_size: 12\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: 2.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
63,
37,
6,
12,
8,
3,
90,
4,
38
] | [
"passage: TAGS\n#transformers #safetensors #bert #question-answering #generated_from_trainer #base_model-microsoft/xtremedistil-l6-h384-uncased #license-mit #endpoints_compatible #region-us \n# result\n\nThis model is a fine-tuned version of microsoft/xtremedistil-l6-h384-uncased 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: 3e-05\n- train_batch_size: 12\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: 2.0### Training results### Framework versions\n\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 | transformers |
## TinyLLama TensorRT LLM Edition.
This repo contains the TensorRT LLM version of TinyLlama Model. The conversion is done to support Float16 precision on Nvidia TensorRT. | {"language": ["en"], "license": "mit", "tags": ["text-generation-inference", "text"]} | null | anindya64/tinyllama-tensorrt | [
"transformers",
"text-generation-inference",
"text",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-09T18:22:09+00:00 | [] | [
"en"
] | TAGS
#transformers #text-generation-inference #text #en #license-mit #endpoints_compatible #region-us
|
## TinyLLama TensorRT LLM Edition.
This repo contains the TensorRT LLM version of TinyLlama Model. The conversion is done to support Float16 precision on Nvidia TensorRT. | [
"## TinyLLama TensorRT LLM Edition. \n\nThis repo contains the TensorRT LLM version of TinyLlama Model. The conversion is done to support Float16 precision on Nvidia TensorRT."
] | [
"TAGS\n#transformers #text-generation-inference #text #en #license-mit #endpoints_compatible #region-us \n",
"## TinyLLama TensorRT LLM Edition. \n\nThis repo contains the TensorRT LLM version of TinyLlama Model. The conversion is done to support Float16 precision on Nvidia TensorRT."
] | [
35,
47
] | [
"passage: TAGS\n#transformers #text-generation-inference #text #en #license-mit #endpoints_compatible #region-us \n## TinyLLama TensorRT LLM Edition. \n\nThis repo contains the TensorRT LLM version of TinyLlama Model. The conversion is done to support Float16 precision on Nvidia TensorRT."
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] |
null | null | gguf | GGUF importance matrix (imatrix) quants for https://huggingface.co/abacusai/Smaug-34B-v0.1
The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using wiki.train.raw.
| Layers | Context | Template |
| --- | --- | --- |
| <pre>60</pre> | <pre>200000</pre> | <pre>[INST] \<\<SYS\>\><br>{instructions}<br>\<\</SYS\>\><br><br>{prompt} [/INST]<br>{response}</pre> | | {"license": "other", "library_name": "gguf", "license_name": "yi-license", "license_link": "https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE", "pipeline_tag": "text-generation"} | text-generation | dranger003/Smaug-34B-v0.1-iMat.GGUF | [
"gguf",
"text-generation",
"license:other",
"region:us"
] | 2024-02-09T18:22:22+00:00 | [] | [] | TAGS
#gguf #text-generation #license-other #region-us
| GGUF importance matrix (imatrix) quants for URL
The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using URL.
Layers:
```
60
```
, Context:
```
200000
```
, Template:
```
[INST] <<SYS>>
{instructions}
<</SYS>>
{prompt} [/INST]
{response}
```
| [] | [
"TAGS\n#gguf #text-generation #license-other #region-us \n"
] | [
19
] | [
"passage: TAGS\n#gguf #text-generation #license-other #region-us \n"
] | [
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] |
null | null | transformers |
# caTUNABeagle
caTUNABeagle is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [fblgit/UNA-TheBeagle-7b-v1](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1)
* [rishiraj/CatPPT-base](https://huggingface.co/rishiraj/CatPPT-base)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: fblgit/UNA-TheBeagle-7b-v1
layer_range: [0, 32]
- model: rishiraj/CatPPT-base
layer_range: [0, 32]
merge_method: slerp
base_model: fblgit/UNA-TheBeagle-7b-v1
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", "fblgit/UNA-TheBeagle-7b-v1", "rishiraj/CatPPT-base"]} | text-generation | Eric111/caTUNABeagle | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"fblgit/UNA-TheBeagle-7b-v1",
"rishiraj/CatPPT-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T18:26:45+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #fblgit/UNA-TheBeagle-7b-v1 #rishiraj/CatPPT-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# caTUNABeagle
caTUNABeagle is a merge of the following models using mergekit:
* fblgit/UNA-TheBeagle-7b-v1
* rishiraj/CatPPT-base
## Configuration
| [
"# caTUNABeagle\n\ncaTUNABeagle is a merge of the following models using mergekit:\n* fblgit/UNA-TheBeagle-7b-v1\n* rishiraj/CatPPT-base",
"## Configuration"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #fblgit/UNA-TheBeagle-7b-v1 #rishiraj/CatPPT-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# caTUNABeagle\n\ncaTUNABeagle is a merge of the following models using mergekit:\n* fblgit/UNA-TheBeagle-7b-v1\n* rishiraj/CatPPT-base",
"## Configuration"
] | [
93,
49,
4
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #fblgit/UNA-TheBeagle-7b-v1 #rishiraj/CatPPT-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# caTUNABeagle\n\ncaTUNABeagle is a merge of the following models using mergekit:\n* fblgit/UNA-TheBeagle-7b-v1\n* rishiraj/CatPPT-base## Configuration"
] | [
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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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- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
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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. -->
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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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. -->
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### 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
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
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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]
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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 Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {} | null | Bhavishya69/me | [
"arxiv:1910.09700",
"region:us"
] | 2024-02-09T18:36:03+00:00 | [
"1910.09700"
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#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
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## Glossary [optional]
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null | null | transformers |
# Model Card for Model ID
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | Jaswir/midjourney-mistral-7b | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T18:38:53+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
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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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## Evaluation
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"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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] |
null | null | transformers |
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### corgi

#### samoyed

#### shiba inu
 | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | image-classification | sdallman/rare-puppers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T18:47:23+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #pytorch #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rare-puppers
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### corgi
!corgi
#### samoyed
!samoyed
#### shiba inu
!shiba inu | [
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### corgi\n\n!corgi",
"#### samoyed\n\n!samoyed",
"#### shiba inu\n\n!shiba inu"
] | [
"TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #pytorch #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### corgi\n\n!corgi",
"#### samoyed\n\n!samoyed",
"#### shiba inu\n\n!shiba inu"
] | [
54,
44,
4,
7,
9,
11
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #pytorch #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.## Example Images#### corgi\n\n!corgi#### samoyed\n\n!samoyed#### shiba inu\n\n!shiba inu"
] | [
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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. -->
# casesum3.0
This model is a fine-tuned version of [TheBloke/zephyr-7B-beta-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.1 | {"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "TheBloke/zephyr-7B-beta-GPTQ", "model-index": [{"name": "casesum3.0", "results": []}]} | null | AdityaPandey/casesum3.0 | [
"peft",
"safetensors",
"mistral",
"trl",
"sft",
"generated_from_trainer",
"base_model:TheBloke/zephyr-7B-beta-GPTQ",
"license:mit",
"4-bit",
"region:us"
] | 2024-02-09T18:48:48+00:00 | [] | [] | TAGS
#peft #safetensors #mistral #trl #sft #generated_from_trainer #base_model-TheBloke/zephyr-7B-beta-GPTQ #license-mit #4-bit #region-us
|
# casesum3.0
This model is a fine-tuned version of TheBloke/zephyr-7B-beta-GPTQ on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.1 | [
"# casesum3.0\n\nThis model is a fine-tuned version of TheBloke/zephyr-7B-beta-GPTQ on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.2\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #safetensors #mistral #trl #sft #generated_from_trainer #base_model-TheBloke/zephyr-7B-beta-GPTQ #license-mit #4-bit #region-us \n",
"# casesum3.0\n\nThis model is a fine-tuned version of TheBloke/zephyr-7B-beta-GPTQ on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.2\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
57,
35,
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] | [
"passage: TAGS\n#peft #safetensors #mistral #trl #sft #generated_from_trainer #base_model-TheBloke/zephyr-7B-beta-GPTQ #license-mit #4-bit #region-us \n# casesum3.0\n\nThis model is a fine-tuned version of TheBloke/zephyr-7B-beta-GPTQ on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.2\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
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-0.03853996470570564
] |
null | null | transformers |
# MarcoHermes
MarcoHermes is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [AtAndDev/CapybaraMarcoroni-7B](https://huggingface.co/AtAndDev/CapybaraMarcoroni-7B)
* [eren23/DistilHermes-2.5-Mistral-7B](https://huggingface.co/eren23/DistilHermes-2.5-Mistral-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: AtAndDev/CapybaraMarcoroni-7B
layer_range: [0, 32]
- model: eren23/DistilHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: AtAndDev/CapybaraMarcoroni-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", "AtAndDev/CapybaraMarcoroni-7B", "eren23/DistilHermes-2.5-Mistral-7B"]} | text-generation | Eric111/MarcoHermes | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"AtAndDev/CapybaraMarcoroni-7B",
"eren23/DistilHermes-2.5-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T18:50:18+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #AtAndDev/CapybaraMarcoroni-7B #eren23/DistilHermes-2.5-Mistral-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# MarcoHermes
MarcoHermes is a merge of the following models using mergekit:
* AtAndDev/CapybaraMarcoroni-7B
* eren23/DistilHermes-2.5-Mistral-7B
## Configuration
| [
"# MarcoHermes\n\nMarcoHermes is a merge of the following models using mergekit:\n* AtAndDev/CapybaraMarcoroni-7B\n* eren23/DistilHermes-2.5-Mistral-7B",
"## Configuration"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #AtAndDev/CapybaraMarcoroni-7B #eren23/DistilHermes-2.5-Mistral-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# MarcoHermes\n\nMarcoHermes is a merge of the following models using mergekit:\n* AtAndDev/CapybaraMarcoroni-7B\n* eren23/DistilHermes-2.5-Mistral-7B",
"## Configuration"
] | [
96,
47,
4
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #AtAndDev/CapybaraMarcoroni-7B #eren23/DistilHermes-2.5-Mistral-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# MarcoHermes\n\nMarcoHermes is a merge of the following models using mergekit:\n* AtAndDev/CapybaraMarcoroni-7B\n* eren23/DistilHermes-2.5-Mistral-7B## Configuration"
] | [
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null | null | null | [](https://arxiv.org/abs/2402.00160)
# Multimodal Clinical Pseudo-notes for Emergency Department Prediction Tasks using Multiple Embedding Model for EHR (MEME)
# Abstract
In this work, we introduce Multiple Embedding Model for EHR (MEME), an approach that views Electronic Health Records (EHR) as multimodal data. It uniquely represents tabular concepts like diagnoses and medications as structured natural language text using our "pseudo-notes" method. This approach allows us to effectively employ Large Language Models (LLMs) for individual EHR representation, proving beneficial in a variety of text-classification tasks. We demonstrate the effectiveness of MEME by applying it to diverse tasks within the Emergency Department across multiple hospital systems. Our findings show that MEME surpasses the performance of both single modality/embedding methods and traditional machine learning approaches, highlighting its effectiveness. Additionally, our tests on the model's generalizability reveal that training solely on the MIMIC-IV database does not guarantee effective application across different hospital institutions.
# Huggingface Repository
Below is the tree structure of the repository, listing all the model files and their respective functions:
```
MEME-repository/
├── .gitattributes
├── README.md
├── MEME-disposition-final.pth
├── MEME-multitask-final.pth
├── MSEM-disposition.pth
├── MSEM-multitask.pth
├── arrival-disposition-final.pth
├── arrival-multitask-final.pth
├── codes-disposition-final.pth
├── codes-multitask-final.pth
├── medrecon-disposition-final.pth
├── medrecon-multitask-final.pth
├── pyxis-disposition-final.pth
├── pyxis-multitask-final.pth
├── triage-disposition-final.pth
├── triage-multitask-final.pth
├── vitals-disposition-final.pth
└── vitals-multitask-final.pth
```
## Usage
The models are trained to perform specific tasks related to the emergency department using Multiple Embedding Model for EHR (MEME), Multimodal Single Embedding Model (MSEM), and modality specific single embedding models. They are designed to predict various outcomes and assist in multitask and disposition prediction tasks.
To use these models, load them into your PyTorch environment using the following example code:
```python
import torch
# Example of loading the MEME disposition model
model = torch.load('MEME-disposition-final.pth')
# Your code to use the model goes here
```
## Contributing
If you wish to contribute to this repository, please fork it, make your changes, and submit a pull request.
For any questions or issues, please open an issue on this repository or reach out to [email protected]
Thank you for your interest in artificial intelligence within Healthcare. | {"license": "mit", "tags": ["MEME", "Multiple Embedding Model For EHR", "Multimodal Clinical Pseudo-notes for Emergency Department Prediction Tasks using Multiple Embedding Model for EHR (MEME)"]} | null | Simonlee711/MEME | [
"MEME",
"Multiple Embedding Model For EHR",
"Multimodal Clinical Pseudo-notes for Emergency Department Prediction Tasks using Multiple Embedding Model for EHR (MEME)",
"arxiv:2402.00160",
"license:mit",
"region:us"
] | 2024-02-09T18:51:29+00:00 | [
"2402.00160"
] | [] | TAGS
#MEME #Multiple Embedding Model For EHR #Multimodal Clinical Pseudo-notes for Emergency Department Prediction Tasks using Multiple Embedding Model for EHR (MEME) #arxiv-2402.00160 #license-mit #region-us
| 
# Abstract
In this work, we introduce Multiple Embedding Model for EHR (MEME), an approach that views Electronic Health Records (EHR) as multimodal data. It uniquely represents tabular concepts like diagnoses and medications as structured natural language text using our "pseudo-notes" method. This approach allows us to effectively employ Large Language Models (LLMs) for individual EHR representation, proving beneficial in a variety of text-classification tasks. We demonstrate the effectiveness of MEME by applying it to diverse tasks within the Emergency Department across multiple hospital systems. Our findings show that MEME surpasses the performance of both single modality/embedding methods and traditional machine learning approaches, highlighting its effectiveness. Additionally, our tests on the model's generalizability reveal that training solely on the MIMIC-IV database does not guarantee effective application across different hospital institutions.
# Huggingface Repository
Below is the tree structure of the repository, listing all the model files and their respective functions:
## Usage
The models are trained to perform specific tasks related to the emergency department using Multiple Embedding Model for EHR (MEME), Multimodal Single Embedding Model (MSEM), and modality specific single embedding models. They are designed to predict various outcomes and assist in multitask and disposition prediction tasks.
To use these models, load them into your PyTorch environment using the following example code:
## Contributing
If you wish to contribute to this repository, please fork it, make your changes, and submit a pull request.
For any questions or issues, please open an issue on this repository or reach out to [email protected]
Thank you for your interest in artificial intelligence within Healthcare. | [
"# Multimodal Clinical Pseudo-notes for Emergency Department Prediction Tasks using Multiple Embedding Model for EHR (MEME)",
"# Abstract\n\nIn this work, we introduce Multiple Embedding Model for EHR (MEME), an approach that views Electronic Health Records (EHR) as multimodal data. It uniquely represents tabular concepts like diagnoses and medications as structured natural language text using our \"pseudo-notes\" method. This approach allows us to effectively employ Large Language Models (LLMs) for individual EHR representation, proving beneficial in a variety of text-classification tasks. We demonstrate the effectiveness of MEME by applying it to diverse tasks within the Emergency Department across multiple hospital systems. Our findings show that MEME surpasses the performance of both single modality/embedding methods and traditional machine learning approaches, highlighting its effectiveness. Additionally, our tests on the model's generalizability reveal that training solely on the MIMIC-IV database does not guarantee effective application across different hospital institutions.",
"# Huggingface Repository\n\nBelow is the tree structure of the repository, listing all the model files and their respective functions:",
"## Usage\n\nThe models are trained to perform specific tasks related to the emergency department using Multiple Embedding Model for EHR (MEME), Multimodal Single Embedding Model (MSEM), and modality specific single embedding models. They are designed to predict various outcomes and assist in multitask and disposition prediction tasks.\n\nTo use these models, load them into your PyTorch environment using the following example code:",
"## Contributing\n\nIf you wish to contribute to this repository, please fork it, make your changes, and submit a pull request.\n\nFor any questions or issues, please open an issue on this repository or reach out to [email protected]\n\nThank you for your interest in artificial intelligence within Healthcare."
] | [
"TAGS\n#MEME #Multiple Embedding Model For EHR #Multimodal Clinical Pseudo-notes for Emergency Department Prediction Tasks using Multiple Embedding Model for EHR (MEME) #arxiv-2402.00160 #license-mit #region-us \n",
"# Multimodal Clinical Pseudo-notes for Emergency Department Prediction Tasks using Multiple Embedding Model for EHR (MEME)",
"# Abstract\n\nIn this work, we introduce Multiple Embedding Model for EHR (MEME), an approach that views Electronic Health Records (EHR) as multimodal data. It uniquely represents tabular concepts like diagnoses and medications as structured natural language text using our \"pseudo-notes\" method. This approach allows us to effectively employ Large Language Models (LLMs) for individual EHR representation, proving beneficial in a variety of text-classification tasks. We demonstrate the effectiveness of MEME by applying it to diverse tasks within the Emergency Department across multiple hospital systems. Our findings show that MEME surpasses the performance of both single modality/embedding methods and traditional machine learning approaches, highlighting its effectiveness. Additionally, our tests on the model's generalizability reveal that training solely on the MIMIC-IV database does not guarantee effective application across different hospital institutions.",
"# Huggingface Repository\n\nBelow is the tree structure of the repository, listing all the model files and their respective functions:",
"## Usage\n\nThe models are trained to perform specific tasks related to the emergency department using Multiple Embedding Model for EHR (MEME), Multimodal Single Embedding Model (MSEM), and modality specific single embedding models. They are designed to predict various outcomes and assist in multitask and disposition prediction tasks.\n\nTo use these models, load them into your PyTorch environment using the following example code:",
"## Contributing\n\nIf you wish to contribute to this repository, please fork it, make your changes, and submit a pull request.\n\nFor any questions or issues, please open an issue on this repository or reach out to [email protected]\n\nThank you for your interest in artificial intelligence within Healthcare."
] | [
69,
36,
208,
31,
94,
71
] | [
"passage: TAGS\n#MEME #Multiple Embedding Model For EHR #Multimodal Clinical Pseudo-notes for Emergency Department Prediction Tasks using Multiple Embedding Model for EHR (MEME) #arxiv-2402.00160 #license-mit #region-us \n# Multimodal Clinical Pseudo-notes for Emergency Department Prediction Tasks using Multiple Embedding Model for EHR (MEME)# Abstract\n\nIn this work, we introduce Multiple Embedding Model for EHR (MEME), an approach that views Electronic Health Records (EHR) as multimodal data. It uniquely represents tabular concepts like diagnoses and medications as structured natural language text using our \"pseudo-notes\" method. This approach allows us to effectively employ Large Language Models (LLMs) for individual EHR representation, proving beneficial in a variety of text-classification tasks. We demonstrate the effectiveness of MEME by applying it to diverse tasks within the Emergency Department across multiple hospital systems. Our findings show that MEME surpasses the performance of both single modality/embedding methods and traditional machine learning approaches, highlighting its effectiveness. Additionally, our tests on the model's generalizability reveal that training solely on the MIMIC-IV database does not guarantee effective application across different hospital institutions.# Huggingface Repository\n\nBelow is the tree structure of the repository, listing all the model files and their respective functions:## Usage\n\nThe models are trained to perform specific tasks related to the emergency department using Multiple Embedding Model for EHR (MEME), Multimodal Single Embedding Model (MSEM), and modality specific single embedding models. They are designed to predict various outcomes and assist in multitask and disposition prediction tasks.\n\nTo use these models, load them into your PyTorch environment using the following example code:"
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null | null | transformers |
# Model Card for Model ID
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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| {"library_name": "transformers", "tags": []} | fill-mask | neimp/dummy-model | [
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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:
- 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
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null | null | transformers | # Model Card for CodeFuse-DeepSeek-33B

[[中文]](#chinese) [[English]](#english)
<a id="english"></a>
## Model Description
CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-related tasks on the base model DeepSeek-Coder-33B.
<br>
## News and Updates
🔥🔥🔥 2024-01-12 CodeFuse-DeepSeek-33B has been released, achieving a pass@1 (greedy decoding) score of 78.65% on HumanEval.
🔥🔥🔥 2024-01-12 CodeFuse-Mixtral-8x7B has been released, achieving a pass@1 (greedy decoding) score of 56.1% on HumanEval, which is a 15% increase compared to Mixtral-8x7b's 40%.
🔥🔥 2023-11-10 CodeFuse-CodeGeeX2-6B has been released, achieving a pass@1 (greedy decoding) score of 45.12% on HumanEval, which is a 9.22% increase compared to CodeGeeX2 35.9%.
🔥🔥 2023-10-20 CodeFuse-QWen-14B technical documentation has been released. For those interested, please refer to the CodeFuse article on our WeChat official account via the provided link.(https://mp.weixin.qq.com/s/PCQPkvbvfxSPzsqjOILCDw)
🔥🔥 2023-10-16 CodeFuse-QWen-14B has been released, achieving a pass@1 (greedy decoding) score of 48.78% on HumanEval, which is a 16% increase compared to Qwen-14b's 32.3%.
🔥🔥 2023-09-27 CodeFuse-StarCoder-15B has been released, achieving a pass@1 (greedy decoding) score of 54.9% on HumanEval, which is a 21% increase compared to StarCoder's 33.6%.
🔥🔥 2023-09-26 We are pleased to announce the release of the 4-bit quantized version of CodeFuse-CodeLlama-34B. Despite the quantization process, the model still achieves a remarkable 73.8% accuracy (greedy decoding) on the HumanEval pass@1 metric.
🔥🔥 2023-09-11 CodeFuse-CodeLlama-34B has achieved 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for openspurced LLMs at present.
<br>
## Code Community
**Homepage**: 🏡 https://github.com/codefuse-ai (**Please give us your support with a Star🌟 + Fork🚀 + Watch👀**)
+ If you wish to fine-tune the model yourself, you can visit ✨[MFTCoder](https://github.com/codefuse-ai/MFTCoder)✨✨
+ If you wish to see a demo of the model, you can visit ✨[CodeFuse Demo](https://github.com/codefuse-ai/codefuse)✨✨
<br>
## Performance
### Code
| Model | HumanEval(pass@1) | Date |
|:----------------------------|:-----------------:|:-------:|
| **CodeFuse-DeepSeek-33B** | **78.65%** | 2024.01 |
| **CodeFuse-Mixtral-8x7B** | **56.10%** | 2024.01 |
| **CodeFuse-CodeLlama-34B** | 74.4% | 2023.9 |
|**CodeFuse-CodeLlama-34B-4bits** | 73.8% | 2023.9 |
| **CodeFuse-StarCoder-15B** | 54.9% | 2023.9 |
| **CodeFuse-QWen-14B** | 48.78% | 2023.10 |
| **CodeFuse-CodeGeeX2-6B** | 45.12% | 2023.11 |
| WizardCoder-Python-34B-V1.0 | 73.2% | 2023.8 |
| GPT-4(zero-shot) | 67.0% | 2023.3 |
| PanGu-Coder2 15B | 61.6% | 2023.8 |
| CodeLlama-34b-Python | 53.7% | 2023.8 |
| CodeLlama-34b | 48.8% | 2023.8 |
| GPT-3.5(zero-shot) | 48.1% | 2022.11 |
| OctoCoder | 46.2% | 2023.8 |
| StarCoder-15B | 33.6% | 2023.5 |
| Qwen-14b | 32.3% | 2023.10 |
### NLP

<br>
## Requirements
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
<br>
## Inference String Format
The inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.
Here are examples of prompts used to request the model:
**Multi-Round with System Prompt:**
```python
"""
<s>system
System instruction
<s>human
Human 1st round input
<s>bot
Bot 1st round output<|end▁of▁sentence|>
<s>human
Human 2nd round input
<s>bot
Bot 2nd round output<|end▁of▁sentence|>
...
...
...
<s>human
Human nth round input
<s>bot
"""
```
**Single-Round without System Prompt:**
```python
"""
<s>human
User prompt...
<s>bot
"""
```
In this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with "\<s\>bot" to ask the model generating answers.
For example, the format used to infer HumanEval is like the following:
```
<s>human
# language: Python
from typing import List
def separate_paren_groups(paren_string: str) -> List[str]:
""" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to
separate those group into separate strings and return the list of those.
Separate groups are balanced (each open brace is properly closed) and not nested within each other
Ignore any spaces in the input string.
>>> separate_paren_groups('( ) (( )) (( )( ))')
['()', '(())', '(()())']
"""
<s>bot
```
Specifically, we also add the Programming Language Tag (e.g. "```# language: Python```" for Python) used by CodeGeex models.
## Quickstart
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_dir = "codefuse-ai/CodeFuse-DeepSeek-33B"
def load_model_tokenizer(model_path):
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
tokenizer.eos_token = "<|end▁of▁sentence|>"
tokenizer.pad_token = "<|end▁of▁sentence|>"
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
tokenizer.padding_side = "left"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto',torch_dtype=torch.bfloat16, trust_remote_code=True)
return model, tokenizer
HUMAN_ROLE_START_TAG = "<s>human\n"
BOT_ROLE_START_TAG = "<s>bot\n"
text_list = [f'{HUMAN_ROLE_START_TAG}Write a QuickSort program\n#Python\n{BOT_ROLE_START_TAG}']
model, tokenizer = load_model_tokenizer(model_dir)
inputs = tokenizer(text_list, return_tensors='pt', padding=True, add_special_tokens=False).to('cuda')
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
generation_config = GenerationConfig(
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
temperature=0.1,
max_new_tokens=512,
num_return_sequences=1,
num_beams=1,
top_p=0.95,
do_sample=False
)
outputs = model.generate(
inputs= input_ids,
attention_mask=attention_mask,
**generation_config.to_dict()
)
gen_text = tokenizer.batch_decode(outputs[:, input_ids.shape[1]:], skip_special_tokens=True)
print(gen_text[0])
```
<a id="chinese"></a>
## 模型简介
CodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。
<br>
## 新闻
🔥🔥🔥 2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。
🔥🔥🔥 2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)
🔥🔥🔥 2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:https://mp.weixin.qq.com/s/PCQPkvbvfxSPzsqjOILCDw
🔥🔥🔥 2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)
🔥🔥🔥 2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)
🔥🔥🔥 2023-09-26 [CodeFuse-CodeLlama-34B 4bits](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B-4bits/summary)量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。
🔥🔥🔥 2023-09-11 [CodeFuse-CodeLlama-34B](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B/summary)发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。
<br>
## 代码社区
**大本营**: 🏡 https://github.com/codefuse-ai (**请支持我们的项目Star🌟 + Fork🚀 + Watch👀**)
+ 如果您想自己微调该模型,可以访问 ✨[MFTCoder](https://github.com/codefuse-ai/MFTCoder)✨✨
+ 如果您想观看该模型示例,可以访问 ✨[CodeFuse Demo](https://github.com/codefuse-ai/codefuse)✨✨
<br>
## 评测表现
### 代码
| 模型 | HumanEval(pass@1) | 日期 |
|:----------------------------|:-----------------:|:-------:|
| **CodeFuse-CodeLlama-34B** | 74.4% | 2023.9 |
|**CodeFuse-CodeLlama-34B-4bits** | 73.8% | 2023.9 |
| WizardCoder-Python-34B-V1.0 | 73.2% | 2023.8 |
| GPT-4(zero-shot) | 67.0% | 2023.3 |
| PanGu-Coder2 15B | 61.6% | 2023.8 |
| CodeLlama-34b-Python | 53.7% | 2023.8 |
| CodeLlama-34b | 48.8% | 2023.8 |
| GPT-3.5(zero-shot) | 48.1% | 2022.11 |
| OctoCoder | 46.2% | 2023.8 |
| StarCoder-15B | 33.6% | 2023.5 |
| Qwen-14b | 32.3% | 2023.10 |
| **CodeFuse-StarCoder-15B** | 54.9% | 2023.9 |
| **CodeFuse-QWen-14B** | 48.78% | 2023.8 |
| **CodeFuse-CodeGeeX2-6B** | 45.12% | 2023.11 |
| **CodeFuse-DeepSeek-33B**. | **78.65%** | 2024.01 |
### NLP

## Requirements
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
<br>
## 推理数据格式
推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:
**带System提示的多轮会话格式:**
```python
"""
<s>system
System instruction
<s>human
Human 1st round input
<s>bot
Bot 1st round output<|end▁of▁sentence|>
<s>human
Human 2nd round input
<s>bot
Bot 2nd round output<|end▁of▁sentence|>
...
...
...
<s>human
Human nth round input
<s>bot
"""
```
**不带System提示的单轮会话格式:**
```python
"""
<s>human
User prompt...
<s>bot
"""
```
在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以"\<s\>bot\n"结尾,引导模型生成回答。
例如,推理HumanEval数据时使用的格式如下所示:
```python
<s>human
# language: Python
from typing import List
def separate_paren_groups(paren_string: str) -> List[str]:
""" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to
separate those group into separate strings and return the list of those.
Separate groups are balanced (each open brace is properly closed) and not nested within each other
Ignore any spaces in the input string.
>>> separate_paren_groups('( ) (( )) (( )( ))')
['()', '(())', '(()())']
"""
<s>bot
```
特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用"```# language: Python```")。
## 快速使用
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_dir = "codefuse-ai/CodeFuse-DeepSeek-33B"
def load_model_tokenizer(model_path):
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
tokenizer.eos_token = "<|end▁of▁sentence|>"
tokenizer.pad_token = "<|end▁of▁sentence|>"
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
tokenizer.padding_side = "left"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto',torch_dtype=torch.bfloat16, trust_remote_code=True)
return model, tokenizer
HUMAN_ROLE_START_TAG = "<s>human\n"
BOT_ROLE_START_TAG = "<s>bot\n"
text_list = [f'{HUMAN_ROLE_START_TAG}请写一个快排程序\n#Python\n{BOT_ROLE_START_TAG}']
model, tokenizer = load_model_tokenizer(model_dir)
inputs = tokenizer(text_list, return_tensors='pt', padding=True, add_special_tokens=False).to('cuda')
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
generation_config = GenerationConfig(
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
temperature=0.2,
max_new_tokens=512,
num_return_sequences=1,
num_beams=1,
top_p=0.95,
do_sample=False
)
outputs = model.generate(
inputs= input_ids,
attention_mask=attention_mask,
**generation_config.to_dict()
)
gen_text = tokenizer.batch_decode(outputs[:, input_ids.shape[1]:], skip_special_tokens=True)
print(gen_text[0])
```
| {"license": "other", "tasks": ["code-generation"]} | text-generation | LoneStriker/CodeFuse-DeepSeek-33B-3.0bpw-h6-exl2 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T18:55:29+00:00 | [] | [] | TAGS
#transformers #pytorch #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card for CodeFuse-DeepSeek-33B
====================================
!logo
[[中文]](#chinese) [[English]](#english)
Model Description
-----------------
CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-related tasks on the base model DeepSeek-Coder-33B.
News and Updates
----------------
2024-01-12 CodeFuse-DeepSeek-33B has been released, achieving a pass@1 (greedy decoding) score of 78.65% on HumanEval.
2024-01-12 CodeFuse-Mixtral-8x7B has been released, achieving a pass@1 (greedy decoding) score of 56.1% on HumanEval, which is a 15% increase compared to Mixtral-8x7b's 40%.
2023-11-10 CodeFuse-CodeGeeX2-6B has been released, achieving a pass@1 (greedy decoding) score of 45.12% on HumanEval, which is a 9.22% increase compared to CodeGeeX2 35.9%.
2023-10-20 CodeFuse-QWen-14B technical documentation has been released. For those interested, please refer to the CodeFuse article on our WeChat official account via the provided link.(URL
2023-10-16 CodeFuse-QWen-14B has been released, achieving a pass@1 (greedy decoding) score of 48.78% on HumanEval, which is a 16% increase compared to Qwen-14b's 32.3%.
2023-09-27 CodeFuse-StarCoder-15B has been released, achieving a pass@1 (greedy decoding) score of 54.9% on HumanEval, which is a 21% increase compared to StarCoder's 33.6%.
2023-09-26 We are pleased to announce the release of the 4-bit quantized version of CodeFuse-CodeLlama-34B. Despite the quantization process, the model still achieves a remarkable 73.8% accuracy (greedy decoding) on the HumanEval pass@1 metric.
2023-09-11 CodeFuse-CodeLlama-34B has achieved 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for openspurced LLMs at present.
Code Community
--------------
Homepage: URL (Please give us your support with a Star + Fork + Watch)
* If you wish to fine-tune the model yourself, you can visit MFTCoder
* If you wish to see a demo of the model, you can visit CodeFuse Demo
Performance
-----------
### Code
### NLP
!NLP Performance Radar
Requirements
------------
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
Inference String Format
-----------------------
The inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.
Here are examples of prompts used to request the model:
Multi-Round with System Prompt:
Single-Round without System Prompt:
In this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with "<s>bot" to ask the model generating answers.
For example, the format used to infer HumanEval is like the following:
Specifically, we also add the Programming Language Tag (e.g. "" for Python) used by CodeGeex models.
Quickstart
----------
模型简介
----
CodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。
新闻
--
2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。
2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)
2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:URL
2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)
2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)
2023-09-26 CodeFuse-CodeLlama-34B 4bits量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。
2023-09-11 CodeFuse-CodeLlama-34B发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。
代码社区
----
大本营: URL (请支持我们的项目Star + Fork + Watch)
* 如果您想自己微调该模型,可以访问 MFTCoder
* 如果您想观看该模型示例,可以访问 CodeFuse Demo
评测表现
----
### 代码
### NLP
!NLP Performance Radar
Requirements
------------
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
推理数据格式
------
推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:
带System提示的多轮会话格式:
不带System提示的单轮会话格式:
在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以"<s>bot\n"结尾,引导模型生成回答。
例如,推理HumanEval数据时使用的格式如下所示:
特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用"")。
快速使用
----
| [
"### Code",
"### NLP\n\n\n!NLP Performance Radar\n\n\n \n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\nInference String Format\n-----------------------\n\n\nThe inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.\nHere are examples of prompts used to request the model:\n\n\nMulti-Round with System Prompt:\n\n\nSingle-Round without System Prompt:\n\n\nIn this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with \"<s>bot\" to ask the model generating answers.\n\n\nFor example, the format used to infer HumanEval is like the following:\n\n\nSpecifically, we also add the Programming Language Tag (e.g. \"\" for Python) used by CodeGeex models.\n\n\nQuickstart\n----------\n\n\n\n模型简介\n----\n\n\nCodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。\n \n\n\n\n新闻\n--\n\n\n2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。\n\n\n2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)\n\n\n2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:URL\n\n\n2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)\n\n\n2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)\n\n\n2023-09-26 CodeFuse-CodeLlama-34B 4bits量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。\n\n\n2023-09-11 CodeFuse-CodeLlama-34B发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。\n\n\n \n\n代码社区\n----\n\n\n大本营: URL (请支持我们的项目Star + Fork + Watch)\n\n\n* 如果您想自己微调该模型,可以访问 MFTCoder\n* 如果您想观看该模型示例,可以访问 CodeFuse Demo\n\n\n \n\n评测表现\n----",
"### 代码",
"### NLP\n\n\n!NLP Performance Radar\n\n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\n推理数据格式\n------\n\n\n推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:\n\n\n带System提示的多轮会话格式:\n\n\n不带System提示的单轮会话格式:\n\n\n在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以\"<s>bot\\n\"结尾,引导模型生成回答。\n\n\n例如,推理HumanEval数据时使用的格式如下所示:\n\n\n特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用\"\")。\n\n\n快速使用\n----"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Code",
"### NLP\n\n\n!NLP Performance Radar\n\n\n \n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\nInference String Format\n-----------------------\n\n\nThe inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.\nHere are examples of prompts used to request the model:\n\n\nMulti-Round with System Prompt:\n\n\nSingle-Round without System Prompt:\n\n\nIn this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with \"<s>bot\" to ask the model generating answers.\n\n\nFor example, the format used to infer HumanEval is like the following:\n\n\nSpecifically, we also add the Programming Language Tag (e.g. \"\" for Python) used by CodeGeex models.\n\n\nQuickstart\n----------\n\n\n\n模型简介\n----\n\n\nCodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。\n \n\n\n\n新闻\n--\n\n\n2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。\n\n\n2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)\n\n\n2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:URL\n\n\n2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)\n\n\n2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)\n\n\n2023-09-26 CodeFuse-CodeLlama-34B 4bits量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。\n\n\n2023-09-11 CodeFuse-CodeLlama-34B发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。\n\n\n \n\n代码社区\n----\n\n\n大本营: URL (请支持我们的项目Star + Fork + Watch)\n\n\n* 如果您想自己微调该模型,可以访问 MFTCoder\n* 如果您想观看该模型示例,可以访问 CodeFuse Demo\n\n\n \n\n评测表现\n----",
"### 代码",
"### NLP\n\n\n!NLP Performance Radar\n\n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\n推理数据格式\n------\n\n\n推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:\n\n\n带System提示的多轮会话格式:\n\n\n不带System提示的单轮会话格式:\n\n\n在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以\"<s>bot\\n\"结尾,引导模型生成回答。\n\n\n例如,推理HumanEval数据时使用的格式如下所示:\n\n\n特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用\"\")。\n\n\n快速使用\n----"
] | [
55,
3,
656,
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"passage: TAGS\n#transformers #pytorch #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Code"
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null | null | transformers | # Description
[MaziyarPanahi/Mistral-7B-Instruct-v0.2-AWQ](https://huggingface.co/MaziyarPanahi/Mistral-7B-Instruct-v0.2-AWQ) is a quantized (AWQ) version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
## How to use
### Install the necessary packages
```
pip install --upgrade accelerate autoawq transformers
```
### Example Python code
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "MaziyarPanahi/Mistral-7B-Instruct-v0.2-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id).to(0)
text = "User:\nHello can you provide me with top-3 cool places to visit in Paris?\n\nAssistant:\n"
inputs = tokenizer(text, return_tensors="pt").to(0)
out = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(out[0], skip_special_tokens=True))
```
Results:
```
User:
Hello can you provide me with top-3 cool places to visit in Paris?
Assistant:
Absolutely, here are my top-3 recommendations for must-see places in Paris:
1. The Eiffel Tower: An icon of Paris, this wrought-iron lattice tower is a global cultural icon of France and is among the most recognizable structures in the world. Climbing up to the top offers breathtaking views of the city.
2. The Louvre Museum: Home to thousands of works of art, the Louvre is the world's largest art museum and a historic monument in Paris. Must-see pieces include the Mona Lisa, the Winged Victory of Samothrace, and the Venus de Milo.
3. Notre-Dame Cathedral: This cathedral is a masterpiece of French Gothic architecture and is famous for its intricate stone carvings, beautiful stained glass, and its iconic twin towers. Be sure to spend some time exploring its history and learning about the fascinating restoration efforts post the 2019 fire.
I hope you find these recommendations helpful and that they make for an enjoyable and memorable trip to Paris. Safe travels!
``` | {"tags": ["finetuned", "quantized", "4-bit", "AWQ", "transformers", "pytorch", "safetensors", "mistral", "text-generation", "finetuned", "conversational", "arxiv:2310.06825", "license:apache-2.0", "autotrain_compatible", "has_space", "text-generation-inference", "region:us"], "model_name": "Mistral-7B-Instruct-v0.2-AWQ", "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "inference": false, "model_creator": "mistralai", "pipeline_tag": "text-generation", "quantized_by": "MaziyarPanahi"} | text-generation | MaziyarPanahi/Mistral-7B-Instruct-v0.2-AWQ | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"quantized",
"4-bit",
"AWQ",
"pytorch",
"conversational",
"arxiv:2310.06825",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us",
"base_model:mistralai/Mistral-7B-Instruct-v0.2"
] | 2024-02-09T18:55:57+00:00 | [
"2310.06825"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #finetuned #quantized #4-bit #AWQ #pytorch #conversational #arxiv-2310.06825 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us #base_model-mistralai/Mistral-7B-Instruct-v0.2
| # Description
MaziyarPanahi/Mistral-7B-Instruct-v0.2-AWQ is a quantized (AWQ) version of mistralai/Mistral-7B-Instruct-v0.2
## How to use
### Install the necessary packages
### Example Python code
Results:
| [
"# Description\nMaziyarPanahi/Mistral-7B-Instruct-v0.2-AWQ is a quantized (AWQ) version of mistralai/Mistral-7B-Instruct-v0.2",
"## How to use",
"### Install the necessary packages",
"### Example Python code\n\n\n\n\nResults:"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #finetuned #quantized #4-bit #AWQ #pytorch #conversational #arxiv-2310.06825 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us #base_model-mistralai/Mistral-7B-Instruct-v0.2 \n",
"# Description\nMaziyarPanahi/Mistral-7B-Instruct-v0.2-AWQ is a quantized (AWQ) version of mistralai/Mistral-7B-Instruct-v0.2",
"## How to use",
"### Install the necessary packages",
"### Example Python code\n\n\n\n\nResults:"
] | [
96,
44,
4,
7,
8
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #finetuned #quantized #4-bit #AWQ #pytorch #conversational #arxiv-2310.06825 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us #base_model-mistralai/Mistral-7B-Instruct-v0.2 \n# Description\nMaziyarPanahi/Mistral-7B-Instruct-v0.2-AWQ is a quantized (AWQ) version of mistralai/Mistral-7B-Instruct-v0.2## How to use### Install the necessary packages### Example Python code\n\n\n\n\nResults:"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | rpunuru/test123 | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
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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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### 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]
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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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0.04875178262591362,
0.01804669201374054,
0.12263146042823792,
0.02951994352042675,
-0.1865430772304535,
0.032826557755470276,
-0.01144319772720337,
0.10186848044395447,
-0.25588861107826233,
-0.08421015739440918,
0.08833149075508118,
-0.011924264021217823,
-0.05105875805020332,
0.10560628771781921,
0.057650718837976456,
0.04243382066488266,
-0.043439045548439026,
-0.10480839014053345,
-0.02186836116015911,
0.14663739502429962,
-0.1469624787569046,
-0.025013303384184837
] |
null | null | transformers | # Model Card for CodeFuse-DeepSeek-33B

[[中文]](#chinese) [[English]](#english)
<a id="english"></a>
## Model Description
CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-related tasks on the base model DeepSeek-Coder-33B.
<br>
## News and Updates
🔥🔥🔥 2024-01-12 CodeFuse-DeepSeek-33B has been released, achieving a pass@1 (greedy decoding) score of 78.65% on HumanEval.
🔥🔥🔥 2024-01-12 CodeFuse-Mixtral-8x7B has been released, achieving a pass@1 (greedy decoding) score of 56.1% on HumanEval, which is a 15% increase compared to Mixtral-8x7b's 40%.
🔥🔥 2023-11-10 CodeFuse-CodeGeeX2-6B has been released, achieving a pass@1 (greedy decoding) score of 45.12% on HumanEval, which is a 9.22% increase compared to CodeGeeX2 35.9%.
🔥🔥 2023-10-20 CodeFuse-QWen-14B technical documentation has been released. For those interested, please refer to the CodeFuse article on our WeChat official account via the provided link.(https://mp.weixin.qq.com/s/PCQPkvbvfxSPzsqjOILCDw)
🔥🔥 2023-10-16 CodeFuse-QWen-14B has been released, achieving a pass@1 (greedy decoding) score of 48.78% on HumanEval, which is a 16% increase compared to Qwen-14b's 32.3%.
🔥🔥 2023-09-27 CodeFuse-StarCoder-15B has been released, achieving a pass@1 (greedy decoding) score of 54.9% on HumanEval, which is a 21% increase compared to StarCoder's 33.6%.
🔥🔥 2023-09-26 We are pleased to announce the release of the 4-bit quantized version of CodeFuse-CodeLlama-34B. Despite the quantization process, the model still achieves a remarkable 73.8% accuracy (greedy decoding) on the HumanEval pass@1 metric.
🔥🔥 2023-09-11 CodeFuse-CodeLlama-34B has achieved 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for openspurced LLMs at present.
<br>
## Code Community
**Homepage**: 🏡 https://github.com/codefuse-ai (**Please give us your support with a Star🌟 + Fork🚀 + Watch👀**)
+ If you wish to fine-tune the model yourself, you can visit ✨[MFTCoder](https://github.com/codefuse-ai/MFTCoder)✨✨
+ If you wish to see a demo of the model, you can visit ✨[CodeFuse Demo](https://github.com/codefuse-ai/codefuse)✨✨
<br>
## Performance
### Code
| Model | HumanEval(pass@1) | Date |
|:----------------------------|:-----------------:|:-------:|
| **CodeFuse-DeepSeek-33B** | **78.65%** | 2024.01 |
| **CodeFuse-Mixtral-8x7B** | **56.10%** | 2024.01 |
| **CodeFuse-CodeLlama-34B** | 74.4% | 2023.9 |
|**CodeFuse-CodeLlama-34B-4bits** | 73.8% | 2023.9 |
| **CodeFuse-StarCoder-15B** | 54.9% | 2023.9 |
| **CodeFuse-QWen-14B** | 48.78% | 2023.10 |
| **CodeFuse-CodeGeeX2-6B** | 45.12% | 2023.11 |
| WizardCoder-Python-34B-V1.0 | 73.2% | 2023.8 |
| GPT-4(zero-shot) | 67.0% | 2023.3 |
| PanGu-Coder2 15B | 61.6% | 2023.8 |
| CodeLlama-34b-Python | 53.7% | 2023.8 |
| CodeLlama-34b | 48.8% | 2023.8 |
| GPT-3.5(zero-shot) | 48.1% | 2022.11 |
| OctoCoder | 46.2% | 2023.8 |
| StarCoder-15B | 33.6% | 2023.5 |
| Qwen-14b | 32.3% | 2023.10 |
### NLP

<br>
## Requirements
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
<br>
## Inference String Format
The inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.
Here are examples of prompts used to request the model:
**Multi-Round with System Prompt:**
```python
"""
<s>system
System instruction
<s>human
Human 1st round input
<s>bot
Bot 1st round output<|end▁of▁sentence|>
<s>human
Human 2nd round input
<s>bot
Bot 2nd round output<|end▁of▁sentence|>
...
...
...
<s>human
Human nth round input
<s>bot
"""
```
**Single-Round without System Prompt:**
```python
"""
<s>human
User prompt...
<s>bot
"""
```
In this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with "\<s\>bot" to ask the model generating answers.
For example, the format used to infer HumanEval is like the following:
```
<s>human
# language: Python
from typing import List
def separate_paren_groups(paren_string: str) -> List[str]:
""" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to
separate those group into separate strings and return the list of those.
Separate groups are balanced (each open brace is properly closed) and not nested within each other
Ignore any spaces in the input string.
>>> separate_paren_groups('( ) (( )) (( )( ))')
['()', '(())', '(()())']
"""
<s>bot
```
Specifically, we also add the Programming Language Tag (e.g. "```# language: Python```" for Python) used by CodeGeex models.
## Quickstart
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_dir = "codefuse-ai/CodeFuse-DeepSeek-33B"
def load_model_tokenizer(model_path):
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
tokenizer.eos_token = "<|end▁of▁sentence|>"
tokenizer.pad_token = "<|end▁of▁sentence|>"
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
tokenizer.padding_side = "left"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto',torch_dtype=torch.bfloat16, trust_remote_code=True)
return model, tokenizer
HUMAN_ROLE_START_TAG = "<s>human\n"
BOT_ROLE_START_TAG = "<s>bot\n"
text_list = [f'{HUMAN_ROLE_START_TAG}Write a QuickSort program\n#Python\n{BOT_ROLE_START_TAG}']
model, tokenizer = load_model_tokenizer(model_dir)
inputs = tokenizer(text_list, return_tensors='pt', padding=True, add_special_tokens=False).to('cuda')
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
generation_config = GenerationConfig(
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
temperature=0.1,
max_new_tokens=512,
num_return_sequences=1,
num_beams=1,
top_p=0.95,
do_sample=False
)
outputs = model.generate(
inputs= input_ids,
attention_mask=attention_mask,
**generation_config.to_dict()
)
gen_text = tokenizer.batch_decode(outputs[:, input_ids.shape[1]:], skip_special_tokens=True)
print(gen_text[0])
```
<a id="chinese"></a>
## 模型简介
CodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。
<br>
## 新闻
🔥🔥🔥 2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。
🔥🔥🔥 2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)
🔥🔥🔥 2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:https://mp.weixin.qq.com/s/PCQPkvbvfxSPzsqjOILCDw
🔥🔥🔥 2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)
🔥🔥🔥 2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)
🔥🔥🔥 2023-09-26 [CodeFuse-CodeLlama-34B 4bits](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B-4bits/summary)量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。
🔥🔥🔥 2023-09-11 [CodeFuse-CodeLlama-34B](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B/summary)发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。
<br>
## 代码社区
**大本营**: 🏡 https://github.com/codefuse-ai (**请支持我们的项目Star🌟 + Fork🚀 + Watch👀**)
+ 如果您想自己微调该模型,可以访问 ✨[MFTCoder](https://github.com/codefuse-ai/MFTCoder)✨✨
+ 如果您想观看该模型示例,可以访问 ✨[CodeFuse Demo](https://github.com/codefuse-ai/codefuse)✨✨
<br>
## 评测表现
### 代码
| 模型 | HumanEval(pass@1) | 日期 |
|:----------------------------|:-----------------:|:-------:|
| **CodeFuse-CodeLlama-34B** | 74.4% | 2023.9 |
|**CodeFuse-CodeLlama-34B-4bits** | 73.8% | 2023.9 |
| WizardCoder-Python-34B-V1.0 | 73.2% | 2023.8 |
| GPT-4(zero-shot) | 67.0% | 2023.3 |
| PanGu-Coder2 15B | 61.6% | 2023.8 |
| CodeLlama-34b-Python | 53.7% | 2023.8 |
| CodeLlama-34b | 48.8% | 2023.8 |
| GPT-3.5(zero-shot) | 48.1% | 2022.11 |
| OctoCoder | 46.2% | 2023.8 |
| StarCoder-15B | 33.6% | 2023.5 |
| Qwen-14b | 32.3% | 2023.10 |
| **CodeFuse-StarCoder-15B** | 54.9% | 2023.9 |
| **CodeFuse-QWen-14B** | 48.78% | 2023.8 |
| **CodeFuse-CodeGeeX2-6B** | 45.12% | 2023.11 |
| **CodeFuse-DeepSeek-33B**. | **78.65%** | 2024.01 |
### NLP

## Requirements
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
<br>
## 推理数据格式
推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:
**带System提示的多轮会话格式:**
```python
"""
<s>system
System instruction
<s>human
Human 1st round input
<s>bot
Bot 1st round output<|end▁of▁sentence|>
<s>human
Human 2nd round input
<s>bot
Bot 2nd round output<|end▁of▁sentence|>
...
...
...
<s>human
Human nth round input
<s>bot
"""
```
**不带System提示的单轮会话格式:**
```python
"""
<s>human
User prompt...
<s>bot
"""
```
在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以"\<s\>bot\n"结尾,引导模型生成回答。
例如,推理HumanEval数据时使用的格式如下所示:
```python
<s>human
# language: Python
from typing import List
def separate_paren_groups(paren_string: str) -> List[str]:
""" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to
separate those group into separate strings and return the list of those.
Separate groups are balanced (each open brace is properly closed) and not nested within each other
Ignore any spaces in the input string.
>>> separate_paren_groups('( ) (( )) (( )( ))')
['()', '(())', '(()())']
"""
<s>bot
```
特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用"```# language: Python```")。
## 快速使用
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_dir = "codefuse-ai/CodeFuse-DeepSeek-33B"
def load_model_tokenizer(model_path):
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
tokenizer.eos_token = "<|end▁of▁sentence|>"
tokenizer.pad_token = "<|end▁of▁sentence|>"
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
tokenizer.padding_side = "left"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto',torch_dtype=torch.bfloat16, trust_remote_code=True)
return model, tokenizer
HUMAN_ROLE_START_TAG = "<s>human\n"
BOT_ROLE_START_TAG = "<s>bot\n"
text_list = [f'{HUMAN_ROLE_START_TAG}请写一个快排程序\n#Python\n{BOT_ROLE_START_TAG}']
model, tokenizer = load_model_tokenizer(model_dir)
inputs = tokenizer(text_list, return_tensors='pt', padding=True, add_special_tokens=False).to('cuda')
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
generation_config = GenerationConfig(
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
temperature=0.2,
max_new_tokens=512,
num_return_sequences=1,
num_beams=1,
top_p=0.95,
do_sample=False
)
outputs = model.generate(
inputs= input_ids,
attention_mask=attention_mask,
**generation_config.to_dict()
)
gen_text = tokenizer.batch_decode(outputs[:, input_ids.shape[1]:], skip_special_tokens=True)
print(gen_text[0])
```
| {"license": "other", "tasks": ["code-generation"]} | text-generation | LoneStriker/CodeFuse-DeepSeek-33B-4.0bpw-h6-exl2 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T19:01:20+00:00 | [] | [] | TAGS
#transformers #pytorch #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card for CodeFuse-DeepSeek-33B
====================================
!logo
[[中文]](#chinese) [[English]](#english)
Model Description
-----------------
CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-related tasks on the base model DeepSeek-Coder-33B.
News and Updates
----------------
2024-01-12 CodeFuse-DeepSeek-33B has been released, achieving a pass@1 (greedy decoding) score of 78.65% on HumanEval.
2024-01-12 CodeFuse-Mixtral-8x7B has been released, achieving a pass@1 (greedy decoding) score of 56.1% on HumanEval, which is a 15% increase compared to Mixtral-8x7b's 40%.
2023-11-10 CodeFuse-CodeGeeX2-6B has been released, achieving a pass@1 (greedy decoding) score of 45.12% on HumanEval, which is a 9.22% increase compared to CodeGeeX2 35.9%.
2023-10-20 CodeFuse-QWen-14B technical documentation has been released. For those interested, please refer to the CodeFuse article on our WeChat official account via the provided link.(URL
2023-10-16 CodeFuse-QWen-14B has been released, achieving a pass@1 (greedy decoding) score of 48.78% on HumanEval, which is a 16% increase compared to Qwen-14b's 32.3%.
2023-09-27 CodeFuse-StarCoder-15B has been released, achieving a pass@1 (greedy decoding) score of 54.9% on HumanEval, which is a 21% increase compared to StarCoder's 33.6%.
2023-09-26 We are pleased to announce the release of the 4-bit quantized version of CodeFuse-CodeLlama-34B. Despite the quantization process, the model still achieves a remarkable 73.8% accuracy (greedy decoding) on the HumanEval pass@1 metric.
2023-09-11 CodeFuse-CodeLlama-34B has achieved 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for openspurced LLMs at present.
Code Community
--------------
Homepage: URL (Please give us your support with a Star + Fork + Watch)
* If you wish to fine-tune the model yourself, you can visit MFTCoder
* If you wish to see a demo of the model, you can visit CodeFuse Demo
Performance
-----------
### Code
### NLP
!NLP Performance Radar
Requirements
------------
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
Inference String Format
-----------------------
The inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.
Here are examples of prompts used to request the model:
Multi-Round with System Prompt:
Single-Round without System Prompt:
In this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with "<s>bot" to ask the model generating answers.
For example, the format used to infer HumanEval is like the following:
Specifically, we also add the Programming Language Tag (e.g. "" for Python) used by CodeGeex models.
Quickstart
----------
模型简介
----
CodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。
新闻
--
2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。
2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)
2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:URL
2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)
2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)
2023-09-26 CodeFuse-CodeLlama-34B 4bits量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。
2023-09-11 CodeFuse-CodeLlama-34B发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。
代码社区
----
大本营: URL (请支持我们的项目Star + Fork + Watch)
* 如果您想自己微调该模型,可以访问 MFTCoder
* 如果您想观看该模型示例,可以访问 CodeFuse Demo
评测表现
----
### 代码
### NLP
!NLP Performance Radar
Requirements
------------
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
推理数据格式
------
推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:
带System提示的多轮会话格式:
不带System提示的单轮会话格式:
在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以"<s>bot\n"结尾,引导模型生成回答。
例如,推理HumanEval数据时使用的格式如下所示:
特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用"")。
快速使用
----
| [
"### Code",
"### NLP\n\n\n!NLP Performance Radar\n\n\n \n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\nInference String Format\n-----------------------\n\n\nThe inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.\nHere are examples of prompts used to request the model:\n\n\nMulti-Round with System Prompt:\n\n\nSingle-Round without System Prompt:\n\n\nIn this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with \"<s>bot\" to ask the model generating answers.\n\n\nFor example, the format used to infer HumanEval is like the following:\n\n\nSpecifically, we also add the Programming Language Tag (e.g. \"\" for Python) used by CodeGeex models.\n\n\nQuickstart\n----------\n\n\n\n模型简介\n----\n\n\nCodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。\n \n\n\n\n新闻\n--\n\n\n2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。\n\n\n2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)\n\n\n2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:URL\n\n\n2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)\n\n\n2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)\n\n\n2023-09-26 CodeFuse-CodeLlama-34B 4bits量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。\n\n\n2023-09-11 CodeFuse-CodeLlama-34B发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。\n\n\n \n\n代码社区\n----\n\n\n大本营: URL (请支持我们的项目Star + Fork + Watch)\n\n\n* 如果您想自己微调该模型,可以访问 MFTCoder\n* 如果您想观看该模型示例,可以访问 CodeFuse Demo\n\n\n \n\n评测表现\n----",
"### 代码",
"### NLP\n\n\n!NLP Performance Radar\n\n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\n推理数据格式\n------\n\n\n推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:\n\n\n带System提示的多轮会话格式:\n\n\n不带System提示的单轮会话格式:\n\n\n在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以\"<s>bot\\n\"结尾,引导模型生成回答。\n\n\n例如,推理HumanEval数据时使用的格式如下所示:\n\n\n特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用\"\")。\n\n\n快速使用\n----"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Code",
"### NLP\n\n\n!NLP Performance Radar\n\n\n \n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\nInference String Format\n-----------------------\n\n\nThe inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.\nHere are examples of prompts used to request the model:\n\n\nMulti-Round with System Prompt:\n\n\nSingle-Round without System Prompt:\n\n\nIn this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with \"<s>bot\" to ask the model generating answers.\n\n\nFor example, the format used to infer HumanEval is like the following:\n\n\nSpecifically, we also add the Programming Language Tag (e.g. \"\" for Python) used by CodeGeex models.\n\n\nQuickstart\n----------\n\n\n\n模型简介\n----\n\n\nCodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。\n \n\n\n\n新闻\n--\n\n\n2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。\n\n\n2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)\n\n\n2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:URL\n\n\n2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)\n\n\n2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)\n\n\n2023-09-26 CodeFuse-CodeLlama-34B 4bits量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。\n\n\n2023-09-11 CodeFuse-CodeLlama-34B发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。\n\n\n \n\n代码社区\n----\n\n\n大本营: URL (请支持我们的项目Star + Fork + Watch)\n\n\n* 如果您想自己微调该模型,可以访问 MFTCoder\n* 如果您想观看该模型示例,可以访问 CodeFuse Demo\n\n\n \n\n评测表现\n----",
"### 代码",
"### NLP\n\n\n!NLP Performance Radar\n\n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\n推理数据格式\n------\n\n\n推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:\n\n\n带System提示的多轮会话格式:\n\n\n不带System提示的单轮会话格式:\n\n\n在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以\"<s>bot\\n\"结尾,引导模型生成回答。\n\n\n例如,推理HumanEval数据时使用的格式如下所示:\n\n\n特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用\"\")。\n\n\n快速使用\n----"
] | [
55,
3,
656,
4,
244
] | [
"passage: TAGS\n#transformers #pytorch #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Code"
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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 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('{MODEL_NAME}')
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
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# 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('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# 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, cls pooling.
sentence_embeddings = cls_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={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 410959 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.DenoisingAutoEncoderLoss.DenoisingAutoEncoderLoss`
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": 3e-05
},
"scheduler": "constantlr",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | alexjones1925/ibotta-global-products-dae-finetuned-v1 | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | 2024-02-09T19:06:43+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
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:
## 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 410959 with parameters:
Loss:
'sentence_transformers.losses.DenoisingAutoEncoderLoss.DenoisingAutoEncoderLoss'
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 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:",
"## 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 410959 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.DenoisingAutoEncoderLoss.DenoisingAutoEncoderLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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:",
"## 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 410959 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.DenoisingAutoEncoderLoss.DenoisingAutoEncoderLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
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] | [
"passage: TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n# {MODEL_NAME}\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:## 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 410959 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.DenoisingAutoEncoderLoss.DenoisingAutoEncoderLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors"
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null | null | transformers |
# Model Card for Model ID
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"transformers",
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#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
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# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Model Sources [optional]
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- Demo [optional]:
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### 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.
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Use the code below to get started with the model.
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### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
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#### Factors
#### Metrics
### Results
#### Summary
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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="cnyc/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": "Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.52 +/- 2.73", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | cnyc/Taxi-v3 | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-09T19:08:52+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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-0.044773899018764496,
-0.09409699589014053
] |
null | null | transformers | # Model Card for CodeFuse-DeepSeek-33B

[[中文]](#chinese) [[English]](#english)
<a id="english"></a>
## Model Description
CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-related tasks on the base model DeepSeek-Coder-33B.
<br>
## News and Updates
🔥🔥🔥 2024-01-12 CodeFuse-DeepSeek-33B has been released, achieving a pass@1 (greedy decoding) score of 78.65% on HumanEval.
🔥🔥🔥 2024-01-12 CodeFuse-Mixtral-8x7B has been released, achieving a pass@1 (greedy decoding) score of 56.1% on HumanEval, which is a 15% increase compared to Mixtral-8x7b's 40%.
🔥🔥 2023-11-10 CodeFuse-CodeGeeX2-6B has been released, achieving a pass@1 (greedy decoding) score of 45.12% on HumanEval, which is a 9.22% increase compared to CodeGeeX2 35.9%.
🔥🔥 2023-10-20 CodeFuse-QWen-14B technical documentation has been released. For those interested, please refer to the CodeFuse article on our WeChat official account via the provided link.(https://mp.weixin.qq.com/s/PCQPkvbvfxSPzsqjOILCDw)
🔥🔥 2023-10-16 CodeFuse-QWen-14B has been released, achieving a pass@1 (greedy decoding) score of 48.78% on HumanEval, which is a 16% increase compared to Qwen-14b's 32.3%.
🔥🔥 2023-09-27 CodeFuse-StarCoder-15B has been released, achieving a pass@1 (greedy decoding) score of 54.9% on HumanEval, which is a 21% increase compared to StarCoder's 33.6%.
🔥🔥 2023-09-26 We are pleased to announce the release of the 4-bit quantized version of CodeFuse-CodeLlama-34B. Despite the quantization process, the model still achieves a remarkable 73.8% accuracy (greedy decoding) on the HumanEval pass@1 metric.
🔥🔥 2023-09-11 CodeFuse-CodeLlama-34B has achieved 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for openspurced LLMs at present.
<br>
## Code Community
**Homepage**: 🏡 https://github.com/codefuse-ai (**Please give us your support with a Star🌟 + Fork🚀 + Watch👀**)
+ If you wish to fine-tune the model yourself, you can visit ✨[MFTCoder](https://github.com/codefuse-ai/MFTCoder)✨✨
+ If you wish to see a demo of the model, you can visit ✨[CodeFuse Demo](https://github.com/codefuse-ai/codefuse)✨✨
<br>
## Performance
### Code
| Model | HumanEval(pass@1) | Date |
|:----------------------------|:-----------------:|:-------:|
| **CodeFuse-DeepSeek-33B** | **78.65%** | 2024.01 |
| **CodeFuse-Mixtral-8x7B** | **56.10%** | 2024.01 |
| **CodeFuse-CodeLlama-34B** | 74.4% | 2023.9 |
|**CodeFuse-CodeLlama-34B-4bits** | 73.8% | 2023.9 |
| **CodeFuse-StarCoder-15B** | 54.9% | 2023.9 |
| **CodeFuse-QWen-14B** | 48.78% | 2023.10 |
| **CodeFuse-CodeGeeX2-6B** | 45.12% | 2023.11 |
| WizardCoder-Python-34B-V1.0 | 73.2% | 2023.8 |
| GPT-4(zero-shot) | 67.0% | 2023.3 |
| PanGu-Coder2 15B | 61.6% | 2023.8 |
| CodeLlama-34b-Python | 53.7% | 2023.8 |
| CodeLlama-34b | 48.8% | 2023.8 |
| GPT-3.5(zero-shot) | 48.1% | 2022.11 |
| OctoCoder | 46.2% | 2023.8 |
| StarCoder-15B | 33.6% | 2023.5 |
| Qwen-14b | 32.3% | 2023.10 |
### NLP

<br>
## Requirements
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
<br>
## Inference String Format
The inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.
Here are examples of prompts used to request the model:
**Multi-Round with System Prompt:**
```python
"""
<s>system
System instruction
<s>human
Human 1st round input
<s>bot
Bot 1st round output<|end▁of▁sentence|>
<s>human
Human 2nd round input
<s>bot
Bot 2nd round output<|end▁of▁sentence|>
...
...
...
<s>human
Human nth round input
<s>bot
"""
```
**Single-Round without System Prompt:**
```python
"""
<s>human
User prompt...
<s>bot
"""
```
In this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with "\<s\>bot" to ask the model generating answers.
For example, the format used to infer HumanEval is like the following:
```
<s>human
# language: Python
from typing import List
def separate_paren_groups(paren_string: str) -> List[str]:
""" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to
separate those group into separate strings and return the list of those.
Separate groups are balanced (each open brace is properly closed) and not nested within each other
Ignore any spaces in the input string.
>>> separate_paren_groups('( ) (( )) (( )( ))')
['()', '(())', '(()())']
"""
<s>bot
```
Specifically, we also add the Programming Language Tag (e.g. "```# language: Python```" for Python) used by CodeGeex models.
## Quickstart
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_dir = "codefuse-ai/CodeFuse-DeepSeek-33B"
def load_model_tokenizer(model_path):
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
tokenizer.eos_token = "<|end▁of▁sentence|>"
tokenizer.pad_token = "<|end▁of▁sentence|>"
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
tokenizer.padding_side = "left"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto',torch_dtype=torch.bfloat16, trust_remote_code=True)
return model, tokenizer
HUMAN_ROLE_START_TAG = "<s>human\n"
BOT_ROLE_START_TAG = "<s>bot\n"
text_list = [f'{HUMAN_ROLE_START_TAG}Write a QuickSort program\n#Python\n{BOT_ROLE_START_TAG}']
model, tokenizer = load_model_tokenizer(model_dir)
inputs = tokenizer(text_list, return_tensors='pt', padding=True, add_special_tokens=False).to('cuda')
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
generation_config = GenerationConfig(
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
temperature=0.1,
max_new_tokens=512,
num_return_sequences=1,
num_beams=1,
top_p=0.95,
do_sample=False
)
outputs = model.generate(
inputs= input_ids,
attention_mask=attention_mask,
**generation_config.to_dict()
)
gen_text = tokenizer.batch_decode(outputs[:, input_ids.shape[1]:], skip_special_tokens=True)
print(gen_text[0])
```
<a id="chinese"></a>
## 模型简介
CodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。
<br>
## 新闻
🔥🔥🔥 2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。
🔥🔥🔥 2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)
🔥🔥🔥 2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:https://mp.weixin.qq.com/s/PCQPkvbvfxSPzsqjOILCDw
🔥🔥🔥 2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)
🔥🔥🔥 2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)
🔥🔥🔥 2023-09-26 [CodeFuse-CodeLlama-34B 4bits](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B-4bits/summary)量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。
🔥🔥🔥 2023-09-11 [CodeFuse-CodeLlama-34B](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B/summary)发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。
<br>
## 代码社区
**大本营**: 🏡 https://github.com/codefuse-ai (**请支持我们的项目Star🌟 + Fork🚀 + Watch👀**)
+ 如果您想自己微调该模型,可以访问 ✨[MFTCoder](https://github.com/codefuse-ai/MFTCoder)✨✨
+ 如果您想观看该模型示例,可以访问 ✨[CodeFuse Demo](https://github.com/codefuse-ai/codefuse)✨✨
<br>
## 评测表现
### 代码
| 模型 | HumanEval(pass@1) | 日期 |
|:----------------------------|:-----------------:|:-------:|
| **CodeFuse-CodeLlama-34B** | 74.4% | 2023.9 |
|**CodeFuse-CodeLlama-34B-4bits** | 73.8% | 2023.9 |
| WizardCoder-Python-34B-V1.0 | 73.2% | 2023.8 |
| GPT-4(zero-shot) | 67.0% | 2023.3 |
| PanGu-Coder2 15B | 61.6% | 2023.8 |
| CodeLlama-34b-Python | 53.7% | 2023.8 |
| CodeLlama-34b | 48.8% | 2023.8 |
| GPT-3.5(zero-shot) | 48.1% | 2022.11 |
| OctoCoder | 46.2% | 2023.8 |
| StarCoder-15B | 33.6% | 2023.5 |
| Qwen-14b | 32.3% | 2023.10 |
| **CodeFuse-StarCoder-15B** | 54.9% | 2023.9 |
| **CodeFuse-QWen-14B** | 48.78% | 2023.8 |
| **CodeFuse-CodeGeeX2-6B** | 45.12% | 2023.11 |
| **CodeFuse-DeepSeek-33B**. | **78.65%** | 2024.01 |
### NLP

## Requirements
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
<br>
## 推理数据格式
推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:
**带System提示的多轮会话格式:**
```python
"""
<s>system
System instruction
<s>human
Human 1st round input
<s>bot
Bot 1st round output<|end▁of▁sentence|>
<s>human
Human 2nd round input
<s>bot
Bot 2nd round output<|end▁of▁sentence|>
...
...
...
<s>human
Human nth round input
<s>bot
"""
```
**不带System提示的单轮会话格式:**
```python
"""
<s>human
User prompt...
<s>bot
"""
```
在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以"\<s\>bot\n"结尾,引导模型生成回答。
例如,推理HumanEval数据时使用的格式如下所示:
```python
<s>human
# language: Python
from typing import List
def separate_paren_groups(paren_string: str) -> List[str]:
""" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to
separate those group into separate strings and return the list of those.
Separate groups are balanced (each open brace is properly closed) and not nested within each other
Ignore any spaces in the input string.
>>> separate_paren_groups('( ) (( )) (( )( ))')
['()', '(())', '(()())']
"""
<s>bot
```
特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用"```# language: Python```")。
## 快速使用
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_dir = "codefuse-ai/CodeFuse-DeepSeek-33B"
def load_model_tokenizer(model_path):
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
tokenizer.eos_token = "<|end▁of▁sentence|>"
tokenizer.pad_token = "<|end▁of▁sentence|>"
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
tokenizer.padding_side = "left"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto',torch_dtype=torch.bfloat16, trust_remote_code=True)
return model, tokenizer
HUMAN_ROLE_START_TAG = "<s>human\n"
BOT_ROLE_START_TAG = "<s>bot\n"
text_list = [f'{HUMAN_ROLE_START_TAG}请写一个快排程序\n#Python\n{BOT_ROLE_START_TAG}']
model, tokenizer = load_model_tokenizer(model_dir)
inputs = tokenizer(text_list, return_tensors='pt', padding=True, add_special_tokens=False).to('cuda')
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
generation_config = GenerationConfig(
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
temperature=0.2,
max_new_tokens=512,
num_return_sequences=1,
num_beams=1,
top_p=0.95,
do_sample=False
)
outputs = model.generate(
inputs= input_ids,
attention_mask=attention_mask,
**generation_config.to_dict()
)
gen_text = tokenizer.batch_decode(outputs[:, input_ids.shape[1]:], skip_special_tokens=True)
print(gen_text[0])
```
| {"license": "other", "tasks": ["code-generation"]} | text-generation | LoneStriker/CodeFuse-DeepSeek-33B-4.65bpw-h6-exl2 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T19:08:59+00:00 | [] | [] | TAGS
#transformers #pytorch #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card for CodeFuse-DeepSeek-33B
====================================
!logo
[[中文]](#chinese) [[English]](#english)
Model Description
-----------------
CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-related tasks on the base model DeepSeek-Coder-33B.
News and Updates
----------------
2024-01-12 CodeFuse-DeepSeek-33B has been released, achieving a pass@1 (greedy decoding) score of 78.65% on HumanEval.
2024-01-12 CodeFuse-Mixtral-8x7B has been released, achieving a pass@1 (greedy decoding) score of 56.1% on HumanEval, which is a 15% increase compared to Mixtral-8x7b's 40%.
2023-11-10 CodeFuse-CodeGeeX2-6B has been released, achieving a pass@1 (greedy decoding) score of 45.12% on HumanEval, which is a 9.22% increase compared to CodeGeeX2 35.9%.
2023-10-20 CodeFuse-QWen-14B technical documentation has been released. For those interested, please refer to the CodeFuse article on our WeChat official account via the provided link.(URL
2023-10-16 CodeFuse-QWen-14B has been released, achieving a pass@1 (greedy decoding) score of 48.78% on HumanEval, which is a 16% increase compared to Qwen-14b's 32.3%.
2023-09-27 CodeFuse-StarCoder-15B has been released, achieving a pass@1 (greedy decoding) score of 54.9% on HumanEval, which is a 21% increase compared to StarCoder's 33.6%.
2023-09-26 We are pleased to announce the release of the 4-bit quantized version of CodeFuse-CodeLlama-34B. Despite the quantization process, the model still achieves a remarkable 73.8% accuracy (greedy decoding) on the HumanEval pass@1 metric.
2023-09-11 CodeFuse-CodeLlama-34B has achieved 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for openspurced LLMs at present.
Code Community
--------------
Homepage: URL (Please give us your support with a Star + Fork + Watch)
* If you wish to fine-tune the model yourself, you can visit MFTCoder
* If you wish to see a demo of the model, you can visit CodeFuse Demo
Performance
-----------
### Code
### NLP
!NLP Performance Radar
Requirements
------------
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
Inference String Format
-----------------------
The inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.
Here are examples of prompts used to request the model:
Multi-Round with System Prompt:
Single-Round without System Prompt:
In this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with "<s>bot" to ask the model generating answers.
For example, the format used to infer HumanEval is like the following:
Specifically, we also add the Programming Language Tag (e.g. "" for Python) used by CodeGeex models.
Quickstart
----------
模型简介
----
CodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。
新闻
--
2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。
2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)
2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:URL
2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)
2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)
2023-09-26 CodeFuse-CodeLlama-34B 4bits量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。
2023-09-11 CodeFuse-CodeLlama-34B发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。
代码社区
----
大本营: URL (请支持我们的项目Star + Fork + Watch)
* 如果您想自己微调该模型,可以访问 MFTCoder
* 如果您想观看该模型示例,可以访问 CodeFuse Demo
评测表现
----
### 代码
### NLP
!NLP Performance Radar
Requirements
------------
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
推理数据格式
------
推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:
带System提示的多轮会话格式:
不带System提示的单轮会话格式:
在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以"<s>bot\n"结尾,引导模型生成回答。
例如,推理HumanEval数据时使用的格式如下所示:
特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用"")。
快速使用
----
| [
"### Code",
"### NLP\n\n\n!NLP Performance Radar\n\n\n \n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\nInference String Format\n-----------------------\n\n\nThe inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.\nHere are examples of prompts used to request the model:\n\n\nMulti-Round with System Prompt:\n\n\nSingle-Round without System Prompt:\n\n\nIn this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with \"<s>bot\" to ask the model generating answers.\n\n\nFor example, the format used to infer HumanEval is like the following:\n\n\nSpecifically, we also add the Programming Language Tag (e.g. \"\" for Python) used by CodeGeex models.\n\n\nQuickstart\n----------\n\n\n\n模型简介\n----\n\n\nCodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。\n \n\n\n\n新闻\n--\n\n\n2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。\n\n\n2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)\n\n\n2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:URL\n\n\n2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)\n\n\n2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)\n\n\n2023-09-26 CodeFuse-CodeLlama-34B 4bits量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。\n\n\n2023-09-11 CodeFuse-CodeLlama-34B发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。\n\n\n \n\n代码社区\n----\n\n\n大本营: URL (请支持我们的项目Star + Fork + Watch)\n\n\n* 如果您想自己微调该模型,可以访问 MFTCoder\n* 如果您想观看该模型示例,可以访问 CodeFuse Demo\n\n\n \n\n评测表现\n----",
"### 代码",
"### NLP\n\n\n!NLP Performance Radar\n\n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\n推理数据格式\n------\n\n\n推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:\n\n\n带System提示的多轮会话格式:\n\n\n不带System提示的单轮会话格式:\n\n\n在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以\"<s>bot\\n\"结尾,引导模型生成回答。\n\n\n例如,推理HumanEval数据时使用的格式如下所示:\n\n\n特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用\"\")。\n\n\n快速使用\n----"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Code",
"### NLP\n\n\n!NLP Performance Radar\n\n\n \n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\nInference String Format\n-----------------------\n\n\nThe inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.\nHere are examples of prompts used to request the model:\n\n\nMulti-Round with System Prompt:\n\n\nSingle-Round without System Prompt:\n\n\nIn this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with \"<s>bot\" to ask the model generating answers.\n\n\nFor example, the format used to infer HumanEval is like the following:\n\n\nSpecifically, we also add the Programming Language Tag (e.g. \"\" for Python) used by CodeGeex models.\n\n\nQuickstart\n----------\n\n\n\n模型简介\n----\n\n\nCodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。\n \n\n\n\n新闻\n--\n\n\n2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。\n\n\n2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)\n\n\n2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:URL\n\n\n2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)\n\n\n2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)\n\n\n2023-09-26 CodeFuse-CodeLlama-34B 4bits量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。\n\n\n2023-09-11 CodeFuse-CodeLlama-34B发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。\n\n\n \n\n代码社区\n----\n\n\n大本营: URL (请支持我们的项目Star + Fork + Watch)\n\n\n* 如果您想自己微调该模型,可以访问 MFTCoder\n* 如果您想观看该模型示例,可以访问 CodeFuse Demo\n\n\n \n\n评测表现\n----",
"### 代码",
"### NLP\n\n\n!NLP Performance Radar\n\n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\n推理数据格式\n------\n\n\n推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:\n\n\n带System提示的多轮会话格式:\n\n\n不带System提示的单轮会话格式:\n\n\n在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以\"<s>bot\\n\"结尾,引导模型生成回答。\n\n\n例如,推理HumanEval数据时使用的格式如下所示:\n\n\n特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用\"\")。\n\n\n快速使用\n----"
] | [
55,
3,
656,
4,
244
] | [
"passage: TAGS\n#transformers #pytorch #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Code"
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null | null | transformers | # Model Card for CodeFuse-DeepSeek-33B

[[中文]](#chinese) [[English]](#english)
<a id="english"></a>
## Model Description
CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-related tasks on the base model DeepSeek-Coder-33B.
<br>
## News and Updates
🔥🔥🔥 2024-01-12 CodeFuse-DeepSeek-33B has been released, achieving a pass@1 (greedy decoding) score of 78.65% on HumanEval.
🔥🔥🔥 2024-01-12 CodeFuse-Mixtral-8x7B has been released, achieving a pass@1 (greedy decoding) score of 56.1% on HumanEval, which is a 15% increase compared to Mixtral-8x7b's 40%.
🔥🔥 2023-11-10 CodeFuse-CodeGeeX2-6B has been released, achieving a pass@1 (greedy decoding) score of 45.12% on HumanEval, which is a 9.22% increase compared to CodeGeeX2 35.9%.
🔥🔥 2023-10-20 CodeFuse-QWen-14B technical documentation has been released. For those interested, please refer to the CodeFuse article on our WeChat official account via the provided link.(https://mp.weixin.qq.com/s/PCQPkvbvfxSPzsqjOILCDw)
🔥🔥 2023-10-16 CodeFuse-QWen-14B has been released, achieving a pass@1 (greedy decoding) score of 48.78% on HumanEval, which is a 16% increase compared to Qwen-14b's 32.3%.
🔥🔥 2023-09-27 CodeFuse-StarCoder-15B has been released, achieving a pass@1 (greedy decoding) score of 54.9% on HumanEval, which is a 21% increase compared to StarCoder's 33.6%.
🔥🔥 2023-09-26 We are pleased to announce the release of the 4-bit quantized version of CodeFuse-CodeLlama-34B. Despite the quantization process, the model still achieves a remarkable 73.8% accuracy (greedy decoding) on the HumanEval pass@1 metric.
🔥🔥 2023-09-11 CodeFuse-CodeLlama-34B has achieved 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for openspurced LLMs at present.
<br>
## Code Community
**Homepage**: 🏡 https://github.com/codefuse-ai (**Please give us your support with a Star🌟 + Fork🚀 + Watch👀**)
+ If you wish to fine-tune the model yourself, you can visit ✨[MFTCoder](https://github.com/codefuse-ai/MFTCoder)✨✨
+ If you wish to see a demo of the model, you can visit ✨[CodeFuse Demo](https://github.com/codefuse-ai/codefuse)✨✨
<br>
## Performance
### Code
| Model | HumanEval(pass@1) | Date |
|:----------------------------|:-----------------:|:-------:|
| **CodeFuse-DeepSeek-33B** | **78.65%** | 2024.01 |
| **CodeFuse-Mixtral-8x7B** | **56.10%** | 2024.01 |
| **CodeFuse-CodeLlama-34B** | 74.4% | 2023.9 |
|**CodeFuse-CodeLlama-34B-4bits** | 73.8% | 2023.9 |
| **CodeFuse-StarCoder-15B** | 54.9% | 2023.9 |
| **CodeFuse-QWen-14B** | 48.78% | 2023.10 |
| **CodeFuse-CodeGeeX2-6B** | 45.12% | 2023.11 |
| WizardCoder-Python-34B-V1.0 | 73.2% | 2023.8 |
| GPT-4(zero-shot) | 67.0% | 2023.3 |
| PanGu-Coder2 15B | 61.6% | 2023.8 |
| CodeLlama-34b-Python | 53.7% | 2023.8 |
| CodeLlama-34b | 48.8% | 2023.8 |
| GPT-3.5(zero-shot) | 48.1% | 2022.11 |
| OctoCoder | 46.2% | 2023.8 |
| StarCoder-15B | 33.6% | 2023.5 |
| Qwen-14b | 32.3% | 2023.10 |
### NLP

<br>
## Requirements
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
<br>
## Inference String Format
The inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.
Here are examples of prompts used to request the model:
**Multi-Round with System Prompt:**
```python
"""
<s>system
System instruction
<s>human
Human 1st round input
<s>bot
Bot 1st round output<|end▁of▁sentence|>
<s>human
Human 2nd round input
<s>bot
Bot 2nd round output<|end▁of▁sentence|>
...
...
...
<s>human
Human nth round input
<s>bot
"""
```
**Single-Round without System Prompt:**
```python
"""
<s>human
User prompt...
<s>bot
"""
```
In this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with "\<s\>bot" to ask the model generating answers.
For example, the format used to infer HumanEval is like the following:
```
<s>human
# language: Python
from typing import List
def separate_paren_groups(paren_string: str) -> List[str]:
""" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to
separate those group into separate strings and return the list of those.
Separate groups are balanced (each open brace is properly closed) and not nested within each other
Ignore any spaces in the input string.
>>> separate_paren_groups('( ) (( )) (( )( ))')
['()', '(())', '(()())']
"""
<s>bot
```
Specifically, we also add the Programming Language Tag (e.g. "```# language: Python```" for Python) used by CodeGeex models.
## Quickstart
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_dir = "codefuse-ai/CodeFuse-DeepSeek-33B"
def load_model_tokenizer(model_path):
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
tokenizer.eos_token = "<|end▁of▁sentence|>"
tokenizer.pad_token = "<|end▁of▁sentence|>"
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
tokenizer.padding_side = "left"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto',torch_dtype=torch.bfloat16, trust_remote_code=True)
return model, tokenizer
HUMAN_ROLE_START_TAG = "<s>human\n"
BOT_ROLE_START_TAG = "<s>bot\n"
text_list = [f'{HUMAN_ROLE_START_TAG}Write a QuickSort program\n#Python\n{BOT_ROLE_START_TAG}']
model, tokenizer = load_model_tokenizer(model_dir)
inputs = tokenizer(text_list, return_tensors='pt', padding=True, add_special_tokens=False).to('cuda')
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
generation_config = GenerationConfig(
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
temperature=0.1,
max_new_tokens=512,
num_return_sequences=1,
num_beams=1,
top_p=0.95,
do_sample=False
)
outputs = model.generate(
inputs= input_ids,
attention_mask=attention_mask,
**generation_config.to_dict()
)
gen_text = tokenizer.batch_decode(outputs[:, input_ids.shape[1]:], skip_special_tokens=True)
print(gen_text[0])
```
<a id="chinese"></a>
## 模型简介
CodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。
<br>
## 新闻
🔥🔥🔥 2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。
🔥🔥🔥 2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)
🔥🔥🔥 2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:https://mp.weixin.qq.com/s/PCQPkvbvfxSPzsqjOILCDw
🔥🔥🔥 2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)
🔥🔥🔥 2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)
🔥🔥🔥 2023-09-26 [CodeFuse-CodeLlama-34B 4bits](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B-4bits/summary)量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。
🔥🔥🔥 2023-09-11 [CodeFuse-CodeLlama-34B](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B/summary)发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。
<br>
## 代码社区
**大本营**: 🏡 https://github.com/codefuse-ai (**请支持我们的项目Star🌟 + Fork🚀 + Watch👀**)
+ 如果您想自己微调该模型,可以访问 ✨[MFTCoder](https://github.com/codefuse-ai/MFTCoder)✨✨
+ 如果您想观看该模型示例,可以访问 ✨[CodeFuse Demo](https://github.com/codefuse-ai/codefuse)✨✨
<br>
## 评测表现
### 代码
| 模型 | HumanEval(pass@1) | 日期 |
|:----------------------------|:-----------------:|:-------:|
| **CodeFuse-CodeLlama-34B** | 74.4% | 2023.9 |
|**CodeFuse-CodeLlama-34B-4bits** | 73.8% | 2023.9 |
| WizardCoder-Python-34B-V1.0 | 73.2% | 2023.8 |
| GPT-4(zero-shot) | 67.0% | 2023.3 |
| PanGu-Coder2 15B | 61.6% | 2023.8 |
| CodeLlama-34b-Python | 53.7% | 2023.8 |
| CodeLlama-34b | 48.8% | 2023.8 |
| GPT-3.5(zero-shot) | 48.1% | 2022.11 |
| OctoCoder | 46.2% | 2023.8 |
| StarCoder-15B | 33.6% | 2023.5 |
| Qwen-14b | 32.3% | 2023.10 |
| **CodeFuse-StarCoder-15B** | 54.9% | 2023.9 |
| **CodeFuse-QWen-14B** | 48.78% | 2023.8 |
| **CodeFuse-CodeGeeX2-6B** | 45.12% | 2023.11 |
| **CodeFuse-DeepSeek-33B**. | **78.65%** | 2024.01 |
### NLP

## Requirements
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
<br>
## 推理数据格式
推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:
**带System提示的多轮会话格式:**
```python
"""
<s>system
System instruction
<s>human
Human 1st round input
<s>bot
Bot 1st round output<|end▁of▁sentence|>
<s>human
Human 2nd round input
<s>bot
Bot 2nd round output<|end▁of▁sentence|>
...
...
...
<s>human
Human nth round input
<s>bot
"""
```
**不带System提示的单轮会话格式:**
```python
"""
<s>human
User prompt...
<s>bot
"""
```
在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以"\<s\>bot\n"结尾,引导模型生成回答。
例如,推理HumanEval数据时使用的格式如下所示:
```python
<s>human
# language: Python
from typing import List
def separate_paren_groups(paren_string: str) -> List[str]:
""" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to
separate those group into separate strings and return the list of those.
Separate groups are balanced (each open brace is properly closed) and not nested within each other
Ignore any spaces in the input string.
>>> separate_paren_groups('( ) (( )) (( )( ))')
['()', '(())', '(()())']
"""
<s>bot
```
特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用"```# language: Python```")。
## 快速使用
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_dir = "codefuse-ai/CodeFuse-DeepSeek-33B"
def load_model_tokenizer(model_path):
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
tokenizer.eos_token = "<|end▁of▁sentence|>"
tokenizer.pad_token = "<|end▁of▁sentence|>"
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
tokenizer.padding_side = "left"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto',torch_dtype=torch.bfloat16, trust_remote_code=True)
return model, tokenizer
HUMAN_ROLE_START_TAG = "<s>human\n"
BOT_ROLE_START_TAG = "<s>bot\n"
text_list = [f'{HUMAN_ROLE_START_TAG}请写一个快排程序\n#Python\n{BOT_ROLE_START_TAG}']
model, tokenizer = load_model_tokenizer(model_dir)
inputs = tokenizer(text_list, return_tensors='pt', padding=True, add_special_tokens=False).to('cuda')
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
generation_config = GenerationConfig(
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
temperature=0.2,
max_new_tokens=512,
num_return_sequences=1,
num_beams=1,
top_p=0.95,
do_sample=False
)
outputs = model.generate(
inputs= input_ids,
attention_mask=attention_mask,
**generation_config.to_dict()
)
gen_text = tokenizer.batch_decode(outputs[:, input_ids.shape[1]:], skip_special_tokens=True)
print(gen_text[0])
```
| {"license": "other", "tasks": ["code-generation"]} | text-generation | LoneStriker/CodeFuse-DeepSeek-33B-5.0bpw-h6-exl2 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T19:18:29+00:00 | [] | [] | TAGS
#transformers #pytorch #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card for CodeFuse-DeepSeek-33B
====================================
!logo
[[中文]](#chinese) [[English]](#english)
Model Description
-----------------
CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-related tasks on the base model DeepSeek-Coder-33B.
News and Updates
----------------
2024-01-12 CodeFuse-DeepSeek-33B has been released, achieving a pass@1 (greedy decoding) score of 78.65% on HumanEval.
2024-01-12 CodeFuse-Mixtral-8x7B has been released, achieving a pass@1 (greedy decoding) score of 56.1% on HumanEval, which is a 15% increase compared to Mixtral-8x7b's 40%.
2023-11-10 CodeFuse-CodeGeeX2-6B has been released, achieving a pass@1 (greedy decoding) score of 45.12% on HumanEval, which is a 9.22% increase compared to CodeGeeX2 35.9%.
2023-10-20 CodeFuse-QWen-14B technical documentation has been released. For those interested, please refer to the CodeFuse article on our WeChat official account via the provided link.(URL
2023-10-16 CodeFuse-QWen-14B has been released, achieving a pass@1 (greedy decoding) score of 48.78% on HumanEval, which is a 16% increase compared to Qwen-14b's 32.3%.
2023-09-27 CodeFuse-StarCoder-15B has been released, achieving a pass@1 (greedy decoding) score of 54.9% on HumanEval, which is a 21% increase compared to StarCoder's 33.6%.
2023-09-26 We are pleased to announce the release of the 4-bit quantized version of CodeFuse-CodeLlama-34B. Despite the quantization process, the model still achieves a remarkable 73.8% accuracy (greedy decoding) on the HumanEval pass@1 metric.
2023-09-11 CodeFuse-CodeLlama-34B has achieved 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for openspurced LLMs at present.
Code Community
--------------
Homepage: URL (Please give us your support with a Star + Fork + Watch)
* If you wish to fine-tune the model yourself, you can visit MFTCoder
* If you wish to see a demo of the model, you can visit CodeFuse Demo
Performance
-----------
### Code
### NLP
!NLP Performance Radar
Requirements
------------
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
Inference String Format
-----------------------
The inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.
Here are examples of prompts used to request the model:
Multi-Round with System Prompt:
Single-Round without System Prompt:
In this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with "<s>bot" to ask the model generating answers.
For example, the format used to infer HumanEval is like the following:
Specifically, we also add the Programming Language Tag (e.g. "" for Python) used by CodeGeex models.
Quickstart
----------
模型简介
----
CodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。
新闻
--
2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。
2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)
2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:URL
2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)
2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)
2023-09-26 CodeFuse-CodeLlama-34B 4bits量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。
2023-09-11 CodeFuse-CodeLlama-34B发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。
代码社区
----
大本营: URL (请支持我们的项目Star + Fork + Watch)
* 如果您想自己微调该模型,可以访问 MFTCoder
* 如果您想观看该模型示例,可以访问 CodeFuse Demo
评测表现
----
### 代码
### NLP
!NLP Performance Radar
Requirements
------------
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
推理数据格式
------
推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:
带System提示的多轮会话格式:
不带System提示的单轮会话格式:
在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以"<s>bot\n"结尾,引导模型生成回答。
例如,推理HumanEval数据时使用的格式如下所示:
特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用"")。
快速使用
----
| [
"### Code",
"### NLP\n\n\n!NLP Performance Radar\n\n\n \n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\nInference String Format\n-----------------------\n\n\nThe inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.\nHere are examples of prompts used to request the model:\n\n\nMulti-Round with System Prompt:\n\n\nSingle-Round without System Prompt:\n\n\nIn this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with \"<s>bot\" to ask the model generating answers.\n\n\nFor example, the format used to infer HumanEval is like the following:\n\n\nSpecifically, we also add the Programming Language Tag (e.g. \"\" for Python) used by CodeGeex models.\n\n\nQuickstart\n----------\n\n\n\n模型简介\n----\n\n\nCodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。\n \n\n\n\n新闻\n--\n\n\n2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。\n\n\n2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)\n\n\n2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:URL\n\n\n2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)\n\n\n2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)\n\n\n2023-09-26 CodeFuse-CodeLlama-34B 4bits量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。\n\n\n2023-09-11 CodeFuse-CodeLlama-34B发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。\n\n\n \n\n代码社区\n----\n\n\n大本营: URL (请支持我们的项目Star + Fork + Watch)\n\n\n* 如果您想自己微调该模型,可以访问 MFTCoder\n* 如果您想观看该模型示例,可以访问 CodeFuse Demo\n\n\n \n\n评测表现\n----",
"### 代码",
"### NLP\n\n\n!NLP Performance Radar\n\n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\n推理数据格式\n------\n\n\n推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:\n\n\n带System提示的多轮会话格式:\n\n\n不带System提示的单轮会话格式:\n\n\n在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以\"<s>bot\\n\"结尾,引导模型生成回答。\n\n\n例如,推理HumanEval数据时使用的格式如下所示:\n\n\n特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用\"\")。\n\n\n快速使用\n----"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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"### NLP\n\n\n!NLP Performance Radar\n\n\n \n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\nInference String Format\n-----------------------\n\n\nThe inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.\nHere are examples of prompts used to request the model:\n\n\nMulti-Round with System Prompt:\n\n\nSingle-Round without System Prompt:\n\n\nIn this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with \"<s>bot\" to ask the model generating answers.\n\n\nFor example, the format used to infer HumanEval is like the following:\n\n\nSpecifically, we also add the Programming Language Tag (e.g. \"\" for Python) used by CodeGeex models.\n\n\nQuickstart\n----------\n\n\n\n模型简介\n----\n\n\nCodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。\n \n\n\n\n新闻\n--\n\n\n2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。\n\n\n2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)\n\n\n2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:URL\n\n\n2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)\n\n\n2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)\n\n\n2023-09-26 CodeFuse-CodeLlama-34B 4bits量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。\n\n\n2023-09-11 CodeFuse-CodeLlama-34B发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。\n\n\n \n\n代码社区\n----\n\n\n大本营: URL (请支持我们的项目Star + Fork + Watch)\n\n\n* 如果您想自己微调该模型,可以访问 MFTCoder\n* 如果您想观看该模型示例,可以访问 CodeFuse Demo\n\n\n \n\n评测表现\n----",
"### 代码",
"### NLP\n\n\n!NLP Performance Radar\n\n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\n推理数据格式\n------\n\n\n推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:\n\n\n带System提示的多轮会话格式:\n\n\n不带System提示的单轮会话格式:\n\n\n在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以\"<s>bot\\n\"结尾,引导模型生成回答。\n\n\n例如,推理HumanEval数据时使用的格式如下所示:\n\n\n特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用\"\")。\n\n\n快速使用\n----"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# shruthicapstone-bertbase-qa
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 5.9555
- Validation Loss: 5.9507
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 5.9555 | 5.9507 | 0 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "bert-base-cased", "model-index": [{"name": "shruthicapstone-bertbase-qa", "results": []}]} | question-answering | Shruthi-S/shruthicapstone-bertbase-qa | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"base_model:bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-09T19:19:50+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #base_model-bert-base-cased #license-apache-2.0 #endpoints_compatible #region-us
| shruthicapstone-bertbase-qa
===========================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 5.9555
* Validation Loss: 5.9507
* Epoch: 0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': 0.001, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: mixed\_float16
### Training results
### Framework versions
* Transformers 4.35.2
* TensorFlow 2.15.0
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': 0.001, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: mixed\\_float16",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #base_model-bert-base-cased #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': 0.001, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: mixed\\_float16",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
58,
198,
4,
31
] | [
"passage: TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #base_model-bert-base-cased #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': 0.001, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: mixed\\_float16### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | null | Linked to ml-mgie simplification hosted at [github.com/paulasquin/ml-mgie](https://github.com/paulasquin/ml-mgie)
Proposing ready to use
- `LLaVA-7B-v1` model, originally from [liuhaotian's LLaVA-Lightning-7B-delta-v1-1](https://huggingface.co/liuhaotian/LLaVA-Lightning-7B-delta-v1-1) , sourced from [tsujuifu's repo](https://github.com/tsujuifu/pytorch_mgie?tab=readme-ov-file#:~:text=Put%20official%20LLaVA%2D7B)
- `mgie_7b model` (IPr2Pr + MagicBrush) from [Apple's repo](https://github.com/apple/ml-mgie#:~:text=v1%20and%20download-,pre%2Dtrained%20ckpt,-(on%20IPr2Pr%20%2B%20MagicBrush) with [its own licence](https://github.com/apple/ml-mgie?tab=License-1-ov-file#readme)
This is only a conveniance storage. I do not hold any credits for these models. | {"license": "apache-2.0", "tags": ["llava", "ml-mgie"]} | null | paulasquin/ml-mgie | [
"llava",
"ml-mgie",
"license:apache-2.0",
"region:us"
] | 2024-02-09T19:26:56+00:00 | [] | [] | TAGS
#llava #ml-mgie #license-apache-2.0 #region-us
| Linked to ml-mgie simplification hosted at URL
Proposing ready to use
- 'LLaVA-7B-v1' model, originally from liuhaotian's LLaVA-Lightning-7B-delta-v1-1 , sourced from tsujuifu's repo
- 'mgie_7b model' (IPr2Pr + MagicBrush) from Apple's repo with its own licence
This is only a conveniance storage. I do not hold any credits for these models. | [] | [
"TAGS\n#llava #ml-mgie #license-apache-2.0 #region-us \n"
] | [
22
] | [
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null | null | transformers | # Model Card for CodeFuse-DeepSeek-33B

[[中文]](#chinese) [[English]](#english)
<a id="english"></a>
## Model Description
CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-related tasks on the base model DeepSeek-Coder-33B.
<br>
## News and Updates
🔥🔥🔥 2024-01-12 CodeFuse-DeepSeek-33B has been released, achieving a pass@1 (greedy decoding) score of 78.65% on HumanEval.
🔥🔥🔥 2024-01-12 CodeFuse-Mixtral-8x7B has been released, achieving a pass@1 (greedy decoding) score of 56.1% on HumanEval, which is a 15% increase compared to Mixtral-8x7b's 40%.
🔥🔥 2023-11-10 CodeFuse-CodeGeeX2-6B has been released, achieving a pass@1 (greedy decoding) score of 45.12% on HumanEval, which is a 9.22% increase compared to CodeGeeX2 35.9%.
🔥🔥 2023-10-20 CodeFuse-QWen-14B technical documentation has been released. For those interested, please refer to the CodeFuse article on our WeChat official account via the provided link.(https://mp.weixin.qq.com/s/PCQPkvbvfxSPzsqjOILCDw)
🔥🔥 2023-10-16 CodeFuse-QWen-14B has been released, achieving a pass@1 (greedy decoding) score of 48.78% on HumanEval, which is a 16% increase compared to Qwen-14b's 32.3%.
🔥🔥 2023-09-27 CodeFuse-StarCoder-15B has been released, achieving a pass@1 (greedy decoding) score of 54.9% on HumanEval, which is a 21% increase compared to StarCoder's 33.6%.
🔥🔥 2023-09-26 We are pleased to announce the release of the 4-bit quantized version of CodeFuse-CodeLlama-34B. Despite the quantization process, the model still achieves a remarkable 73.8% accuracy (greedy decoding) on the HumanEval pass@1 metric.
🔥🔥 2023-09-11 CodeFuse-CodeLlama-34B has achieved 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for openspurced LLMs at present.
<br>
## Code Community
**Homepage**: 🏡 https://github.com/codefuse-ai (**Please give us your support with a Star🌟 + Fork🚀 + Watch👀**)
+ If you wish to fine-tune the model yourself, you can visit ✨[MFTCoder](https://github.com/codefuse-ai/MFTCoder)✨✨
+ If you wish to see a demo of the model, you can visit ✨[CodeFuse Demo](https://github.com/codefuse-ai/codefuse)✨✨
<br>
## Performance
### Code
| Model | HumanEval(pass@1) | Date |
|:----------------------------|:-----------------:|:-------:|
| **CodeFuse-DeepSeek-33B** | **78.65%** | 2024.01 |
| **CodeFuse-Mixtral-8x7B** | **56.10%** | 2024.01 |
| **CodeFuse-CodeLlama-34B** | 74.4% | 2023.9 |
|**CodeFuse-CodeLlama-34B-4bits** | 73.8% | 2023.9 |
| **CodeFuse-StarCoder-15B** | 54.9% | 2023.9 |
| **CodeFuse-QWen-14B** | 48.78% | 2023.10 |
| **CodeFuse-CodeGeeX2-6B** | 45.12% | 2023.11 |
| WizardCoder-Python-34B-V1.0 | 73.2% | 2023.8 |
| GPT-4(zero-shot) | 67.0% | 2023.3 |
| PanGu-Coder2 15B | 61.6% | 2023.8 |
| CodeLlama-34b-Python | 53.7% | 2023.8 |
| CodeLlama-34b | 48.8% | 2023.8 |
| GPT-3.5(zero-shot) | 48.1% | 2022.11 |
| OctoCoder | 46.2% | 2023.8 |
| StarCoder-15B | 33.6% | 2023.5 |
| Qwen-14b | 32.3% | 2023.10 |
### NLP

<br>
## Requirements
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
<br>
## Inference String Format
The inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.
Here are examples of prompts used to request the model:
**Multi-Round with System Prompt:**
```python
"""
<s>system
System instruction
<s>human
Human 1st round input
<s>bot
Bot 1st round output<|end▁of▁sentence|>
<s>human
Human 2nd round input
<s>bot
Bot 2nd round output<|end▁of▁sentence|>
...
...
...
<s>human
Human nth round input
<s>bot
"""
```
**Single-Round without System Prompt:**
```python
"""
<s>human
User prompt...
<s>bot
"""
```
In this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with "\<s\>bot" to ask the model generating answers.
For example, the format used to infer HumanEval is like the following:
```
<s>human
# language: Python
from typing import List
def separate_paren_groups(paren_string: str) -> List[str]:
""" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to
separate those group into separate strings and return the list of those.
Separate groups are balanced (each open brace is properly closed) and not nested within each other
Ignore any spaces in the input string.
>>> separate_paren_groups('( ) (( )) (( )( ))')
['()', '(())', '(()())']
"""
<s>bot
```
Specifically, we also add the Programming Language Tag (e.g. "```# language: Python```" for Python) used by CodeGeex models.
## Quickstart
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_dir = "codefuse-ai/CodeFuse-DeepSeek-33B"
def load_model_tokenizer(model_path):
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
tokenizer.eos_token = "<|end▁of▁sentence|>"
tokenizer.pad_token = "<|end▁of▁sentence|>"
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
tokenizer.padding_side = "left"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto',torch_dtype=torch.bfloat16, trust_remote_code=True)
return model, tokenizer
HUMAN_ROLE_START_TAG = "<s>human\n"
BOT_ROLE_START_TAG = "<s>bot\n"
text_list = [f'{HUMAN_ROLE_START_TAG}Write a QuickSort program\n#Python\n{BOT_ROLE_START_TAG}']
model, tokenizer = load_model_tokenizer(model_dir)
inputs = tokenizer(text_list, return_tensors='pt', padding=True, add_special_tokens=False).to('cuda')
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
generation_config = GenerationConfig(
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
temperature=0.1,
max_new_tokens=512,
num_return_sequences=1,
num_beams=1,
top_p=0.95,
do_sample=False
)
outputs = model.generate(
inputs= input_ids,
attention_mask=attention_mask,
**generation_config.to_dict()
)
gen_text = tokenizer.batch_decode(outputs[:, input_ids.shape[1]:], skip_special_tokens=True)
print(gen_text[0])
```
<a id="chinese"></a>
## 模型简介
CodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。
<br>
## 新闻
🔥🔥🔥 2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。
🔥🔥🔥 2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)
🔥🔥🔥 2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:https://mp.weixin.qq.com/s/PCQPkvbvfxSPzsqjOILCDw
🔥🔥🔥 2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)
🔥🔥🔥 2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)
🔥🔥🔥 2023-09-26 [CodeFuse-CodeLlama-34B 4bits](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B-4bits/summary)量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。
🔥🔥🔥 2023-09-11 [CodeFuse-CodeLlama-34B](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B/summary)发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。
<br>
## 代码社区
**大本营**: 🏡 https://github.com/codefuse-ai (**请支持我们的项目Star🌟 + Fork🚀 + Watch👀**)
+ 如果您想自己微调该模型,可以访问 ✨[MFTCoder](https://github.com/codefuse-ai/MFTCoder)✨✨
+ 如果您想观看该模型示例,可以访问 ✨[CodeFuse Demo](https://github.com/codefuse-ai/codefuse)✨✨
<br>
## 评测表现
### 代码
| 模型 | HumanEval(pass@1) | 日期 |
|:----------------------------|:-----------------:|:-------:|
| **CodeFuse-CodeLlama-34B** | 74.4% | 2023.9 |
|**CodeFuse-CodeLlama-34B-4bits** | 73.8% | 2023.9 |
| WizardCoder-Python-34B-V1.0 | 73.2% | 2023.8 |
| GPT-4(zero-shot) | 67.0% | 2023.3 |
| PanGu-Coder2 15B | 61.6% | 2023.8 |
| CodeLlama-34b-Python | 53.7% | 2023.8 |
| CodeLlama-34b | 48.8% | 2023.8 |
| GPT-3.5(zero-shot) | 48.1% | 2022.11 |
| OctoCoder | 46.2% | 2023.8 |
| StarCoder-15B | 33.6% | 2023.5 |
| Qwen-14b | 32.3% | 2023.10 |
| **CodeFuse-StarCoder-15B** | 54.9% | 2023.9 |
| **CodeFuse-QWen-14B** | 48.78% | 2023.8 |
| **CodeFuse-CodeGeeX2-6B** | 45.12% | 2023.11 |
| **CodeFuse-DeepSeek-33B**. | **78.65%** | 2024.01 |
### NLP

## Requirements
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
<br>
## 推理数据格式
推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:
**带System提示的多轮会话格式:**
```python
"""
<s>system
System instruction
<s>human
Human 1st round input
<s>bot
Bot 1st round output<|end▁of▁sentence|>
<s>human
Human 2nd round input
<s>bot
Bot 2nd round output<|end▁of▁sentence|>
...
...
...
<s>human
Human nth round input
<s>bot
"""
```
**不带System提示的单轮会话格式:**
```python
"""
<s>human
User prompt...
<s>bot
"""
```
在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以"\<s\>bot\n"结尾,引导模型生成回答。
例如,推理HumanEval数据时使用的格式如下所示:
```python
<s>human
# language: Python
from typing import List
def separate_paren_groups(paren_string: str) -> List[str]:
""" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to
separate those group into separate strings and return the list of those.
Separate groups are balanced (each open brace is properly closed) and not nested within each other
Ignore any spaces in the input string.
>>> separate_paren_groups('( ) (( )) (( )( ))')
['()', '(())', '(()())']
"""
<s>bot
```
特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用"```# language: Python```")。
## 快速使用
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_dir = "codefuse-ai/CodeFuse-DeepSeek-33B"
def load_model_tokenizer(model_path):
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
tokenizer.eos_token = "<|end▁of▁sentence|>"
tokenizer.pad_token = "<|end▁of▁sentence|>"
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
tokenizer.padding_side = "left"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto',torch_dtype=torch.bfloat16, trust_remote_code=True)
return model, tokenizer
HUMAN_ROLE_START_TAG = "<s>human\n"
BOT_ROLE_START_TAG = "<s>bot\n"
text_list = [f'{HUMAN_ROLE_START_TAG}请写一个快排程序\n#Python\n{BOT_ROLE_START_TAG}']
model, tokenizer = load_model_tokenizer(model_dir)
inputs = tokenizer(text_list, return_tensors='pt', padding=True, add_special_tokens=False).to('cuda')
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
generation_config = GenerationConfig(
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
temperature=0.2,
max_new_tokens=512,
num_return_sequences=1,
num_beams=1,
top_p=0.95,
do_sample=False
)
outputs = model.generate(
inputs= input_ids,
attention_mask=attention_mask,
**generation_config.to_dict()
)
gen_text = tokenizer.batch_decode(outputs[:, input_ids.shape[1]:], skip_special_tokens=True)
print(gen_text[0])
```
| {"license": "other", "tasks": ["code-generation"]} | text-generation | LoneStriker/CodeFuse-DeepSeek-33B-6.0bpw-h6-exl2 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T19:28:04+00:00 | [] | [] | TAGS
#transformers #pytorch #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card for CodeFuse-DeepSeek-33B
====================================
!logo
[[中文]](#chinese) [[English]](#english)
Model Description
-----------------
CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-related tasks on the base model DeepSeek-Coder-33B.
News and Updates
----------------
2024-01-12 CodeFuse-DeepSeek-33B has been released, achieving a pass@1 (greedy decoding) score of 78.65% on HumanEval.
2024-01-12 CodeFuse-Mixtral-8x7B has been released, achieving a pass@1 (greedy decoding) score of 56.1% on HumanEval, which is a 15% increase compared to Mixtral-8x7b's 40%.
2023-11-10 CodeFuse-CodeGeeX2-6B has been released, achieving a pass@1 (greedy decoding) score of 45.12% on HumanEval, which is a 9.22% increase compared to CodeGeeX2 35.9%.
2023-10-20 CodeFuse-QWen-14B technical documentation has been released. For those interested, please refer to the CodeFuse article on our WeChat official account via the provided link.(URL
2023-10-16 CodeFuse-QWen-14B has been released, achieving a pass@1 (greedy decoding) score of 48.78% on HumanEval, which is a 16% increase compared to Qwen-14b's 32.3%.
2023-09-27 CodeFuse-StarCoder-15B has been released, achieving a pass@1 (greedy decoding) score of 54.9% on HumanEval, which is a 21% increase compared to StarCoder's 33.6%.
2023-09-26 We are pleased to announce the release of the 4-bit quantized version of CodeFuse-CodeLlama-34B. Despite the quantization process, the model still achieves a remarkable 73.8% accuracy (greedy decoding) on the HumanEval pass@1 metric.
2023-09-11 CodeFuse-CodeLlama-34B has achieved 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for openspurced LLMs at present.
Code Community
--------------
Homepage: URL (Please give us your support with a Star + Fork + Watch)
* If you wish to fine-tune the model yourself, you can visit MFTCoder
* If you wish to see a demo of the model, you can visit CodeFuse Demo
Performance
-----------
### Code
### NLP
!NLP Performance Radar
Requirements
------------
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
Inference String Format
-----------------------
The inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.
Here are examples of prompts used to request the model:
Multi-Round with System Prompt:
Single-Round without System Prompt:
In this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with "<s>bot" to ask the model generating answers.
For example, the format used to infer HumanEval is like the following:
Specifically, we also add the Programming Language Tag (e.g. "" for Python) used by CodeGeex models.
Quickstart
----------
模型简介
----
CodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。
新闻
--
2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。
2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)
2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:URL
2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)
2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)
2023-09-26 CodeFuse-CodeLlama-34B 4bits量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。
2023-09-11 CodeFuse-CodeLlama-34B发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。
代码社区
----
大本营: URL (请支持我们的项目Star + Fork + Watch)
* 如果您想自己微调该模型,可以访问 MFTCoder
* 如果您想观看该模型示例,可以访问 CodeFuse Demo
评测表现
----
### 代码
### NLP
!NLP Performance Radar
Requirements
------------
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
推理数据格式
------
推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:
带System提示的多轮会话格式:
不带System提示的单轮会话格式:
在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以"<s>bot\n"结尾,引导模型生成回答。
例如,推理HumanEval数据时使用的格式如下所示:
特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用"")。
快速使用
----
| [
"### Code",
"### NLP\n\n\n!NLP Performance Radar\n\n\n \n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\nInference String Format\n-----------------------\n\n\nThe inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.\nHere are examples of prompts used to request the model:\n\n\nMulti-Round with System Prompt:\n\n\nSingle-Round without System Prompt:\n\n\nIn this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with \"<s>bot\" to ask the model generating answers.\n\n\nFor example, the format used to infer HumanEval is like the following:\n\n\nSpecifically, we also add the Programming Language Tag (e.g. \"\" for Python) used by CodeGeex models.\n\n\nQuickstart\n----------\n\n\n\n模型简介\n----\n\n\nCodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。\n \n\n\n\n新闻\n--\n\n\n2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。\n\n\n2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)\n\n\n2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:URL\n\n\n2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)\n\n\n2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)\n\n\n2023-09-26 CodeFuse-CodeLlama-34B 4bits量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。\n\n\n2023-09-11 CodeFuse-CodeLlama-34B发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。\n\n\n \n\n代码社区\n----\n\n\n大本营: URL (请支持我们的项目Star + Fork + Watch)\n\n\n* 如果您想自己微调该模型,可以访问 MFTCoder\n* 如果您想观看该模型示例,可以访问 CodeFuse Demo\n\n\n \n\n评测表现\n----",
"### 代码",
"### NLP\n\n\n!NLP Performance Radar\n\n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\n推理数据格式\n------\n\n\n推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:\n\n\n带System提示的多轮会话格式:\n\n\n不带System提示的单轮会话格式:\n\n\n在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以\"<s>bot\\n\"结尾,引导模型生成回答。\n\n\n例如,推理HumanEval数据时使用的格式如下所示:\n\n\n特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用\"\")。\n\n\n快速使用\n----"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Code",
"### NLP\n\n\n!NLP Performance Radar\n\n\n \n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\nInference String Format\n-----------------------\n\n\nThe inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.\nHere are examples of prompts used to request the model:\n\n\nMulti-Round with System Prompt:\n\n\nSingle-Round without System Prompt:\n\n\nIn this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with \"<s>bot\" to ask the model generating answers.\n\n\nFor example, the format used to infer HumanEval is like the following:\n\n\nSpecifically, we also add the Programming Language Tag (e.g. \"\" for Python) used by CodeGeex models.\n\n\nQuickstart\n----------\n\n\n\n模型简介\n----\n\n\nCodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。\n \n\n\n\n新闻\n--\n\n\n2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。\n\n\n2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)\n\n\n2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:URL\n\n\n2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)\n\n\n2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)\n\n\n2023-09-26 CodeFuse-CodeLlama-34B 4bits量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。\n\n\n2023-09-11 CodeFuse-CodeLlama-34B发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。\n\n\n \n\n代码社区\n----\n\n\n大本营: URL (请支持我们的项目Star + Fork + Watch)\n\n\n* 如果您想自己微调该模型,可以访问 MFTCoder\n* 如果您想观看该模型示例,可以访问 CodeFuse Demo\n\n\n \n\n评测表现\n----",
"### 代码",
"### NLP\n\n\n!NLP Performance Radar\n\n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\n推理数据格式\n------\n\n\n推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:\n\n\n带System提示的多轮会话格式:\n\n\n不带System提示的单轮会话格式:\n\n\n在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以\"<s>bot\\n\"结尾,引导模型生成回答。\n\n\n例如,推理HumanEval数据时使用的格式如下所示:\n\n\n特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用\"\")。\n\n\n快速使用\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. -->
# 600_STEPS_1e7_03beta_
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.
It achieves the following results on the evaluation set:
- Loss: 0.6895
- Rewards/chosen: 0.0013
- Rewards/rejected: -0.0065
- Rewards/accuracies: 0.4857
- Rewards/margins: 0.0079
- Logps/rejected: -15.1611
- Logps/chosen: -14.1124
- Logits/rejected: -0.0235
- Logits/chosen: -0.0235
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-07
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 600
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6928 | 0.1 | 50 | 0.6933 | 0.0002 | 0.0002 | 0.4220 | -0.0000 | -15.1385 | -14.1161 | -0.0215 | -0.0215 |
| 0.6921 | 0.2 | 100 | 0.6942 | -0.0040 | -0.0023 | 0.4088 | -0.0018 | -15.1468 | -14.1303 | -0.0212 | -0.0212 |
| 0.6935 | 0.29 | 150 | 0.6928 | -0.0020 | -0.0029 | 0.4637 | 0.0010 | -15.1490 | -14.1234 | -0.0227 | -0.0226 |
| 0.6897 | 0.39 | 200 | 0.6912 | 0.0008 | -0.0034 | 0.4659 | 0.0042 | -15.1506 | -14.1141 | -0.0222 | -0.0222 |
| 0.6884 | 0.49 | 250 | 0.6907 | -0.0012 | -0.0065 | 0.4549 | 0.0053 | -15.1610 | -14.1209 | -0.0221 | -0.0221 |
| 0.6879 | 0.59 | 300 | 0.6899 | -0.0011 | -0.0081 | 0.4571 | 0.0070 | -15.1662 | -14.1204 | -0.0226 | -0.0226 |
| 0.689 | 0.68 | 350 | 0.6901 | -0.0005 | -0.0072 | 0.4637 | 0.0067 | -15.1633 | -14.1185 | -0.0229 | -0.0229 |
| 0.6905 | 0.78 | 400 | 0.6898 | -0.0007 | -0.0080 | 0.4593 | 0.0073 | -15.1659 | -14.1191 | -0.0228 | -0.0227 |
| 0.6867 | 0.88 | 450 | 0.6898 | 0.0003 | -0.0070 | 0.4923 | 0.0073 | -15.1627 | -14.1159 | -0.0225 | -0.0225 |
| 0.6902 | 0.98 | 500 | 0.6895 | -0.0007 | -0.0085 | 0.4857 | 0.0078 | -15.1678 | -14.1193 | -0.0226 | -0.0226 |
| 0.6903 | 1.07 | 550 | 0.6903 | -0.0003 | -0.0064 | 0.4615 | 0.0061 | -15.1606 | -14.1178 | -0.0236 | -0.0236 |
| 0.6901 | 1.17 | 600 | 0.6895 | 0.0013 | -0.0065 | 0.4857 | 0.0079 | -15.1611 | -14.1124 | -0.0235 | -0.0235 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0+cu117
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-hf", "model-index": [{"name": "600_STEPS_1e7_03beta_", "results": []}]} | text-generation | tsavage68/600_STEPS_1e7_03beta_DPO_zeroshot | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T19:29:22+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| 600\_STEPS\_1e7\_03beta\_
=========================
This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6895
* Rewards/chosen: 0.0013
* Rewards/rejected: -0.0065
* Rewards/accuracies: 0.4857
* Rewards/margins: 0.0079
* Logps/rejected: -15.1611
* Logps/chosen: -14.1124
* Logits/rejected: -0.0235
* Logits/chosen: -0.0235
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-07
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 600
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.0+cu117
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
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"### Training results",
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | samanjoy2/Mistral-7B-Instruct-v0.2_ML-ESG-3_eng_fr | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T19:35:38+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## 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
### Training Data
<!-- This should link to a Data 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 Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| {"library_name": "peft", "base_model": "meta-llama/Llama-2-13b-chat-hf"} | null | bmehrba/Llama-2-13b-chat-hf-fine-tuned-adapters_Gpt4_t1_Llama13b_Seed105 | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"region:us"
] | 2024-02-09T19:36:46+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-13b-chat-hf #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- 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
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
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null | null | transformers |
# OmniBeagleSquaredMBX-v3-7B-v2
OmniBeagleSquaredMBX-v3-7B-v2 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [paulml/OmniBeagleMBX-v3-7B](https://huggingface.co/paulml/OmniBeagleMBX-v3-7B)
* [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: paulml/OmniBeagleMBX-v3-7B
layer_range: [0, 32]
- model: flemmingmiguel/MBX-7B-v3
layer_range: [0, 32]
merge_method: slerp
base_model: paulml/OmniBeagleMBX-v3-7B
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
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "paulml/OmniBeagleSquaredMBX-v3-7B-v2"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "cc-by-nc-4.0", "tags": ["merge", "mergekit", "lazymergekit", "paulml/OmniBeagleMBX-v3-7B", "flemmingmiguel/MBX-7B-v3"], "base_model": ["paulml/OmniBeagleMBX-v3-7B", "flemmingmiguel/MBX-7B-v3"]} | text-generation | paulml/OmniBeagleSquaredMBX-v3-7B-v2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"paulml/OmniBeagleMBX-v3-7B",
"flemmingmiguel/MBX-7B-v3",
"base_model:paulml/OmniBeagleMBX-v3-7B",
"base_model:flemmingmiguel/MBX-7B-v3",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2024-02-09T19:36:55+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #paulml/OmniBeagleMBX-v3-7B #flemmingmiguel/MBX-7B-v3 #base_model-paulml/OmniBeagleMBX-v3-7B #base_model-flemmingmiguel/MBX-7B-v3 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# OmniBeagleSquaredMBX-v3-7B-v2
OmniBeagleSquaredMBX-v3-7B-v2 is a merge of the following models using LazyMergekit:
* paulml/OmniBeagleMBX-v3-7B
* flemmingmiguel/MBX-7B-v3
## Configuration
## Usage
| [
"# OmniBeagleSquaredMBX-v3-7B-v2\n\nOmniBeagleSquaredMBX-v3-7B-v2 is a merge of the following models using LazyMergekit:\n* paulml/OmniBeagleMBX-v3-7B\n* flemmingmiguel/MBX-7B-v3",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #paulml/OmniBeagleMBX-v3-7B #flemmingmiguel/MBX-7B-v3 #base_model-paulml/OmniBeagleMBX-v3-7B #base_model-flemmingmiguel/MBX-7B-v3 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# OmniBeagleSquaredMBX-v3-7B-v2\n\nOmniBeagleSquaredMBX-v3-7B-v2 is a merge of the following models using LazyMergekit:\n* paulml/OmniBeagleMBX-v3-7B\n* flemmingmiguel/MBX-7B-v3",
"## Configuration",
"## Usage"
] | [
141,
76,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #paulml/OmniBeagleMBX-v3-7B #flemmingmiguel/MBX-7B-v3 #base_model-paulml/OmniBeagleMBX-v3-7B #base_model-flemmingmiguel/MBX-7B-v3 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# OmniBeagleSquaredMBX-v3-7B-v2\n\nOmniBeagleSquaredMBX-v3-7B-v2 is a merge of the following models using LazyMergekit:\n* paulml/OmniBeagleMBX-v3-7B\n* flemmingmiguel/MBX-7B-v3## Configuration## Usage"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### 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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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## 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
### Training Data
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[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. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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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. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| {"library_name": "peft", "base_model": "meta-llama/Llama-2-13b-chat-hf"} | null | bmehrba/Llama-2-13b-chat-hf-fine-tuned_Gpt4_t1_Llama13b_Seed105 | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"region:us"
] | 2024-02-09T19:37:06+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-13b-chat-hf #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- 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
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \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",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
] | [
"TAGS\n#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-13b-chat-hf #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \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",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
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"passage: TAGS\n#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-13b-chat-hf #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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] |
null | null | transformers | # Model Card for CodeFuse-DeepSeek-33B

[[中文]](#chinese) [[English]](#english)
<a id="english"></a>
## Model Description
CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-related tasks on the base model DeepSeek-Coder-33B.
<br>
## News and Updates
🔥🔥🔥 2024-01-12 CodeFuse-DeepSeek-33B has been released, achieving a pass@1 (greedy decoding) score of 78.65% on HumanEval.
🔥🔥🔥 2024-01-12 CodeFuse-Mixtral-8x7B has been released, achieving a pass@1 (greedy decoding) score of 56.1% on HumanEval, which is a 15% increase compared to Mixtral-8x7b's 40%.
🔥🔥 2023-11-10 CodeFuse-CodeGeeX2-6B has been released, achieving a pass@1 (greedy decoding) score of 45.12% on HumanEval, which is a 9.22% increase compared to CodeGeeX2 35.9%.
🔥🔥 2023-10-20 CodeFuse-QWen-14B technical documentation has been released. For those interested, please refer to the CodeFuse article on our WeChat official account via the provided link.(https://mp.weixin.qq.com/s/PCQPkvbvfxSPzsqjOILCDw)
🔥🔥 2023-10-16 CodeFuse-QWen-14B has been released, achieving a pass@1 (greedy decoding) score of 48.78% on HumanEval, which is a 16% increase compared to Qwen-14b's 32.3%.
🔥🔥 2023-09-27 CodeFuse-StarCoder-15B has been released, achieving a pass@1 (greedy decoding) score of 54.9% on HumanEval, which is a 21% increase compared to StarCoder's 33.6%.
🔥🔥 2023-09-26 We are pleased to announce the release of the 4-bit quantized version of CodeFuse-CodeLlama-34B. Despite the quantization process, the model still achieves a remarkable 73.8% accuracy (greedy decoding) on the HumanEval pass@1 metric.
🔥🔥 2023-09-11 CodeFuse-CodeLlama-34B has achieved 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for openspurced LLMs at present.
<br>
## Code Community
**Homepage**: 🏡 https://github.com/codefuse-ai (**Please give us your support with a Star🌟 + Fork🚀 + Watch👀**)
+ If you wish to fine-tune the model yourself, you can visit ✨[MFTCoder](https://github.com/codefuse-ai/MFTCoder)✨✨
+ If you wish to see a demo of the model, you can visit ✨[CodeFuse Demo](https://github.com/codefuse-ai/codefuse)✨✨
<br>
## Performance
### Code
| Model | HumanEval(pass@1) | Date |
|:----------------------------|:-----------------:|:-------:|
| **CodeFuse-DeepSeek-33B** | **78.65%** | 2024.01 |
| **CodeFuse-Mixtral-8x7B** | **56.10%** | 2024.01 |
| **CodeFuse-CodeLlama-34B** | 74.4% | 2023.9 |
|**CodeFuse-CodeLlama-34B-4bits** | 73.8% | 2023.9 |
| **CodeFuse-StarCoder-15B** | 54.9% | 2023.9 |
| **CodeFuse-QWen-14B** | 48.78% | 2023.10 |
| **CodeFuse-CodeGeeX2-6B** | 45.12% | 2023.11 |
| WizardCoder-Python-34B-V1.0 | 73.2% | 2023.8 |
| GPT-4(zero-shot) | 67.0% | 2023.3 |
| PanGu-Coder2 15B | 61.6% | 2023.8 |
| CodeLlama-34b-Python | 53.7% | 2023.8 |
| CodeLlama-34b | 48.8% | 2023.8 |
| GPT-3.5(zero-shot) | 48.1% | 2022.11 |
| OctoCoder | 46.2% | 2023.8 |
| StarCoder-15B | 33.6% | 2023.5 |
| Qwen-14b | 32.3% | 2023.10 |
### NLP

<br>
## Requirements
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
<br>
## Inference String Format
The inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.
Here are examples of prompts used to request the model:
**Multi-Round with System Prompt:**
```python
"""
<s>system
System instruction
<s>human
Human 1st round input
<s>bot
Bot 1st round output<|end▁of▁sentence|>
<s>human
Human 2nd round input
<s>bot
Bot 2nd round output<|end▁of▁sentence|>
...
...
...
<s>human
Human nth round input
<s>bot
"""
```
**Single-Round without System Prompt:**
```python
"""
<s>human
User prompt...
<s>bot
"""
```
In this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with "\<s\>bot" to ask the model generating answers.
For example, the format used to infer HumanEval is like the following:
```
<s>human
# language: Python
from typing import List
def separate_paren_groups(paren_string: str) -> List[str]:
""" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to
separate those group into separate strings and return the list of those.
Separate groups are balanced (each open brace is properly closed) and not nested within each other
Ignore any spaces in the input string.
>>> separate_paren_groups('( ) (( )) (( )( ))')
['()', '(())', '(()())']
"""
<s>bot
```
Specifically, we also add the Programming Language Tag (e.g. "```# language: Python```" for Python) used by CodeGeex models.
## Quickstart
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_dir = "codefuse-ai/CodeFuse-DeepSeek-33B"
def load_model_tokenizer(model_path):
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
tokenizer.eos_token = "<|end▁of▁sentence|>"
tokenizer.pad_token = "<|end▁of▁sentence|>"
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
tokenizer.padding_side = "left"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto',torch_dtype=torch.bfloat16, trust_remote_code=True)
return model, tokenizer
HUMAN_ROLE_START_TAG = "<s>human\n"
BOT_ROLE_START_TAG = "<s>bot\n"
text_list = [f'{HUMAN_ROLE_START_TAG}Write a QuickSort program\n#Python\n{BOT_ROLE_START_TAG}']
model, tokenizer = load_model_tokenizer(model_dir)
inputs = tokenizer(text_list, return_tensors='pt', padding=True, add_special_tokens=False).to('cuda')
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
generation_config = GenerationConfig(
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
temperature=0.1,
max_new_tokens=512,
num_return_sequences=1,
num_beams=1,
top_p=0.95,
do_sample=False
)
outputs = model.generate(
inputs= input_ids,
attention_mask=attention_mask,
**generation_config.to_dict()
)
gen_text = tokenizer.batch_decode(outputs[:, input_ids.shape[1]:], skip_special_tokens=True)
print(gen_text[0])
```
<a id="chinese"></a>
## 模型简介
CodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。
<br>
## 新闻
🔥🔥🔥 2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。
🔥🔥🔥 2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)
🔥🔥🔥 2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:https://mp.weixin.qq.com/s/PCQPkvbvfxSPzsqjOILCDw
🔥🔥🔥 2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)
🔥🔥🔥 2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)
🔥🔥🔥 2023-09-26 [CodeFuse-CodeLlama-34B 4bits](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B-4bits/summary)量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。
🔥🔥🔥 2023-09-11 [CodeFuse-CodeLlama-34B](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B/summary)发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。
<br>
## 代码社区
**大本营**: 🏡 https://github.com/codefuse-ai (**请支持我们的项目Star🌟 + Fork🚀 + Watch👀**)
+ 如果您想自己微调该模型,可以访问 ✨[MFTCoder](https://github.com/codefuse-ai/MFTCoder)✨✨
+ 如果您想观看该模型示例,可以访问 ✨[CodeFuse Demo](https://github.com/codefuse-ai/codefuse)✨✨
<br>
## 评测表现
### 代码
| 模型 | HumanEval(pass@1) | 日期 |
|:----------------------------|:-----------------:|:-------:|
| **CodeFuse-CodeLlama-34B** | 74.4% | 2023.9 |
|**CodeFuse-CodeLlama-34B-4bits** | 73.8% | 2023.9 |
| WizardCoder-Python-34B-V1.0 | 73.2% | 2023.8 |
| GPT-4(zero-shot) | 67.0% | 2023.3 |
| PanGu-Coder2 15B | 61.6% | 2023.8 |
| CodeLlama-34b-Python | 53.7% | 2023.8 |
| CodeLlama-34b | 48.8% | 2023.8 |
| GPT-3.5(zero-shot) | 48.1% | 2022.11 |
| OctoCoder | 46.2% | 2023.8 |
| StarCoder-15B | 33.6% | 2023.5 |
| Qwen-14b | 32.3% | 2023.10 |
| **CodeFuse-StarCoder-15B** | 54.9% | 2023.9 |
| **CodeFuse-QWen-14B** | 48.78% | 2023.8 |
| **CodeFuse-CodeGeeX2-6B** | 45.12% | 2023.11 |
| **CodeFuse-DeepSeek-33B**. | **78.65%** | 2024.01 |
### NLP

## Requirements
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
<br>
## 推理数据格式
推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:
**带System提示的多轮会话格式:**
```python
"""
<s>system
System instruction
<s>human
Human 1st round input
<s>bot
Bot 1st round output<|end▁of▁sentence|>
<s>human
Human 2nd round input
<s>bot
Bot 2nd round output<|end▁of▁sentence|>
...
...
...
<s>human
Human nth round input
<s>bot
"""
```
**不带System提示的单轮会话格式:**
```python
"""
<s>human
User prompt...
<s>bot
"""
```
在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以"\<s\>bot\n"结尾,引导模型生成回答。
例如,推理HumanEval数据时使用的格式如下所示:
```python
<s>human
# language: Python
from typing import List
def separate_paren_groups(paren_string: str) -> List[str]:
""" Input to this function is a string containing multiple groups of nested parentheses. Your goal is to
separate those group into separate strings and return the list of those.
Separate groups are balanced (each open brace is properly closed) and not nested within each other
Ignore any spaces in the input string.
>>> separate_paren_groups('( ) (( )) (( )( ))')
['()', '(())', '(()())']
"""
<s>bot
```
特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用"```# language: Python```")。
## 快速使用
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_dir = "codefuse-ai/CodeFuse-DeepSeek-33B"
def load_model_tokenizer(model_path):
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
tokenizer.eos_token = "<|end▁of▁sentence|>"
tokenizer.pad_token = "<|end▁of▁sentence|>"
tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
tokenizer.padding_side = "left"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto',torch_dtype=torch.bfloat16, trust_remote_code=True)
return model, tokenizer
HUMAN_ROLE_START_TAG = "<s>human\n"
BOT_ROLE_START_TAG = "<s>bot\n"
text_list = [f'{HUMAN_ROLE_START_TAG}请写一个快排程序\n#Python\n{BOT_ROLE_START_TAG}']
model, tokenizer = load_model_tokenizer(model_dir)
inputs = tokenizer(text_list, return_tensors='pt', padding=True, add_special_tokens=False).to('cuda')
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
generation_config = GenerationConfig(
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
temperature=0.2,
max_new_tokens=512,
num_return_sequences=1,
num_beams=1,
top_p=0.95,
do_sample=False
)
outputs = model.generate(
inputs= input_ids,
attention_mask=attention_mask,
**generation_config.to_dict()
)
gen_text = tokenizer.batch_decode(outputs[:, input_ids.shape[1]:], skip_special_tokens=True)
print(gen_text[0])
```
| {"license": "other", "tasks": ["code-generation"]} | text-generation | LoneStriker/CodeFuse-DeepSeek-33B-8.0bpw-h8-exl2 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T19:39:18+00:00 | [] | [] | TAGS
#transformers #pytorch #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Card for CodeFuse-DeepSeek-33B
====================================
!logo
[[中文]](#chinese) [[English]](#english)
Model Description
-----------------
CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-related tasks on the base model DeepSeek-Coder-33B.
News and Updates
----------------
2024-01-12 CodeFuse-DeepSeek-33B has been released, achieving a pass@1 (greedy decoding) score of 78.65% on HumanEval.
2024-01-12 CodeFuse-Mixtral-8x7B has been released, achieving a pass@1 (greedy decoding) score of 56.1% on HumanEval, which is a 15% increase compared to Mixtral-8x7b's 40%.
2023-11-10 CodeFuse-CodeGeeX2-6B has been released, achieving a pass@1 (greedy decoding) score of 45.12% on HumanEval, which is a 9.22% increase compared to CodeGeeX2 35.9%.
2023-10-20 CodeFuse-QWen-14B technical documentation has been released. For those interested, please refer to the CodeFuse article on our WeChat official account via the provided link.(URL
2023-10-16 CodeFuse-QWen-14B has been released, achieving a pass@1 (greedy decoding) score of 48.78% on HumanEval, which is a 16% increase compared to Qwen-14b's 32.3%.
2023-09-27 CodeFuse-StarCoder-15B has been released, achieving a pass@1 (greedy decoding) score of 54.9% on HumanEval, which is a 21% increase compared to StarCoder's 33.6%.
2023-09-26 We are pleased to announce the release of the 4-bit quantized version of CodeFuse-CodeLlama-34B. Despite the quantization process, the model still achieves a remarkable 73.8% accuracy (greedy decoding) on the HumanEval pass@1 metric.
2023-09-11 CodeFuse-CodeLlama-34B has achieved 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for openspurced LLMs at present.
Code Community
--------------
Homepage: URL (Please give us your support with a Star + Fork + Watch)
* If you wish to fine-tune the model yourself, you can visit MFTCoder
* If you wish to see a demo of the model, you can visit CodeFuse Demo
Performance
-----------
### Code
### NLP
!NLP Performance Radar
Requirements
------------
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
Inference String Format
-----------------------
The inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.
Here are examples of prompts used to request the model:
Multi-Round with System Prompt:
Single-Round without System Prompt:
In this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with "<s>bot" to ask the model generating answers.
For example, the format used to infer HumanEval is like the following:
Specifically, we also add the Programming Language Tag (e.g. "" for Python) used by CodeGeex models.
Quickstart
----------
模型简介
----
CodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。
新闻
--
2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。
2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)
2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:URL
2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)
2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)
2023-09-26 CodeFuse-CodeLlama-34B 4bits量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。
2023-09-11 CodeFuse-CodeLlama-34B发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。
代码社区
----
大本营: URL (请支持我们的项目Star + Fork + Watch)
* 如果您想自己微调该模型,可以访问 MFTCoder
* 如果您想观看该模型示例,可以访问 CodeFuse Demo
评测表现
----
### 代码
### NLP
!NLP Performance Radar
Requirements
------------
* python>=3.8
* pytorch>=2.0.0
* transformers>=4.33.2
* Sentencepiece
* CUDA 11.4
推理数据格式
------
推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:
带System提示的多轮会话格式:
不带System提示的单轮会话格式:
在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以"<s>bot\n"结尾,引导模型生成回答。
例如,推理HumanEval数据时使用的格式如下所示:
特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用"")。
快速使用
----
| [
"### Code",
"### NLP\n\n\n!NLP Performance Radar\n\n\n \n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\nInference String Format\n-----------------------\n\n\nThe inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.\nHere are examples of prompts used to request the model:\n\n\nMulti-Round with System Prompt:\n\n\nSingle-Round without System Prompt:\n\n\nIn this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with \"<s>bot\" to ask the model generating answers.\n\n\nFor example, the format used to infer HumanEval is like the following:\n\n\nSpecifically, we also add the Programming Language Tag (e.g. \"\" for Python) used by CodeGeex models.\n\n\nQuickstart\n----------\n\n\n\n模型简介\n----\n\n\nCodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。\n \n\n\n\n新闻\n--\n\n\n2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。\n\n\n2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)\n\n\n2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:URL\n\n\n2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)\n\n\n2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)\n\n\n2023-09-26 CodeFuse-CodeLlama-34B 4bits量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。\n\n\n2023-09-11 CodeFuse-CodeLlama-34B发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。\n\n\n \n\n代码社区\n----\n\n\n大本营: URL (请支持我们的项目Star + Fork + Watch)\n\n\n* 如果您想自己微调该模型,可以访问 MFTCoder\n* 如果您想观看该模型示例,可以访问 CodeFuse Demo\n\n\n \n\n评测表现\n----",
"### 代码",
"### NLP\n\n\n!NLP Performance Radar\n\n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\n推理数据格式\n------\n\n\n推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:\n\n\n带System提示的多轮会话格式:\n\n\n不带System提示的单轮会话格式:\n\n\n在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以\"<s>bot\\n\"结尾,引导模型生成回答。\n\n\n例如,推理HumanEval数据时使用的格式如下所示:\n\n\n特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用\"\")。\n\n\n快速使用\n----"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Code",
"### NLP\n\n\n!NLP Performance Radar\n\n\n \n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\nInference String Format\n-----------------------\n\n\nThe inference string is a concatenated string formed by combining conversation data(system, human and bot contents) in the training data format. It is used as input during the inference process.\nHere are examples of prompts used to request the model:\n\n\nMulti-Round with System Prompt:\n\n\nSingle-Round without System Prompt:\n\n\nIn this format, the system section is optional and the conversation can be either single-turn or multi-turn. When applying inference, you always make your input string end with \"<s>bot\" to ask the model generating answers.\n\n\nFor example, the format used to infer HumanEval is like the following:\n\n\nSpecifically, we also add the Programming Language Tag (e.g. \"\" for Python) used by CodeGeex models.\n\n\nQuickstart\n----------\n\n\n\n模型简介\n----\n\n\nCodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进行多代码任务微调而得到的代码大模型。\n \n\n\n\n新闻\n--\n\n\n2024-01-12 CodeFuse-DeepSeek-33B模型发布,模型在HumanEval pass@1指标为78.65% (贪婪解码)。\n\n\n2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型,在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力(HumanEval)\n\n\n2023-10-20 公布了CodeFuse-QWen-14B技术文档,感兴趣详见微信公众号CodeFuse文章:URL\n\n\n2023-10-16开源了CodeFuse-QWen-14B模型,在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力(HumanEval)\n\n\n2023-09-27开源了CodeFuse-StarCoder-15B模型,在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力(HumanEval)\n\n\n2023-09-26 CodeFuse-CodeLlama-34B 4bits量化版本发布,量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。\n\n\n2023-09-11 CodeFuse-CodeLlama-34B发布,HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。\n\n\n \n\n代码社区\n----\n\n\n大本营: URL (请支持我们的项目Star + Fork + Watch)\n\n\n* 如果您想自己微调该模型,可以访问 MFTCoder\n* 如果您想观看该模型示例,可以访问 CodeFuse Demo\n\n\n \n\n评测表现\n----",
"### 代码",
"### NLP\n\n\n!NLP Performance Radar\n\n\nRequirements\n------------\n\n\n* python>=3.8\n* pytorch>=2.0.0\n* transformers>=4.33.2\n* Sentencepiece\n* CUDA 11.4\n\n\n推理数据格式\n------\n\n\n推理数据为模型在训练数据格式下拼接的字符串形式,它也是推理时输入prompt拼接的方式. 下面分别是带系统提示的多轮会话格式和不带系统提示的单轮会话格式:\n\n\n带System提示的多轮会话格式:\n\n\n不带System提示的单轮会话格式:\n\n\n在这个格式中,System提示是可选的(按需设定),支持单轮会话也支持多轮会话。推理时,请确保拼接的prompt字符串以\"<s>bot\\n\"结尾,引导模型生成回答。\n\n\n例如,推理HumanEval数据时使用的格式如下所示:\n\n\n特别地,我们也使用了CodeGeeX系列模型采用的编程语言区分标签(例如,对于Python语言,我们会使用\"\")。\n\n\n快速使用\n----"
] | [
55,
3,
656,
4,
244
] | [
"passage: TAGS\n#transformers #pytorch #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Code"
] | [
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# result
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "result", "results": []}]} | question-answering | elifnurd/distilbert-base-uncased-data-squad-v2 | [
"transformers",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-09T19:40:34+00:00 | [] | [] | TAGS
#transformers #distilbert #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us
|
# result
This model is a fine-tuned version of distilbert-base-uncased 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| [
"# result\n\nThis model is a fine-tuned version of distilbert-base-uncased 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: 3e-05\n- train_batch_size: 12\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: 2.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #distilbert #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n",
"# result\n\nThis model is a fine-tuned version of distilbert-base-uncased 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: 3e-05\n- train_batch_size: 12\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: 2.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
] | [
56,
29,
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90,
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38
] | [
"passage: TAGS\n#transformers #distilbert #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n# result\n\nThis model is a fine-tuned version of distilbert-base-uncased 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: 3e-05\n- train_batch_size: 12\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: 2.0### Training results### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0"
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] |
null | null | transformers | ## **BigMaid-20B-v1.0**
[exllamav2](https://github.com/turboderp/exllamav2) quant for [TeeZee/BigMaid-20B-v1.0](https://huggingface.co/TeeZee/BigMaid-20B-v1.0)
Runs smoothly on single 3090 in webui with context length set to 4096, ExLlamav2_HF loader
and cache_8bit=True
All comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:
<a href="https://www.buymeacoffee.com/TeeZee" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a> | {"license": "apache-2.0", "tags": ["merge", "not-for-all-audiences"]} | text-generation | TeeZee/BigMaid-20B-v1.0-bpw8-h8-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"not-for-all-audiences",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T19:40:40+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #merge #not-for-all-audiences #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## BigMaid-20B-v1.0
exllamav2 quant for TeeZee/BigMaid-20B-v1.0
Runs smoothly on single 3090 in webui with context length set to 4096, ExLlamav2_HF loader
and cache_8bit=True
All comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:
<a href="URL target="_blank"><img src="URL alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a> | [
"## BigMaid-20B-v1.0\n\nexllamav2 quant for TeeZee/BigMaid-20B-v1.0\n\nRuns smoothly on single 3090 in webui with context length set to 4096, ExLlamav2_HF loader\nand cache_8bit=True\n\nAll comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 60px !important;width: 217px !important;\" ></a>"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #merge #not-for-all-audiences #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## BigMaid-20B-v1.0\n\nexllamav2 quant for TeeZee/BigMaid-20B-v1.0\n\nRuns smoothly on single 3090 in webui with context length set to 4096, ExLlamav2_HF loader\nand cache_8bit=True\n\nAll comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 60px !important;width: 217px !important;\" ></a>"
] | [
67,
139
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #not-for-all-audiences #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## BigMaid-20B-v1.0\n\nexllamav2 quant for TeeZee/BigMaid-20B-v1.0\n\nRuns smoothly on single 3090 in webui with context length set to 4096, ExLlamav2_HF loader\nand cache_8bit=True\n\nAll comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 60px !important;width: 217px !important;\" ></a>"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | eediker/Llama-2-7b-chat-therapist | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T19:41:24+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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## Uses
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### 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:
- Hours used:
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### Compute Infrastructure
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#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
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## Model Card Contact
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #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 |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | mjschock/mamba-370m | [
"transformers",
"safetensors",
"mamba",
"text-generation",
"custom_code",
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"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #mamba #text-generation #custom_code #arxiv-1910.09700 #autotrain_compatible #region-us
|
# 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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- Model type:
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- License:
- Finetuned from model [optional]:
### Model Sources [optional]
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- 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:
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## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
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BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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null | null | peft |
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#### 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. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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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. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "NousResearch/Llama-2-7b-chat-hf"} | null | vectscal/llama2-shakespeare-sh | [
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## 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]
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### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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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 | transformers |
# This model is now live (We'll always be serving the newest model on our web app)!
Access at: https://www.whiterabbitneo.com/
# Our Discord Server
Join us at: https://discord.gg/8Ynkrcbk92 (Updated on Dec 29th. Now permanent link to join)
# DeepSeek Coder Licence + WhiteRabbitNeo Extended Version
# Licence: Usage Restrictions
```
You agree not to use the Model or Derivatives of the Model:
- In any way that violates any applicable national or international law or regulation or infringes upon the lawful rights and interests of any third party;
- For military use in any way;
- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
- To generate or disseminate inappropriate content subject to applicable regulatory requirements;
- To generate or disseminate personal identifiable information without due authorization or for unreasonable use;
- To defame, disparage or otherwise harass others;
- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories.
```
# Topics Covered:
```
- Open Ports: Identifying open ports is crucial as they can be entry points for attackers. Common ports to check include HTTP (80, 443), FTP (21), SSH (22), and SMB (445).
- Outdated Software or Services: Systems running outdated software or services are often vulnerable to exploits. This includes web servers, database servers, and any third-party software.
- Default Credentials: Many systems and services are installed with default usernames and passwords, which are well-known and can be easily exploited.
- Misconfigurations: Incorrectly configured services, permissions, and security settings can introduce vulnerabilities.
- Injection Flaws: SQL injection, command injection, and cross-site scripting (XSS) are common issues in web applications.
- Unencrypted Services: Services that do not use encryption (like HTTP instead of HTTPS) can expose sensitive data.
- Known Software Vulnerabilities: Checking for known vulnerabilities in software using databases like the National Vulnerability Database (NVD) or tools like Nessus or OpenVAS.
- Cross-Site Request Forgery (CSRF): This is where unauthorized commands are transmitted from a user that the web application trusts.
- Insecure Direct Object References: This occurs when an application provides direct access to objects based on user-supplied input.
- Security Misconfigurations in Web Servers/Applications: This includes issues like insecure HTTP headers or verbose error messages that reveal too much information.
- Broken Authentication and Session Management: This can allow attackers to compromise passwords, keys, or session tokens, or to exploit other implementation flaws to assume other users' identities.
- Sensitive Data Exposure: Includes vulnerabilities that expose sensitive data, such as credit card numbers, health records, or personal information.
- API Vulnerabilities: In modern web applications, APIs are often used and can have vulnerabilities like insecure endpoints or data leakage.
- Denial of Service (DoS) Vulnerabilities: Identifying services that are vulnerable to DoS attacks, which can make the resource unavailable to legitimate users.
- Buffer Overflows: Common in older software, these vulnerabilities can allow an attacker to crash the system or execute arbitrary code.
```
# Terms of Use
By accessing and using this Artificial Intelligence (AI) model, you, the user, acknowledge and agree that you are solely responsible for your use of the model and its outcomes. You hereby agree to indemnify, defend, and hold harmless the creators, developers, and any affiliated persons or entities of this AI model from and against any and all claims, liabilities, damages, losses, costs, expenses, fees (including reasonable attorneys' fees and court costs) that may arise, directly or indirectly, from your use of the AI model.
This AI model is provided "as is" and "as available" without any warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and non-infringement. The creators make no warranty that the AI model will meet your requirements or be available on an uninterrupted, secure, or error-free basis.
Your use of the AI model is at your own risk and discretion, and you will be solely responsible for any damage to computer systems or loss of data that results from the use of the AI model.
This disclaimer constitutes part of the agreement between you and the creators of the AI model regarding your use of the model, superseding any prior agreements between you and the creators regarding your use of this AI model.
# WhiteRabbitNeo
<br>

<br>
WhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity.
Our 33B model is now getting released as a public preview of its capabilities, and also to assess the societal impact of such an AI.
```python
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "whiterabbitneo/WhiteRabbitNeo-33B-v-1"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_4bit=False,
load_in_8bit=True,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.5,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: You are an AI that code. Answer with code."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
# print(conversation)
json_data = {"prompt": user_input, "answer": answer}
# print(json_data)
# with open(output_file_path, "a") as output_file:
# output_file.write(json.dumps(json_data) + "\n")
```
| {"license": "other", "license_name": "deepseek", "license_link": "https://huggingface.co/deepseek-ai/deepseek-coder-33b-base/blob/main/LICENSE"} | text-generation | WhiteRabbitNeo/WhiteRabbitNeo-33B-v1.5 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"has_space"
] | 2024-02-09T19:51:38+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #has_space
|
# This model is now live (We'll always be serving the newest model on our web app)!
Access at: URL
# Our Discord Server
Join us at: URL (Updated on Dec 29th. Now permanent link to join)
# DeepSeek Coder Licence + WhiteRabbitNeo Extended Version
# Licence: Usage Restrictions
# Topics Covered:
# Terms of Use
By accessing and using this Artificial Intelligence (AI) model, you, the user, acknowledge and agree that you are solely responsible for your use of the model and its outcomes. You hereby agree to indemnify, defend, and hold harmless the creators, developers, and any affiliated persons or entities of this AI model from and against any and all claims, liabilities, damages, losses, costs, expenses, fees (including reasonable attorneys' fees and court costs) that may arise, directly or indirectly, from your use of the AI model.
This AI model is provided "as is" and "as available" without any warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and non-infringement. The creators make no warranty that the AI model will meet your requirements or be available on an uninterrupted, secure, or error-free basis.
Your use of the AI model is at your own risk and discretion, and you will be solely responsible for any damage to computer systems or loss of data that results from the use of the AI model.
This disclaimer constitutes part of the agreement between you and the creators of the AI model regarding your use of the model, superseding any prior agreements between you and the creators regarding your use of this AI model.
# WhiteRabbitNeo
<br>
!WhiteRabbitNeo
<br>
WhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity.
Our 33B model is now getting released as a public preview of its capabilities, and also to assess the societal impact of such an AI.
| [
"# This model is now live (We'll always be serving the newest model on our web app)!\n Access at: URL",
"# Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# DeepSeek Coder Licence + WhiteRabbitNeo Extended Version",
"# Licence: Usage Restrictions",
"# Topics Covered:",
"# Terms of Use\nBy accessing and using this Artificial Intelligence (AI) model, you, the user, acknowledge and agree that you are solely responsible for your use of the model and its outcomes. You hereby agree to indemnify, defend, and hold harmless the creators, developers, and any affiliated persons or entities of this AI model from and against any and all claims, liabilities, damages, losses, costs, expenses, fees (including reasonable attorneys' fees and court costs) that may arise, directly or indirectly, from your use of the AI model.\n\nThis AI model is provided \"as is\" and \"as available\" without any warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and non-infringement. The creators make no warranty that the AI model will meet your requirements or be available on an uninterrupted, secure, or error-free basis.\n\nYour use of the AI model is at your own risk and discretion, and you will be solely responsible for any damage to computer systems or loss of data that results from the use of the AI model.\n\nThis disclaimer constitutes part of the agreement between you and the creators of the AI model regarding your use of the model, superseding any prior agreements between you and the creators regarding your use of this AI model.",
"# WhiteRabbitNeo\n\n<br>\n\n!WhiteRabbitNeo\n\n<br>\n\nWhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity. \n\nOur 33B model is now getting released as a public preview of its capabilities, and also to assess the societal impact of such an AI."
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #has_space \n",
"# This model is now live (We'll always be serving the newest model on our web app)!\n Access at: URL",
"# Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# DeepSeek Coder Licence + WhiteRabbitNeo Extended Version",
"# Licence: Usage Restrictions",
"# Topics Covered:",
"# Terms of Use\nBy accessing and using this Artificial Intelligence (AI) model, you, the user, acknowledge and agree that you are solely responsible for your use of the model and its outcomes. You hereby agree to indemnify, defend, and hold harmless the creators, developers, and any affiliated persons or entities of this AI model from and against any and all claims, liabilities, damages, losses, costs, expenses, fees (including reasonable attorneys' fees and court costs) that may arise, directly or indirectly, from your use of the AI model.\n\nThis AI model is provided \"as is\" and \"as available\" without any warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and non-infringement. The creators make no warranty that the AI model will meet your requirements or be available on an uninterrupted, secure, or error-free basis.\n\nYour use of the AI model is at your own risk and discretion, and you will be solely responsible for any damage to computer systems or loss of data that results from the use of the AI model.\n\nThis disclaimer constitutes part of the agreement between you and the creators of the AI model regarding your use of the model, superseding any prior agreements between you and the creators regarding your use of this AI model.",
"# WhiteRabbitNeo\n\n<br>\n\n!WhiteRabbitNeo\n\n<br>\n\nWhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity. \n\nOur 33B model is now getting released as a public preview of its capabilities, and also to assess the societal impact of such an AI."
] | [
60,
28,
24,
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #has_space \n# This model is now live (We'll always be serving the newest model on our web app)!\n Access at: URL# Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)# DeepSeek Coder Licence + WhiteRabbitNeo Extended Version# Licence: Usage Restrictions# Topics Covered:# Terms of Use\nBy accessing and using this Artificial Intelligence (AI) model, you, the user, acknowledge and agree that you are solely responsible for your use of the model and its outcomes. You hereby agree to indemnify, defend, and hold harmless the creators, developers, and any affiliated persons or entities of this AI model from and against any and all claims, liabilities, damages, losses, costs, expenses, fees (including reasonable attorneys' fees and court costs) that may arise, directly or indirectly, from your use of the AI model.\n\nThis AI model is provided \"as is\" and \"as available\" without any warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and non-infringement. The creators make no warranty that the AI model will meet your requirements or be available on an uninterrupted, secure, or error-free basis.\n\nYour use of the AI model is at your own risk and discretion, and you will be solely responsible for any damage to computer systems or loss of data that results from the use of the AI model.\n\nThis disclaimer constitutes part of the agreement between you and the creators of the AI model regarding your use of the model, superseding any prior agreements between you and the creators regarding your use of this AI model."
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null | null | diffusers |
# Stable Diffusion v2 Model Card
This model card focuses on the model associated with the Stable Diffusion v2, available [here](https://github.com/Stability-AI/stablediffusion).
This `stable-diffusion-2-inpainting` model is resumed from [stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) (`512-base-ema.ckpt`) and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning.

- Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `512-inpainting-ema.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting/resolve/main/512-inpainting-ema.ckpt).
- Use it with 🧨 [`diffusers`](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting#examples)
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL)
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)).
- **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
## Examples
Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 inpainting in a simple and efficient manner.
```bash
pip install diffusers transformers accelerate scipy safetensors
```
```python
from diffusers import StableDiffusionInpaintPipeline
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-inpainting",
torch_dtype=torch.float16,
)
pipe.to("cuda")
prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
#image and mask_image should be PIL images.
#The mask structure is white for inpainting and black for keeping as is
image = pipe(prompt=prompt, image=image, mask_image=mask_image).images[0]
image.save("./yellow_cat_on_park_bench.png")
```
**Notes**:
- Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance)
- If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed)
**How it works:**
`image` | `mask_image`
:-------------------------:|:-------------------------:|
<img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" alt="drawing" width="300"/> | <img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" alt="drawing" width="300"/>
`prompt` | `Output`
:-------------------------:|:-------------------------:|
<span style="position: relative;bottom: 150px;">Face of a yellow cat, high resolution, sitting on a park bench</span> | <img src="https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/test.png" alt="drawing" width="300"/>
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a subset of the large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section).
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion vw was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic.
**Training Procedure**
Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through the OpenCLIP-ViT/H text-encoder.
- The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512.
We currently provide the following checkpoints:
- `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`.
850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`.
- `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset.
- `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning.
The additional input channels of the U-Net which process this extra information were zero-initialized.
- `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning.
The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://github.com/saic-mdal/lama).
- `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752).
In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml).
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 1
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:

Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 200000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq.
## Citation
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
*This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
| {"license": "openrail++", "tags": ["stable-diffusion"], "inference": false} | null | alwold/stable-diffusion-2-inpainting | [
"diffusers",
"safetensors",
"stable-diffusion",
"arxiv:2112.10752",
"arxiv:2202.00512",
"arxiv:1910.09700",
"license:openrail++",
"endpoints_compatible",
"diffusers:StableDiffusionInpaintPipeline",
"region:us"
] | 2024-02-09T19:58:58+00:00 | [
"2112.10752",
"2202.00512",
"1910.09700"
] | [] | TAGS
#diffusers #safetensors #stable-diffusion #arxiv-2112.10752 #arxiv-2202.00512 #arxiv-1910.09700 #license-openrail++ #endpoints_compatible #diffusers-StableDiffusionInpaintPipeline #region-us
| Stable Diffusion v2 Model Card
==============================
This model card focuses on the model associated with the Stable Diffusion v2, available here.
This 'stable-diffusion-2-inpainting' model is resumed from stable-diffusion-2-base ('URL') and trained for another 200k steps. Follows the mask-generation strategy presented in LAMA which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning.
!image
* Use it with the 'stablediffusion' repository: download the 'URL' here.
* Use it with 'diffusers'
Model Details
-------------
* Developed by: Robin Rombach, Patrick Esser
* Model type: Diffusion-based text-to-image generation model
* Language(s): English
* License: CreativeML Open RAIL++-M License
* Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a Latent Diffusion Model that uses a fixed, pretrained text encoder (OpenCLIP-ViT/H).
* Resources for more information: GitHub Repository.
* Cite as:
```
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
```
Examples
--------
Using the 's Diffusers library to run Stable Diffusion 2 inpainting in a simple and efficient manner.
Notes:
* Despite not being a dependency, we highly recommend you to install xformers for memory efficient attention (better performance)
* If you have low GPU RAM available, make sure to add a 'pipe.enable\_attention\_slicing()' after sending it to 'cuda' for less VRAM usage (to the cost of speed)
How it works:
Uses
====
Direct Use
----------
The model is intended for research purposes only. Possible research areas and tasks include
* Safe deployment of models which have the potential to generate harmful content.
* Probing and understanding the limitations and biases of generative models.
* Generation of artworks and use in design and other artistic processes.
* Applications in educational or creative tools.
* Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
*Note: This section is originally taken from the DALLE-MINI model card, was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2*.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
* Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
* Intentionally promoting or propagating discriminatory content or harmful stereotypes.
* Impersonating individuals without their consent.
* Sexual content without consent of the people who might see it.
* Mis- and disinformation
* Representations of egregious violence and gore
* Sharing of copyrighted or licensed material in violation of its terms of use.
* Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
Limitations and Bias
--------------------
### Limitations
* The model does not achieve perfect photorealism
* The model cannot render legible text
* The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
* Faces and people in general may not be generated properly.
* The model was trained mainly with English captions and will not work as well in other languages.
* The autoencoding part of the model is lossy
* The model was trained on a subset of the large-scale dataset
LAION-5B, which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section).
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion vw was primarily trained on subsets of LAION-2B(en),
which consists of images that are limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.
Training
--------
Training Data
The model developers used the following dataset for training the model:
* LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p\_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's NeurIPS 2022 paper and reviewer discussions on the topic.
Training Procedure
Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
* Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
* Text prompts are encoded through the OpenCLIP-ViT/H text-encoder.
* The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
* The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called *v-objective*, see URL
We currently provide the following checkpoints:
* 'URL': 550k steps at resolution '256x256' on a subset of LAION-5B filtered for explicit pornographic material, using the LAION-NSFW classifier with 'punsafe=0.1' and an aesthetic score >= '4.5'.
850k steps at resolution '512x512' on the same dataset with resolution '>= 512x512'.
* 'URL': Resumed from 'URL' and trained for 150k steps using a v-objective on the same dataset. Resumed for another 140k steps on a '768x768' subset of our dataset.
* 'URL': Resumed from 'URL' and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by MiDaS ('dpt\_hybrid') which is used as an additional conditioning.
The additional input channels of the U-Net which process this extra information were zero-initialized.
* 'URL': Resumed from 'URL' and trained for another 200k steps. Follows the mask-generation strategy presented in LAMA which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning.
The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the 1.5-inpainting checkpoint.
* 'URL': Trained for 1.25M steps on a 10M subset of LAION containing images '>2048x2048'. The model was trained on crops of size '512x512' and is a text-guided latent upscaling diffusion model.
In addition to the textual input, it receives a 'noise\_level' as an input parameter, which can be used to add noise to the low-resolution input according to a predefined diffusion schedule.
* Hardware: 32 x 8 x A100 GPUs
* Optimizer: AdamW
* Gradient Accumulations: 1
* Batch: 32 x 8 x 2 x 4 = 2048
* Learning rate: warmup to 0.0001 for 10,000 steps and then kept constant
Evaluation Results
------------------
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:
!pareto
Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
Environmental Impact
--------------------
Stable Diffusion v1 Estimated Emissions
Based on that information, we estimate the following CO2 emissions using the Machine Learning Impact calculator presented in Lacoste et al. (2019). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
* Hardware Type: A100 PCIe 40GB
* Hours used: 200000
* Cloud Provider: AWS
* Compute Region: US-east
* Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 15000 kg CO2 eq.
@InProceedings{Rombach\_2022\_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
*This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the Stable Diffusion v1 and DALL-E Mini model card.*
| [
"### Misuse, Malicious Use, and Out-of-Scope Use\n\n\n*Note: This section is originally taken from the DALLE-MINI model card, was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2*.\n\n\nThe model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.",
"#### Out-of-Scope Use\n\n\nThe model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.",
"#### Misuse and Malicious Use\n\n\nUsing the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:\n\n\n* Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.\n* Intentionally promoting or propagating discriminatory content or harmful stereotypes.\n* Impersonating individuals without their consent.\n* Sexual content without consent of the people who might see it.\n* Mis- and disinformation\n* Representations of egregious violence and gore\n* Sharing of copyrighted or licensed material in violation of its terms of use.\n* Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.\n\n\nLimitations and Bias\n--------------------",
"### Limitations\n\n\n* The model does not achieve perfect photorealism\n* The model cannot render legible text\n* The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”\n* Faces and people in general may not be generated properly.\n* The model was trained mainly with English captions and will not work as well in other languages.\n* The autoencoding part of the model is lossy\n* The model was trained on a subset of the large-scale dataset\nLAION-5B, which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section).",
"### Bias\n\n\nWhile the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.\nStable Diffusion vw was primarily trained on subsets of LAION-2B(en),\nwhich consists of images that are limited to English descriptions.\nTexts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.\nThis affects the overall output of the model, as white and western cultures are often set as the default. Further, the\nability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.\nStable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.\n\n\nTraining\n--------\n\n\nTraining Data\nThe model developers used the following dataset for training the model:\n\n\n* LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a \"p\\_unsafe\" score of 0.1 (conservative). For more details, please refer to LAION-5B's NeurIPS 2022 paper and reviewer discussions on the topic.\n\n\nTraining Procedure\nStable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,\n\n\n* Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4\n* Text prompts are encoded through the OpenCLIP-ViT/H text-encoder.\n* The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.\n* The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called *v-objective*, see URL\n\n\nWe currently provide the following checkpoints:\n\n\n* 'URL': 550k steps at resolution '256x256' on a subset of LAION-5B filtered for explicit pornographic material, using the LAION-NSFW classifier with 'punsafe=0.1' and an aesthetic score >= '4.5'.\n850k steps at resolution '512x512' on the same dataset with resolution '>= 512x512'.\n* 'URL': Resumed from 'URL' and trained for 150k steps using a v-objective on the same dataset. Resumed for another 140k steps on a '768x768' subset of our dataset.\n* 'URL': Resumed from 'URL' and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by MiDaS ('dpt\\_hybrid') which is used as an additional conditioning.\nThe additional input channels of the U-Net which process this extra information were zero-initialized.\n* 'URL': Resumed from 'URL' and trained for another 200k steps. Follows the mask-generation strategy presented in LAMA which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning.\nThe additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the 1.5-inpainting checkpoint.\n* 'URL': Trained for 1.25M steps on a 10M subset of LAION containing images '>2048x2048'. The model was trained on crops of size '512x512' and is a text-guided latent upscaling diffusion model.\nIn addition to the textual input, it receives a 'noise\\_level' as an input parameter, which can be used to add noise to the low-resolution input according to a predefined diffusion schedule.\n* Hardware: 32 x 8 x A100 GPUs\n* Optimizer: AdamW\n* Gradient Accumulations: 1\n* Batch: 32 x 8 x 2 x 4 = 2048\n* Learning rate: warmup to 0.0001 for 10,000 steps and then kept constant\n\n\nEvaluation Results\n------------------\n\n\nEvaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,\n5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:\n\n\n!pareto\n\n\nEvaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.\n\n\nEnvironmental Impact\n--------------------\n\n\nStable Diffusion v1 Estimated Emissions\nBased on that information, we estimate the following CO2 emissions using the Machine Learning Impact calculator presented in Lacoste et al. (2019). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.\n\n\n* Hardware Type: A100 PCIe 40GB\n* Hours used: 200000\n* Cloud Provider: AWS\n* Compute Region: US-east\n* Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 15000 kg CO2 eq.\n\n\n@InProceedings{Rombach\\_2022\\_CVPR,\nauthor = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},\ntitle = {High-Resolution Image Synthesis With Latent Diffusion Models},\nbooktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},\nmonth = {June},\nyear = {2022},\npages = {10684-10695}\n}\n\n\n*This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the Stable Diffusion v1 and DALL-E Mini model card.*"
] | [
"TAGS\n#diffusers #safetensors #stable-diffusion #arxiv-2112.10752 #arxiv-2202.00512 #arxiv-1910.09700 #license-openrail++ #endpoints_compatible #diffusers-StableDiffusionInpaintPipeline #region-us \n",
"### Misuse, Malicious Use, and Out-of-Scope Use\n\n\n*Note: This section is originally taken from the DALLE-MINI model card, was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2*.\n\n\nThe model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.",
"#### Out-of-Scope Use\n\n\nThe model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.",
"#### Misuse and Malicious Use\n\n\nUsing the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:\n\n\n* Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.\n* Intentionally promoting or propagating discriminatory content or harmful stereotypes.\n* Impersonating individuals without their consent.\n* Sexual content without consent of the people who might see it.\n* Mis- and disinformation\n* Representations of egregious violence and gore\n* Sharing of copyrighted or licensed material in violation of its terms of use.\n* Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.\n\n\nLimitations and Bias\n--------------------",
"### Limitations\n\n\n* The model does not achieve perfect photorealism\n* The model cannot render legible text\n* The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”\n* Faces and people in general may not be generated properly.\n* The model was trained mainly with English captions and will not work as well in other languages.\n* The autoencoding part of the model is lossy\n* The model was trained on a subset of the large-scale dataset\nLAION-5B, which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section).",
"### Bias\n\n\nWhile the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.\nStable Diffusion vw was primarily trained on subsets of LAION-2B(en),\nwhich consists of images that are limited to English descriptions.\nTexts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.\nThis affects the overall output of the model, as white and western cultures are often set as the default. Further, the\nability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.\nStable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.\n\n\nTraining\n--------\n\n\nTraining Data\nThe model developers used the following dataset for training the model:\n\n\n* LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a \"p\\_unsafe\" score of 0.1 (conservative). For more details, please refer to LAION-5B's NeurIPS 2022 paper and reviewer discussions on the topic.\n\n\nTraining Procedure\nStable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,\n\n\n* Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4\n* Text prompts are encoded through the OpenCLIP-ViT/H text-encoder.\n* The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.\n* The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called *v-objective*, see URL\n\n\nWe currently provide the following checkpoints:\n\n\n* 'URL': 550k steps at resolution '256x256' on a subset of LAION-5B filtered for explicit pornographic material, using the LAION-NSFW classifier with 'punsafe=0.1' and an aesthetic score >= '4.5'.\n850k steps at resolution '512x512' on the same dataset with resolution '>= 512x512'.\n* 'URL': Resumed from 'URL' and trained for 150k steps using a v-objective on the same dataset. Resumed for another 140k steps on a '768x768' subset of our dataset.\n* 'URL': Resumed from 'URL' and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by MiDaS ('dpt\\_hybrid') which is used as an additional conditioning.\nThe additional input channels of the U-Net which process this extra information were zero-initialized.\n* 'URL': Resumed from 'URL' and trained for another 200k steps. Follows the mask-generation strategy presented in LAMA which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning.\nThe additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the 1.5-inpainting checkpoint.\n* 'URL': Trained for 1.25M steps on a 10M subset of LAION containing images '>2048x2048'. The model was trained on crops of size '512x512' and is a text-guided latent upscaling diffusion model.\nIn addition to the textual input, it receives a 'noise\\_level' as an input parameter, which can be used to add noise to the low-resolution input according to a predefined diffusion schedule.\n* Hardware: 32 x 8 x A100 GPUs\n* Optimizer: AdamW\n* Gradient Accumulations: 1\n* Batch: 32 x 8 x 2 x 4 = 2048\n* Learning rate: warmup to 0.0001 for 10,000 steps and then kept constant\n\n\nEvaluation Results\n------------------\n\n\nEvaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,\n5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:\n\n\n!pareto\n\n\nEvaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.\n\n\nEnvironmental Impact\n--------------------\n\n\nStable Diffusion v1 Estimated Emissions\nBased on that information, we estimate the following CO2 emissions using the Machine Learning Impact calculator presented in Lacoste et al. (2019). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.\n\n\n* Hardware Type: A100 PCIe 40GB\n* Hours used: 200000\n* Cloud Provider: AWS\n* Compute Region: US-east\n* Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 15000 kg CO2 eq.\n\n\n@InProceedings{Rombach\\_2022\\_CVPR,\nauthor = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},\ntitle = {High-Resolution Image Synthesis With Latent Diffusion Models},\nbooktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},\nmonth = {June},\nyear = {2022},\npages = {10684-10695}\n}\n\n\n*This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the Stable Diffusion v1 and DALL-E Mini model card.*"
] | [
79,
125,
51,
176,
173,
1390
] | [
"passage: TAGS\n#diffusers #safetensors #stable-diffusion #arxiv-2112.10752 #arxiv-2202.00512 #arxiv-1910.09700 #license-openrail++ #endpoints_compatible #diffusers-StableDiffusionInpaintPipeline #region-us \n### Misuse, Malicious Use, and Out-of-Scope Use\n\n\n*Note: This section is originally taken from the DALLE-MINI model card, was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2*.\n\n\nThe model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.#### Out-of-Scope Use\n\n\nThe model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.#### Misuse and Malicious Use\n\n\nUsing the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:\n\n\n* Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.\n* Intentionally promoting or propagating discriminatory content or harmful stereotypes.\n* Impersonating individuals without their consent.\n* Sexual content without consent of the people who might see it.\n* Mis- and disinformation\n* Representations of egregious violence and gore\n* Sharing of copyrighted or licensed material in violation of its terms of use.\n* Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.\n\n\nLimitations and Bias\n--------------------",
"passage: ### Limitations\n\n\n* The model does not achieve perfect photorealism\n* The model cannot render legible text\n* The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”\n* Faces and people in general may not be generated properly.\n* The model was trained mainly with English captions and will not work as well in other languages.\n* The autoencoding part of the model is lossy\n* The model was trained on a subset of the large-scale dataset\nLAION-5B, which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section)."
] | [
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] |
null | null | transformers | # Description
[MaziyarPanahi/Mistral-7B-Instruct-v0.1-AWQ](https://huggingface.co/MaziyarPanahi/Mistral-7B-Instruct-v0.1-AWQ) is a quantized (AWQ) version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
## How to use
### Install the necessary packages
```
pip install --upgrade accelerate autoawq transformers
```
### Example Python code
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "MaziyarPanahi/Mistral-7B-Instruct-v0.1-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id).to(0)
text = "User:\nHello can you provide me with top-3 cool places to visit in Paris?\n\nAssistant:\n"
inputs = tokenizer(text, return_tensors="pt").to(0)
out = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(out[0], skip_special_tokens=True))
``` | {"tags": ["finetuned", "quantized", "4-bit", "AWQ", "transformers", "pytorch", "safetensors", "mistral", "text-generation", "finetuned", "conversational", "arxiv:2310.06825", "license:apache-2.0", "autotrain_compatible", "has_space", "text-generation-inference", "region:us"], "model_name": "Mistral-7B-Instruct-v0.1-AWQ", "base_model": "mistralai/Mistral-7B-Instruct-v0.1", "inference": false, "model_creator": "mistralai", "pipeline_tag": "text-generation", "quantized_by": "MaziyarPanahi"} | text-generation | MaziyarPanahi/Mistral-7B-Instruct-v0.1-AWQ | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"quantized",
"4-bit",
"AWQ",
"pytorch",
"conversational",
"arxiv:2310.06825",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us",
"base_model:mistralai/Mistral-7B-Instruct-v0.1"
] | 2024-02-09T19:59:04+00:00 | [
"2310.06825"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #finetuned #quantized #4-bit #AWQ #pytorch #conversational #arxiv-2310.06825 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us #base_model-mistralai/Mistral-7B-Instruct-v0.1
| # Description
MaziyarPanahi/Mistral-7B-Instruct-v0.1-AWQ is a quantized (AWQ) version of mistralai/Mistral-7B-Instruct-v0.1
## How to use
### Install the necessary packages
### Example Python code
| [
"# Description\nMaziyarPanahi/Mistral-7B-Instruct-v0.1-AWQ is a quantized (AWQ) version of mistralai/Mistral-7B-Instruct-v0.1",
"## How to use",
"### Install the necessary packages",
"### Example Python code"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #finetuned #quantized #4-bit #AWQ #pytorch #conversational #arxiv-2310.06825 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us #base_model-mistralai/Mistral-7B-Instruct-v0.1 \n",
"# Description\nMaziyarPanahi/Mistral-7B-Instruct-v0.1-AWQ is a quantized (AWQ) version of mistralai/Mistral-7B-Instruct-v0.1",
"## How to use",
"### Install the necessary packages",
"### Example Python code"
] | [
96,
44,
4,
7,
6
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #finetuned #quantized #4-bit #AWQ #pytorch #conversational #arxiv-2310.06825 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us #base_model-mistralai/Mistral-7B-Instruct-v0.1 \n# Description\nMaziyarPanahi/Mistral-7B-Instruct-v0.1-AWQ is a quantized (AWQ) version of mistralai/Mistral-7B-Instruct-v0.1## How to use### Install the necessary packages### Example Python code"
] | [
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null | null | transformers |

# To see what will happen.
[Join our Discord!](https://discord.gg/aEGuFph9)
[GGUF FILES HERE](https://huggingface.co/Kquant03/Samlagast-7B-GGUF)
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
### Merge Method
This model was merged using the [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using [paulml/NeuralOmniBeagleMBX-v3-7B](https://huggingface.co/paulml/NeuralOmniBeagleMBX-v3-7B) as a base.
### Models Merged
The following models were included in the merge:
* [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3)
* [paulml/NeuralOmniWestBeaglake-7B](https://huggingface.co/paulml/NeuralOmniWestBeaglake-7B)
* [FelixChao/Faraday-7B](https://huggingface.co/FelixChao/Faraday-7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: paulml/NeuralOmniWestBeaglake-7B
parameters:
weight: 1
- model: FelixChao/Faraday-7B
parameters:
weight: 1
- model: flemmingmiguel/MBX-7B-v3
parameters:
weight: 1
- model: paulml/NeuralOmniBeagleMBX-v3-7B
parameters:
weight: 1
merge_method: task_arithmetic
base_model: paulml/NeuralOmniBeagleMBX-v3-7B
parameters:
normalize: true
int8_mask: true
dtype: float16
``` | {"language": ["en"], "license": "apache-2.0", "tags": ["mergekit", "merge"], "base_model": ["flemmingmiguel/MBX-7B-v3", "paulml/NeuralOmniWestBeaglake-7B", "FelixChao/Faraday-7B", "paulml/NeuralOmniBeagleMBX-v3-7B"]} | text-generation | Kquant03/Samlagast-7B-bf16 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"en",
"arxiv:2212.04089",
"base_model:flemmingmiguel/MBX-7B-v3",
"base_model:paulml/NeuralOmniWestBeaglake-7B",
"base_model:FelixChao/Faraday-7B",
"base_model:paulml/NeuralOmniBeagleMBX-v3-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T20:01:21+00:00 | [
"2212.04089"
] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #en #arxiv-2212.04089 #base_model-flemmingmiguel/MBX-7B-v3 #base_model-paulml/NeuralOmniWestBeaglake-7B #base_model-FelixChao/Faraday-7B #base_model-paulml/NeuralOmniBeagleMBX-v3-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
!image/png
# To see what will happen.
Join our Discord!
GGUF FILES HERE
This is a merge of pre-trained language models created using mergekit.
### Merge Method
This model was merged using the task arithmetic merge method using paulml/NeuralOmniBeagleMBX-v3-7B as a base.
### Models Merged
The following models were included in the merge:
* flemmingmiguel/MBX-7B-v3
* paulml/NeuralOmniWestBeaglake-7B
* FelixChao/Faraday-7B
### Configuration
The following YAML configuration was used to produce this model:
| [
"# To see what will happen.\n\nJoin our Discord!\n\nGGUF FILES HERE\n\nThis is a merge of pre-trained language models created using mergekit.",
"### Merge Method\n\nThis model was merged using the task arithmetic merge method using paulml/NeuralOmniBeagleMBX-v3-7B as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* flemmingmiguel/MBX-7B-v3\n* paulml/NeuralOmniWestBeaglake-7B\n* FelixChao/Faraday-7B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #en #arxiv-2212.04089 #base_model-flemmingmiguel/MBX-7B-v3 #base_model-paulml/NeuralOmniWestBeaglake-7B #base_model-FelixChao/Faraday-7B #base_model-paulml/NeuralOmniBeagleMBX-v3-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# To see what will happen.\n\nJoin our Discord!\n\nGGUF FILES HERE\n\nThis is a merge of pre-trained language models created using mergekit.",
"### Merge Method\n\nThis model was merged using the task arithmetic merge method using paulml/NeuralOmniBeagleMBX-v3-7B as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* flemmingmiguel/MBX-7B-v3\n* paulml/NeuralOmniWestBeaglake-7B\n* FelixChao/Faraday-7B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
147,
34,
41,
54,
17
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #en #arxiv-2212.04089 #base_model-flemmingmiguel/MBX-7B-v3 #base_model-paulml/NeuralOmniWestBeaglake-7B #base_model-FelixChao/Faraday-7B #base_model-paulml/NeuralOmniBeagleMBX-v3-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# To see what will happen.\n\nJoin our Discord!\n\nGGUF FILES HERE\n\nThis is a merge of pre-trained language models created using mergekit.### Merge Method\n\nThis model was merged using the task arithmetic merge method using paulml/NeuralOmniBeagleMBX-v3-7B as a base.### Models Merged\n\nThe following models were included in the merge:\n* flemmingmiguel/MBX-7B-v3\n* paulml/NeuralOmniWestBeaglake-7B\n* FelixChao/Faraday-7B### Configuration\n\nThe following YAML configuration was used to produce this model:"
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null | null | peft |
# Model Card for Model ID
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## Model Details
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "mistralai/Mistral-7B-v0.1"} | null | Anakeen/mistral-assurdoc-finetune | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"region:us"
] | 2024-02-09T20:05:06+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-v0.1 #region-us
|
# Model Card for Model ID
## Model Details
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## Uses
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## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## 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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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
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#### 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:
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## Technical Specifications [optional]
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[optional]
BibTeX:
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## Glossary [optional]
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## Model Card Authors [optional]
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### 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",
"### 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 0.8.2"
] | [
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-v0.1 #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 0.8.2"
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"passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-v0.1 #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 0.8.2"
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null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codellama-7b-instruct-ffmperative-top1k-neft
This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-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: 1.41e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.1
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0 | {"license": "llama2", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "codellama/CodeLlama-7b-Instruct-hf", "model-index": [{"name": "codellama-7b-instruct-ffmperative-top1k-neft", "results": []}]} | null | salma-remyx/codellama-7b-instruct-ffmperative-top1k-neft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:codellama/CodeLlama-7b-Instruct-hf",
"license:llama2",
"region:us"
] | 2024-02-09T20:05:35+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-codellama/CodeLlama-7b-Instruct-hf #license-llama2 #region-us
|
# codellama-7b-instruct-ffmperative-top1k-neft
This model is a fine-tuned version of codellama/CodeLlama-7b-Instruct-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: 1.41e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.1
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0 | [
"# codellama-7b-instruct-ffmperative-top1k-neft\n\nThis model is a fine-tuned version of codellama/CodeLlama-7b-Instruct-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: 1.41e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 2\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.36.1\n- Pytorch 2.1.0+cu118\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#peft #safetensors #generated_from_trainer #base_model-codellama/CodeLlama-7b-Instruct-hf #license-llama2 #region-us \n",
"# codellama-7b-instruct-ffmperative-top1k-neft\n\nThis model is a fine-tuned version of codellama/CodeLlama-7b-Instruct-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: 1.41e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 2\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.36.1\n- Pytorch 2.1.0+cu118\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
48,
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12,
8,
3,
114,
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39
] | [
"passage: TAGS\n#peft #safetensors #generated_from_trainer #base_model-codellama/CodeLlama-7b-Instruct-hf #license-llama2 #region-us \n# codellama-7b-instruct-ffmperative-top1k-neft\n\nThis model is a fine-tuned version of codellama/CodeLlama-7b-Instruct-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: 1.41e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 2\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5### Training results### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.36.1\n- Pytorch 2.1.0+cu118\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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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="AlGM93/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 | AlGM93/q-FrozenLake-v1-4x4-noSlippery | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-09T20:06:04+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 |
# **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="AlGM93/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 | AlGM93/q-Taxi-v3 | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-09T20:09:08+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 | null |

# To see what will happen.
[Join our Discord!](https://discord.gg/XfUWdT9D)
[BASE MODEL HERE](https://huggingface.co/Kquant03/Samlagast-7B-bf16)
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
### Merge Method
This model was merged using the [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using [paulml/NeuralOmniBeagleMBX-v3-7B](https://huggingface.co/paulml/NeuralOmniBeagleMBX-v3-7B) as a base.
### Models Merged
The following models were included in the merge:
* [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3)
* [paulml/NeuralOmniWestBeaglake-7B](https://huggingface.co/paulml/NeuralOmniWestBeaglake-7B)
* [FelixChao/Faraday-7B](https://huggingface.co/FelixChao/Faraday-7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: paulml/NeuralOmniWestBeaglake-7B
parameters:
weight: 1
- model: FelixChao/Faraday-7B
parameters:
weight: 1
- model: flemmingmiguel/MBX-7B-v3
parameters:
weight: 1
- model: paulml/NeuralOmniBeagleMBX-v3-7B
parameters:
weight: 1
merge_method: task_arithmetic
base_model: paulml/NeuralOmniBeagleMBX-v3-7B
parameters:
normalize: true
int8_mask: true
dtype: float16
``` | {"language": ["en"], "license": "apache-2.0", "tags": ["mergekit", "merge"], "base_model": ["flemmingmiguel/MBX-7B-v3", "paulml/NeuralOmniWestBeaglake-7B", "FelixChao/Faraday-7B", "paulml/NeuralOmniBeagleMBX-v3-7B"]} | null | Kquant03/Samlagast-7B-GGUF | [
"gguf",
"mergekit",
"merge",
"en",
"arxiv:2212.04089",
"base_model:flemmingmiguel/MBX-7B-v3",
"base_model:paulml/NeuralOmniWestBeaglake-7B",
"base_model:FelixChao/Faraday-7B",
"base_model:paulml/NeuralOmniBeagleMBX-v3-7B",
"license:apache-2.0",
"region:us"
] | 2024-02-09T20:12:15+00:00 | [
"2212.04089"
] | [
"en"
] | TAGS
#gguf #mergekit #merge #en #arxiv-2212.04089 #base_model-flemmingmiguel/MBX-7B-v3 #base_model-paulml/NeuralOmniWestBeaglake-7B #base_model-FelixChao/Faraday-7B #base_model-paulml/NeuralOmniBeagleMBX-v3-7B #license-apache-2.0 #region-us
|
!image/png
# To see what will happen.
Join our Discord!
BASE MODEL HERE
This is a merge of pre-trained language models created using mergekit.
### Merge Method
This model was merged using the task arithmetic merge method using paulml/NeuralOmniBeagleMBX-v3-7B as a base.
### Models Merged
The following models were included in the merge:
* flemmingmiguel/MBX-7B-v3
* paulml/NeuralOmniWestBeaglake-7B
* FelixChao/Faraday-7B
### Configuration
The following YAML configuration was used to produce this model:
| [
"# To see what will happen.\n\nJoin our Discord!\n\nBASE MODEL HERE\n\nThis is a merge of pre-trained language models created using mergekit.",
"### Merge Method\n\nThis model was merged using the task arithmetic merge method using paulml/NeuralOmniBeagleMBX-v3-7B as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* flemmingmiguel/MBX-7B-v3\n* paulml/NeuralOmniWestBeaglake-7B\n* FelixChao/Faraday-7B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#gguf #mergekit #merge #en #arxiv-2212.04089 #base_model-flemmingmiguel/MBX-7B-v3 #base_model-paulml/NeuralOmniWestBeaglake-7B #base_model-FelixChao/Faraday-7B #base_model-paulml/NeuralOmniBeagleMBX-v3-7B #license-apache-2.0 #region-us \n",
"# To see what will happen.\n\nJoin our Discord!\n\nBASE MODEL HERE\n\nThis is a merge of pre-trained language models created using mergekit.",
"### Merge Method\n\nThis model was merged using the task arithmetic merge method using paulml/NeuralOmniBeagleMBX-v3-7B as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* flemmingmiguel/MBX-7B-v3\n* paulml/NeuralOmniWestBeaglake-7B\n* FelixChao/Faraday-7B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
109,
32,
41,
54,
17
] | [
"passage: TAGS\n#gguf #mergekit #merge #en #arxiv-2212.04089 #base_model-flemmingmiguel/MBX-7B-v3 #base_model-paulml/NeuralOmniWestBeaglake-7B #base_model-FelixChao/Faraday-7B #base_model-paulml/NeuralOmniBeagleMBX-v3-7B #license-apache-2.0 #region-us \n# To see what will happen.\n\nJoin our Discord!\n\nBASE MODEL HERE\n\nThis is a merge of pre-trained language models created using mergekit.### Merge Method\n\nThis model was merged using the task arithmetic merge method using paulml/NeuralOmniBeagleMBX-v3-7B as a base.### Models Merged\n\nThe following models were included in the merge:\n* flemmingmiguel/MBX-7B-v3\n* paulml/NeuralOmniWestBeaglake-7B\n* FelixChao/Faraday-7B### 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. -->
# timesformer-base-finetuned-k400-finetuned-asl
This model is a fine-tuned version of [facebook/timesformer-base-finetuned-k400](https://huggingface.co/facebook/timesformer-base-finetuned-k400) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1142
- Accuracy: 0.9625
## 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: 360
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3325 | 0.25 | 90 | 0.2077 | 0.9625 |
| 0.2898 | 1.25 | 180 | 0.1923 | 0.9375 |
| 0.3358 | 2.25 | 270 | 0.1170 | 0.95 |
| 0.3354 | 3.25 | 360 | 0.1142 | 0.9625 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
| {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/timesformer-base-finetuned-k400", "model-index": [{"name": "timesformer-base-finetuned-k400-finetuned-asl", "results": []}]} | video-classification | Krithiik/timesformer-base-finetuned-k400-finetuned-asl | [
"transformers",
"tensorboard",
"safetensors",
"timesformer",
"video-classification",
"generated_from_trainer",
"base_model:facebook/timesformer-base-finetuned-k400",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | 2024-02-09T20:14:09+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #timesformer #video-classification #generated_from_trainer #base_model-facebook/timesformer-base-finetuned-k400 #license-cc-by-nc-4.0 #endpoints_compatible #region-us
| timesformer-base-finetuned-k400-finetuned-asl
=============================================
This model is a fine-tuned version of facebook/timesformer-base-finetuned-k400 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1142
* Accuracy: 0.9625
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: 360
### Training results
### Framework versions
* Transformers 4.37.0
* Pytorch 2.1.2
* Datasets 2.1.0
* 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: 360",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #timesformer #video-classification #generated_from_trainer #base_model-facebook/timesformer-base-finetuned-k400 #license-cc-by-nc-4.0 #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: 360",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1"
] | [
70,
115,
4,
30
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #timesformer #video-classification #generated_from_trainer #base_model-facebook/timesformer-base-finetuned-k400 #license-cc-by-nc-4.0 #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: 360### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1"
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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. -->
# BOLETIN_16bit_27
This model is a fine-tuned version of [bertin-project/BOLETIN](https://huggingface.co/bertin-project/BOLETIN) 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: 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: 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": "openrail", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "bertin-project/BOLETIN", "model-index": [{"name": "BOLETIN_16bit_27", "results": []}]} | null | versae/BOLETIN_16bit_27 | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:bertin-project/BOLETIN",
"license:openrail",
"region:us"
] | 2024-02-09T20:17:31+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-bertin-project/BOLETIN #license-openrail #region-us
|
# BOLETIN_16bit_27
This model is a fine-tuned version of bertin-project/BOLETIN 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: 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: 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 | [
"# BOLETIN_16bit_27\n\nThis model is a fine-tuned version of bertin-project/BOLETIN 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: 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: 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/BOLETIN #license-openrail #region-us \n",
"# BOLETIN_16bit_27\n\nThis model is a fine-tuned version of bertin-project/BOLETIN 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: 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: 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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"passage: TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-bertin-project/BOLETIN #license-openrail #region-us \n# BOLETIN_16bit_27\n\nThis model is a fine-tuned version of bertin-project/BOLETIN 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: 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: 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 |
# BRIA Background Removal v1.4 Model Card
RMBG v1.4 is our state-of-the-art background removal model, designed to effectively separate foreground from background in a range of
categories and image types. This model has been trained on a carefully selected dataset, which includes:
general stock images, e-commerce, gaming, and advertising content, making it suitable for commercial use cases powering enterprise content creation at scale.
The accuracy, efficiency, and versatility currently rival leading source-available models.
It is ideal where content safety, legally licensed datasets, and bias mitigation are paramount.
Developed by BRIA AI, RMBG v1.4 is available as a source-available model for non-commercial use.
[CLICK HERE FOR A DEMO](https://huggingface.co/spaces/briaai/BRIA-RMBG-1.4)

### Model Description
- **Developed by:** [BRIA AI](https://bria.ai/)
- **Model type:** Background Removal
- **License:** [bria-rmbg-1.4](https://bria.ai/bria-huggingface-model-license-agreement/)
- The model is released under a Creative Commons license for non-commercial use.
- Commercial use is subject to a commercial agreement with BRIA. [Contact Us](https://bria.ai/contact-us) for more information.
- **Model Description:** BRIA RMBG 1.4 is a saliency segmentation model trained exclusively on a professional-grade dataset.
- **BRIA:** Resources for more information: [BRIA AI](https://bria.ai/)
## Training data
Bria-RMBG model was trained with over 12,000 high-quality, high-resolution, manually labeled (pixel-wise accuracy), fully licensed images.
Our benchmark included balanced gender, balanced ethnicity, and people with different types of disabilities.
For clarity, we provide our data distribution according to different categories, demonstrating our model’s versatility.
### Distribution of images:
| Category | Distribution |
| -----------------------------------| -----------------------------------:|
| Objects only | 45.11% |
| People with objects/animals | 25.24% |
| People only | 17.35% |
| people/objects/animals with text | 8.52% |
| Text only | 2.52% |
| Animals only | 1.89% |
| Category | Distribution |
| -----------------------------------| -----------------------------------------:|
| Photorealistic | 87.70% |
| Non-Photorealistic | 12.30% |
| Category | Distribution |
| -----------------------------------| -----------------------------------:|
| Non Solid Background | 52.05% |
| Solid Background | 47.95%
| Category | Distribution |
| -----------------------------------| -----------------------------------:|
| Single main foreground object | 51.42% |
| Multiple objects in the foreground | 48.58% |
## Qualitative Evaluation

## Architecture
RMBG v1.4 is developed on the [IS-Net](https://github.com/xuebinqin/DIS) enhanced with our unique training scheme and proprietary dataset.
These modifications significantly improve the model’s accuracy and effectiveness in diverse image-processing scenarios.
## Installation
```bash
git clone https://huggingface.co/briaai/RMBG-1.4
cd RMBG-1.4/
pip install -r requirements.txt
```
## Usage
```python
from skimage import io
import torch, os
from PIL import Image
from briarmbg import BriaRMBG
from utilities import preprocess_image, postprocess_image
from huggingface_hub import hf_hub_download
im_path = f"{os.path.dirname(os.path.abspath(__file__))}/example_input.jpg"
net = BriaRMBG()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = BriaRMBG.from_pretrained("briaai/RMBG-1.4")
net.to(device)
net.eval()
# prepare input
model_input_size = [1024,1024]
orig_im = io.imread(im_path)
orig_im_size = orig_im.shape[0:2]
image = preprocess_image(orig_im, model_input_size).to(device)
# inference
result=net(image)
# post process
result_image = postprocess_image(result[0][0], orig_im_size)
# save result
pil_im = Image.fromarray(result_image)
no_bg_image = Image.new("RGBA", pil_im.size, (0,0,0,0))
orig_image = Image.open(im_path)
no_bg_image.paste(orig_image, mask=pil_im)
no_bg_image.save("example_image_no_bg.png")
``` | {"license": "other", "tags": ["remove background", "background", "background-removal", "Pytorch", "vision", "legal liability"], "license_name": "bria-rmbg-1.4", "license_link": "https://bria.ai/bria-huggingface-model-license-agreement/", "pipeline_tag": "image-to-image", "extra_gated_prompt": "This model weights by BRIA AI can be obtained after a commercial license is agreed upon. Fill in the form below and we reach out to you.", "extra_gated_fields": {"Name": "text", "Company/Org name": "text", "Org Type (Early/Growth Startup, Enterprise, Academy)": "text", "Role": "text", "Country": "text", "Email": "text", "By submitting this form, I agree to BRIA\u2019s Privacy policy and Terms & conditions, see links below": "checkbox"}} | image-to-image | JCTN/RMBG-1.4 | [
"pytorch",
"onnx",
"remove background",
"background",
"background-removal",
"Pytorch",
"vision",
"legal liability",
"image-to-image",
"license:other",
"region:us"
] | 2024-02-09T20:17:57+00:00 | [] | [] | TAGS
#pytorch #onnx #remove background #background #background-removal #Pytorch #vision #legal liability #image-to-image #license-other #region-us
| BRIA Background Removal v1.4 Model Card
=======================================
RMBG v1.4 is our state-of-the-art background removal model, designed to effectively separate foreground from background in a range of
categories and image types. This model has been trained on a carefully selected dataset, which includes:
general stock images, e-commerce, gaming, and advertising content, making it suitable for commercial use cases powering enterprise content creation at scale.
The accuracy, efficiency, and versatility currently rival leading source-available models.
It is ideal where content safety, legally licensed datasets, and bias mitigation are paramount.
Developed by BRIA AI, RMBG v1.4 is available as a source-available model for non-commercial use.
CLICK HERE FOR A DEMO
!examples
### Model Description
* Developed by: BRIA AI
* Model type: Background Removal
* License: bria-rmbg-1.4
+ The model is released under a Creative Commons license for non-commercial use.
+ Commercial use is subject to a commercial agreement with BRIA. Contact Us for more information.
* Model Description: BRIA RMBG 1.4 is a saliency segmentation model trained exclusively on a professional-grade dataset.
* BRIA: Resources for more information: BRIA AI
Training data
-------------
Bria-RMBG model was trained with over 12,000 high-quality, high-resolution, manually labeled (pixel-wise accuracy), fully licensed images.
Our benchmark included balanced gender, balanced ethnicity, and people with different types of disabilities.
For clarity, we provide our data distribution according to different categories, demonstrating our model’s versatility.
### Distribution of images:
Qualitative Evaluation
----------------------
!examples
Architecture
------------
RMBG v1.4 is developed on the IS-Net enhanced with our unique training scheme and proprietary dataset.
These modifications significantly improve the model’s accuracy and effectiveness in diverse image-processing scenarios.
Installation
------------
Usage
-----
| [
"### Model Description\n\n\n* Developed by: BRIA AI\n* Model type: Background Removal\n* License: bria-rmbg-1.4\n\n\n\t+ The model is released under a Creative Commons license for non-commercial use.\n\t+ Commercial use is subject to a commercial agreement with BRIA. Contact Us for more information.\n* Model Description: BRIA RMBG 1.4 is a saliency segmentation model trained exclusively on a professional-grade dataset.\n* BRIA: Resources for more information: BRIA AI\n\n\nTraining data\n-------------\n\n\nBria-RMBG model was trained with over 12,000 high-quality, high-resolution, manually labeled (pixel-wise accuracy), fully licensed images.\nOur benchmark included balanced gender, balanced ethnicity, and people with different types of disabilities.\nFor clarity, we provide our data distribution according to different categories, demonstrating our model’s versatility.",
"### Distribution of images:\n\n\n\n\n\n\nQualitative Evaluation\n----------------------\n\n\n!examples\n\n\nArchitecture\n------------\n\n\nRMBG v1.4 is developed on the IS-Net enhanced with our unique training scheme and proprietary dataset.\nThese modifications significantly improve the model’s accuracy and effectiveness in diverse image-processing scenarios.\n\n\nInstallation\n------------\n\n\nUsage\n-----"
] | [
"TAGS\n#pytorch #onnx #remove background #background #background-removal #Pytorch #vision #legal liability #image-to-image #license-other #region-us \n",
"### Model Description\n\n\n* Developed by: BRIA AI\n* Model type: Background Removal\n* License: bria-rmbg-1.4\n\n\n\t+ The model is released under a Creative Commons license for non-commercial use.\n\t+ Commercial use is subject to a commercial agreement with BRIA. Contact Us for more information.\n* Model Description: BRIA RMBG 1.4 is a saliency segmentation model trained exclusively on a professional-grade dataset.\n* BRIA: Resources for more information: BRIA AI\n\n\nTraining data\n-------------\n\n\nBria-RMBG model was trained with over 12,000 high-quality, high-resolution, manually labeled (pixel-wise accuracy), fully licensed images.\nOur benchmark included balanced gender, balanced ethnicity, and people with different types of disabilities.\nFor clarity, we provide our data distribution according to different categories, demonstrating our model’s versatility.",
"### Distribution of images:\n\n\n\n\n\n\nQualitative Evaluation\n----------------------\n\n\n!examples\n\n\nArchitecture\n------------\n\n\nRMBG v1.4 is developed on the IS-Net enhanced with our unique training scheme and proprietary dataset.\nThese modifications significantly improve the model’s accuracy and effectiveness in diverse image-processing scenarios.\n\n\nInstallation\n------------\n\n\nUsage\n-----"
] | [
49,
202,
78
] | [
"passage: TAGS\n#pytorch #onnx #remove background #background #background-removal #Pytorch #vision #legal liability #image-to-image #license-other #region-us \n### Model Description\n\n\n* Developed by: BRIA AI\n* Model type: Background Removal\n* License: bria-rmbg-1.4\n\n\n\t+ The model is released under a Creative Commons license for non-commercial use.\n\t+ Commercial use is subject to a commercial agreement with BRIA. Contact Us for more information.\n* Model Description: BRIA RMBG 1.4 is a saliency segmentation model trained exclusively on a professional-grade dataset.\n* BRIA: Resources for more information: BRIA AI\n\n\nTraining data\n-------------\n\n\nBria-RMBG model was trained with over 12,000 high-quality, high-resolution, manually labeled (pixel-wise accuracy), fully licensed images.\nOur benchmark included balanced gender, balanced ethnicity, and people with different types of disabilities.\nFor clarity, we provide our data distribution according to different categories, demonstrating our model’s versatility.### Distribution of images:\n\n\n\n\n\n\nQualitative Evaluation\n----------------------\n\n\n!examples\n\n\nArchitecture\n------------\n\n\nRMBG v1.4 is developed on the IS-Net enhanced with our unique training scheme and proprietary dataset.\nThese modifications significantly improve the model’s accuracy and effectiveness in diverse image-processing scenarios.\n\n\nInstallation\n------------\n\n\nUsage\n-----"
] | [
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null | null | espnet |
# reazonspeech-espnet-v1
`reazonspeech-espnet-v1` es un modelo de reconocimiento automático del habla (ASR) entrenado con espnet2 para el español ecuatoriano. Este modelo tiene como objetivo reconocer el habla de diferentes regiones y acentos del Ecuador, usando un corpus propio y el corpus de Common Voice. El modelo usa una arquitectura de transformador con codificación por subpalabras (BPE). El modelo alcanza un WER de X% y un MOS de Y en el conjunto de datos de prueba. Para más detalles sobre el modelo, puedes consultar este artículo.
| {"language": ["es"], "license": "apache-2.0", "library_name": "espnet", "tags": ["automatic-speech-recognition", "speech", "espnet", "spanish"]} | automatic-speech-recognition | Dallyana/ASR_espnet_model2 | [
"espnet",
"automatic-speech-recognition",
"speech",
"spanish",
"es",
"license:apache-2.0",
"region:us"
] | 2024-02-09T20:20:14+00:00 | [] | [
"es"
] | TAGS
#espnet #automatic-speech-recognition #speech #spanish #es #license-apache-2.0 #region-us
|
# reazonspeech-espnet-v1
'reazonspeech-espnet-v1' es un modelo de reconocimiento automático del habla (ASR) entrenado con espnet2 para el español ecuatoriano. Este modelo tiene como objetivo reconocer el habla de diferentes regiones y acentos del Ecuador, usando un corpus propio y el corpus de Common Voice. El modelo usa una arquitectura de transformador con codificación por subpalabras (BPE). El modelo alcanza un WER de X% y un MOS de Y en el conjunto de datos de prueba. Para más detalles sobre el modelo, puedes consultar este artículo.
| [
"# reazonspeech-espnet-v1\n\n'reazonspeech-espnet-v1' es un modelo de reconocimiento automático del habla (ASR) entrenado con espnet2 para el español ecuatoriano. Este modelo tiene como objetivo reconocer el habla de diferentes regiones y acentos del Ecuador, usando un corpus propio y el corpus de Common Voice. El modelo usa una arquitectura de transformador con codificación por subpalabras (BPE). El modelo alcanza un WER de X% y un MOS de Y en el conjunto de datos de prueba. Para más detalles sobre el modelo, puedes consultar este artículo."
] | [
"TAGS\n#espnet #automatic-speech-recognition #speech #spanish #es #license-apache-2.0 #region-us \n",
"# reazonspeech-espnet-v1\n\n'reazonspeech-espnet-v1' es un modelo de reconocimiento automático del habla (ASR) entrenado con espnet2 para el español ecuatoriano. Este modelo tiene como objetivo reconocer el habla de diferentes regiones y acentos del Ecuador, usando un corpus propio y el corpus de Common Voice. El modelo usa una arquitectura de transformador con codificación por subpalabras (BPE). El modelo alcanza un WER de X% y un MOS de Y en el conjunto de datos de prueba. Para más detalles sobre el modelo, puedes consultar este artículo."
] | [
36,
135
] | [
"passage: TAGS\n#espnet #automatic-speech-recognition #speech #spanish #es #license-apache-2.0 #region-us \n# reazonspeech-espnet-v1\n\n'reazonspeech-espnet-v1' es un modelo de reconocimiento automático del habla (ASR) entrenado con espnet2 para el español ecuatoriano. Este modelo tiene como objetivo reconocer el habla de diferentes regiones y acentos del Ecuador, usando un corpus propio y el corpus de Common Voice. El modelo usa una arquitectura de transformador con codificación por subpalabras (BPE). El modelo alcanza un WER de X% y un MOS de Y en el conjunto de datos de prueba. Para más detalles sobre el modelo, puedes consultar este artículo."
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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 | sokpearoun/mistralai-Code-Instruct-Finetune-test | [
"transformers",
"safetensors",
"falcon",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T20:21:09+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #falcon #text-generation #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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BibTeX:
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #falcon #text-generation #custom_code #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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"passage: TAGS\n#transformers #safetensors #falcon #text-generation #custom_code #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 | 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. -->
# mistral-7b-localmentor-top1k-neft
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: 1.41e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.1
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "mistral-7b-localmentor-top1k-neft", "results": []}]} | null | salma-remyx/mistral-7b-localmentor-top1k-neft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | 2024-02-09T20:22:30+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us
|
# mistral-7b-localmentor-top1k-neft
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: 1.41e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.1
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0 | [
"# mistral-7b-localmentor-top1k-neft\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: 1.41e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 2\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.36.1\n- Pytorch 2.1.0+cu118\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n",
"# mistral-7b-localmentor-top1k-neft\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: 1.41e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 2\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.36.1\n- Pytorch 2.1.0+cu118\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
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114,
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39
] | [
"passage: TAGS\n#peft #safetensors #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n# mistral-7b-localmentor-top1k-neft\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: 1.41e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 2\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.36.1\n- Pytorch 2.1.0+cu118\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
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] |
null | null | transformers |
# NeuralTrix-V2
NeuralTrix-V2 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/OmniBeagle-7B](https://huggingface.co/mlabonne/OmniBeagle-7B)
* [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3)
* [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo)
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
# no parameters necessary for base model
- model: mlabonne/OmniBeagle-7B
parameters:
density: 0.65
weight: 0.40
- model: flemmingmiguel/MBX-7B-v3
parameters:
density: 0.60
weight: 0.35
- model: CultriX/NeuralTrix-7B-dpo
parameters:
density: 0.60
weight: 0.35
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "CultriX/NeuralTrix-V2"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "mlabonne/OmniBeagle-7B", "flemmingmiguel/MBX-7B-v3", "CultriX/NeuralTrix-7B-dpo"], "base_model": ["mlabonne/OmniBeagle-7B", "flemmingmiguel/MBX-7B-v3", "CultriX/NeuralTrix-7B-dpo"]} | text-generation | CultriX/NeuralTrix-V2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"mlabonne/OmniBeagle-7B",
"flemmingmiguel/MBX-7B-v3",
"CultriX/NeuralTrix-7B-dpo",
"base_model:mlabonne/OmniBeagle-7B",
"base_model:flemmingmiguel/MBX-7B-v3",
"base_model:CultriX/NeuralTrix-7B-dpo",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T20:33:08+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/OmniBeagle-7B #flemmingmiguel/MBX-7B-v3 #CultriX/NeuralTrix-7B-dpo #base_model-mlabonne/OmniBeagle-7B #base_model-flemmingmiguel/MBX-7B-v3 #base_model-CultriX/NeuralTrix-7B-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# NeuralTrix-V2
NeuralTrix-V2 is a merge of the following models using LazyMergekit:
* mlabonne/OmniBeagle-7B
* flemmingmiguel/MBX-7B-v3
* CultriX/NeuralTrix-7B-dpo
## Configuration
## Usage
| [
"# NeuralTrix-V2\n\nNeuralTrix-V2 is a merge of the following models using LazyMergekit:\n* mlabonne/OmniBeagle-7B\n* flemmingmiguel/MBX-7B-v3\n* CultriX/NeuralTrix-7B-dpo",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/OmniBeagle-7B #flemmingmiguel/MBX-7B-v3 #CultriX/NeuralTrix-7B-dpo #base_model-mlabonne/OmniBeagle-7B #base_model-flemmingmiguel/MBX-7B-v3 #base_model-CultriX/NeuralTrix-7B-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# NeuralTrix-V2\n\nNeuralTrix-V2 is a merge of the following models using LazyMergekit:\n* mlabonne/OmniBeagle-7B\n* flemmingmiguel/MBX-7B-v3\n* CultriX/NeuralTrix-7B-dpo",
"## Configuration",
"## Usage"
] | [
150,
68,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/OmniBeagle-7B #flemmingmiguel/MBX-7B-v3 #CultriX/NeuralTrix-7B-dpo #base_model-mlabonne/OmniBeagle-7B #base_model-flemmingmiguel/MBX-7B-v3 #base_model-CultriX/NeuralTrix-7B-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# NeuralTrix-V2\n\nNeuralTrix-V2 is a merge of the following models using LazyMergekit:\n* mlabonne/OmniBeagle-7B\n* flemmingmiguel/MBX-7B-v3\n* CultriX/NeuralTrix-7B-dpo## Configuration## Usage"
] | [
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null | null | transformers |
# Model Card for llama-2-7b-chat-finetuned-guanaco
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This model is a quantized version of the meta-llama/Llama-2-7b-chat-hf model. The model was quantized using NF4. The model was fine-tuned using the dataset timdettmers/openassistant-guanaco
- **Developed by:** Ted Whooley
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** llama
- **Language(s) (NLP):** en
- **License:** other
- **Finetuned from model [optional]:** meta-llama/Llama-2-7b-chat-hf
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## 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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### 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]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Factors
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#### Metrics
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[More Information Needed]
### Results
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#### Summary
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
[More Information Needed]
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[More Information Needed]
#### Software
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## Citation [optional]
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**BibTeX:**
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| {"language": "en", "license": "other", "tags": ["facebook", "meta", "NF4", "llama-2", "llama"], "datasets": ["twhoool02/guanaco-llama2"], "model_name": "llama-2-7b-chat-finetuned-guanaco", "base_model": "meta-llama/Llama-2-7b-chat-hf", "library": ["Transformers", "NF4"], "arxiv": "https://arxiv.org/abs/2305.14314", "model_type": "llama", "pipeline_tag": "text-generation", "qunatized_by": "twhoool02"} | text-generation | twhoool02/llama-2-7b-chat-finetuned-guanaco | [
"transformers",
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"text-generation",
"facebook",
"meta",
"NF4",
"llama-2",
"conversational",
"en",
"dataset:twhoool02/guanaco-llama2",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T20:33:38+00:00 | [
"1910.09700"
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"en"
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#transformers #safetensors #llama #text-generation #facebook #meta #NF4 #llama-2 #conversational #en #dataset-twhoool02/guanaco-llama2 #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-chat-hf #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for llama-2-7b-chat-finetuned-guanaco
## Model Details
### Model Description
This model is a quantized version of the meta-llama/Llama-2-7b-chat-hf model. The model was quantized using NF4. The model was fine-tuned using the dataset timdettmers/openassistant-guanaco
- Developed by: Ted Whooley
- Funded by [optional]:
- Shared by [optional]:
- Model type: llama
- Language(s) (NLP): en
- License: other
- Finetuned from model [optional]: meta-llama/Llama-2-7b-chat-hf
### 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
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] |
null | null | transformers | # SOLAR-10.7B-Instruct-v1.0-laser
This version of Solar-10.7B was lasered and perplexity was calculated against gsm8k.
+ Initial Model Perplexity: 12.865185737609863
+ New baseline perplexity: 12.554274559020996
The laser process decreased perplexity by 2.41%
| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|-----------------------------------------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[SOLAR-10.7B-Instruct-v1.0-laser](https://huggingface.co/macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser)| 46.9| 74.99| 70.64| 43.74| 59.07|
### AGIEval
| Task |Version| Metric |Value| |Stderr|
|------------------------------|------:|--------|----:|---|-----:|
|agieval_aqua_rat | 0|acc |29.53|± | 2.87|
| | |acc_norm|28.35|± | 2.83|
|agieval_logiqa_en | 0|acc |39.78|± | 1.92|
| | |acc_norm|40.55|± | 1.93|
|agieval_lsat_ar | 0|acc |23.04|± | 2.78|
| | |acc_norm|21.30|± | 2.71|
|agieval_lsat_lr | 0|acc |51.18|± | 2.22|
| | |acc_norm|51.76|± | 2.21|
|agieval_lsat_rc | 0|acc |66.54|± | 2.88|
| | |acc_norm|66.91|± | 2.87|
|agieval_sat_en | 0|acc |78.16|± | 2.89|
| | |acc_norm|78.16|± | 2.89|
|agieval_sat_en_without_passage| 0|acc |50.97|± | 3.49|
| | |acc_norm|50.00|± | 3.49|
|agieval_sat_math | 0|acc |42.73|± | 3.34|
| | |acc_norm|38.18|± | 3.28|
Average: 46.9%
### GPT4All
| Task |Version| Metric |Value| |Stderr|
|-------------|------:|--------|----:|---|-----:|
|arc_challenge| 0|acc |60.84|± | 1.43|
| | |acc_norm|63.99|± | 1.40|
|arc_easy | 0|acc |83.59|± | 0.76|
| | |acc_norm|81.44|± | 0.80|
|boolq | 1|acc |87.58|± | 0.58|
|hellaswag | 0|acc |68.11|± | 0.47|
| | |acc_norm|85.77|± | 0.35|
|openbookqa | 0|acc |35.40|± | 2.14|
| | |acc_norm|48.40|± | 2.24|
|piqa | 0|acc |80.58|± | 0.92|
| | |acc_norm|80.74|± | 0.92|
|winogrande | 0|acc |77.03|± | 1.18|
Average: 74.99%
### TruthfulQA
| Task |Version|Metric|Value| |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc| 1|mc1 |55.45|± | 1.74|
| | |mc2 |70.64|± | 1.49|
Average: 70.64%
### Bigbench
| Task |Version| Metric |Value| |Stderr|
|------------------------------------------------|------:|---------------------|----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|57.37|± | 3.60|
|bigbench_date_understanding | 0|multiple_choice_grade|62.87|± | 2.52|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|35.66|± | 2.99|
|bigbench_geometric_shapes | 0|multiple_choice_grade|33.15|± | 2.49|
| | |exact_str_match | 0.00|± | 0.00|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|26.20|± | 1.97|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|19.71|± | 1.50|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|45.00|± | 2.88|
|bigbench_movie_recommendation | 0|multiple_choice_grade|39.00|± | 2.18|
|bigbench_navigate | 0|multiple_choice_grade|51.20|± | 1.58|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|53.90|± | 1.11|
|bigbench_ruin_names | 0|multiple_choice_grade|40.18|± | 2.32|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|39.98|± | 1.55|
|bigbench_snarks | 0|multiple_choice_grade|63.54|± | 3.59|
|bigbench_sports_understanding | 0|multiple_choice_grade|68.36|± | 1.48|
|bigbench_temporal_sequences | 0|multiple_choice_grade|65.20|± | 1.51|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|22.48|± | 1.18|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|18.46|± | 0.93|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|45.00|± | 2.88|
Average: 43.74%
Average score: 59.07%
Elapsed time: 02:33:24 | {"license": "cc-by-nc-2.0"} | text-generation | macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"license:cc-by-nc-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T20:36:53+00:00 | [] | [] | TAGS
#transformers #pytorch #llama #text-generation #conversational #license-cc-by-nc-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| SOLAR-10.7B-Instruct-v1.0-laser
===============================
This version of Solar-10.7B was lasered and perplexity was calculated against gsm8k.
* Initial Model Perplexity: 12.865185737609863
* New baseline perplexity: 12.554274559020996
The laser process decreased perplexity by 2.41%
### AGIEval
Average: 46.9%
### GPT4All
Average: 74.99%
### TruthfulQA
Average: 70.64%
### Bigbench
Average: 43.74%
Average score: 59.07%
Elapsed time: 02:33:24
| [
"### AGIEval\n\n\n\nAverage: 46.9%",
"### GPT4All\n\n\n\nAverage: 74.99%",
"### TruthfulQA\n\n\n\nAverage: 70.64%",
"### Bigbench\n\n\n\nAverage: 43.74%\n\n\nAverage score: 59.07%\n\n\nElapsed time: 02:33:24"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #conversational #license-cc-by-nc-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### AGIEval\n\n\n\nAverage: 46.9%",
"### GPT4All\n\n\n\nAverage: 74.99%",
"### TruthfulQA\n\n\n\nAverage: 70.64%",
"### Bigbench\n\n\n\nAverage: 43.74%\n\n\nAverage score: 59.07%\n\n\nElapsed time: 02:33:24"
] | [
61,
11,
12,
11,
27
] | [
"passage: TAGS\n#transformers #pytorch #llama #text-generation #conversational #license-cc-by-nc-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### AGIEval\n\n\n\nAverage: 46.9%### GPT4All\n\n\n\nAverage: 74.99%### TruthfulQA\n\n\n\nAverage: 70.64%### Bigbench\n\n\n\nAverage: 43.74%\n\n\nAverage score: 59.07%\n\n\nElapsed time: 02:33:24"
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-lora-text-classification
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5131
- Precision: 0.7307
- Recall: 1.0
- F1 and accuracy: {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 and accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:----------------------------------------------------------:|
| No log | 1.0 | 401 | 0.5787 | 0.7307 | 1.0 | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
| 0.5822 | 2.0 | 802 | 0.5722 | 0.7307 | 1.0 | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
| 0.586 | 3.0 | 1203 | 0.5636 | 0.7307 | 1.0 | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
| 0.5504 | 4.0 | 1604 | 0.5513 | 0.7307 | 1.0 | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
| 0.5661 | 5.0 | 2005 | 0.5451 | 0.7307 | 1.0 | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
| 0.5661 | 6.0 | 2406 | 0.5332 | 0.7307 | 1.0 | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
| 0.5562 | 7.0 | 2807 | 0.5329 | 0.7307 | 1.0 | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
| 0.5354 | 8.0 | 3208 | 0.5252 | 0.7307 | 1.0 | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
| 0.5575 | 9.0 | 3609 | 0.5149 | 0.7307 | 1.0 | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
| 0.5301 | 10.0 | 4010 | 0.5131 | 0.7307 | 1.0 | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall"], "base_model": "FacebookAI/xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-lora-text-classification", "results": []}]} | null | nhankins/zh_xlmroberta_lora_adapter_1.0 | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"license:mit",
"region:us"
] | 2024-02-09T20:38:00+00:00 | [] | [] | TAGS
#tensorboard #safetensors #generated_from_trainer #base_model-FacebookAI/xlm-roberta-base #license-mit #region-us
| xlm-roberta-base-lora-text-classification
=========================================
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5131
* Precision: 0.7307
* Recall: 1.0
* F1 and accuracy: {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133}
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-FacebookAI/xlm-roberta-base #license-mit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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-0.1289830505847931,
-0.0946018174290657
] |
null | null | mlx |
# mistral-7b-instruct-v0.1-4bit-ngs
This model was converted to MLX format from [`mistralai/mistral-7b-instruct-v0.1`]().
Refer to the [original model card](https://huggingface.co/mistralai/mistral-7b-instruct-v0.1) for more details on the model.
## Use with mlx
```bash
pip install mlx
git clone https://github.com/ml-explore/mlx-examples.git
cd mlx-examples/llms/hf_llm
python generate.py --model mlx-community/mistral-7b-instruct-v0.1-4bit-ngs --prompt "My name is"
```
| {"license": "apache-2.0", "tags": ["finetuned", "mlx"], "pipeline_tag": "text-generation", "inference": false} | text-generation | mlx-community/mistral-7b-instruct-v0.1-4bit-ngs | [
"mlx",
"safetensors",
"mistral",
"finetuned",
"text-generation",
"conversational",
"license:apache-2.0",
"region:us"
] | 2024-02-09T20:38:24+00:00 | [] | [] | TAGS
#mlx #safetensors #mistral #finetuned #text-generation #conversational #license-apache-2.0 #region-us
|
# mistral-7b-instruct-v0.1-4bit-ngs
This model was converted to MLX format from ['mistralai/mistral-7b-instruct-v0.1']().
Refer to the original model card for more details on the model.
## Use with mlx
| [
"# mistral-7b-instruct-v0.1-4bit-ngs\nThis model was converted to MLX format from ['mistralai/mistral-7b-instruct-v0.1']().\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
"TAGS\n#mlx #safetensors #mistral #finetuned #text-generation #conversational #license-apache-2.0 #region-us \n",
"# mistral-7b-instruct-v0.1-4bit-ngs\nThis model was converted to MLX format from ['mistralai/mistral-7b-instruct-v0.1']().\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
38,
59,
5
] | [
"passage: TAGS\n#mlx #safetensors #mistral #finetuned #text-generation #conversational #license-apache-2.0 #region-us \n# mistral-7b-instruct-v0.1-4bit-ngs\nThis model was converted to MLX format from ['mistralai/mistral-7b-instruct-v0.1']().\nRefer to the original model card for more details on the model.## Use with mlx"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | mtc/meta-llama-Llama-2-13b-hf-pubmed-summarization-5000-last-lora-full-adapter | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
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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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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 |
# Model Card for Model ID
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## Model Details
### Model Description
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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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| {"library_name": "transformers", "tags": []} | text-generation | mtc/meta-llama-Llama-2-13b-hf-pubmed-summarization-5000-last_merged | [
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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:
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### Model Sources [optional]
- Repository:
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- 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.
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Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
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null | null | null |
This is a pre-trained version of Fast FullSubNet, a real-time denoising model trained on the Deep Noise Suppression Challenge dataset of 2020 ([DNS-INTERSPEECH-2020](https://github.com/microsoft/DNS-Challenge/tree/interspeech2020/master)).
## How to run
https://fullsubnet.readthedocs.io/en/latest/usage/getting_started.html
## Code
https://github.com/Audio-WestlakeU/FullSubNet
Note: The code doesn't support real-time streaming out of the box. See [issue-67](https://github.com/Audio-WestlakeU/FullSubNet/issues/67) for details.
## Paper
[Fast FullSubNet: Accelerate Full-band and Sub-band Fusion Model for Single-channel Speech Enhancement](https://arxiv.org/abs/2212.09019), Xiang Hao, Xiaofei Li
> For many speech enhancement applications, a key feature is that system runs on a real-time, latency-sensitive, battery-powered platform, which strictly limits the algorithm latency and computational complexity. In this work, we propose a new architecture named Fast FullSubNet dedicated to accelerating the computation of FullSubNet. Specifically, Fast FullSubNet processes sub-band speech spectra in the mel-frequency domain by using cascaded linear-to-mel full-band, sub-band, and mel-to-linear full-band models such that frequencies involved in the sub-band computation are vastly reduced. After that, a down-sampling operation is proposed for the sub-band input sequence to further reduce the computational complexity along the time axis. Experimental results show that, compared to FullSubNet, Fast FullSubNet has only 13\% computational complexity and 16\% processing time, and achieves comparable or even better performance.
## Performance
| | With Reverb | | | | No Reverb | | |
-- | -- | -- | -- | -- | -- | -- | --
Method | WB-PESQ | NB-PESQ | SI-SDR | STOI | WB-PESQ | NB-PESQ | SI-SDR | STOI
Fast FullSubNet (118 Epochs) | 2.882 | 3.42 | 15.33 | 0.9233 | 2.694 | 3.222 | 16.34 | 0.9571
[FullSubNet (58 Epochs)](https://github.com/Audio-WestlakeU/FullSubNet/releases/tag/v0.2) (just for comparison) | 2.987 | 3.496 | 15.756 | 0.926 | 2.889 | 3.385 | 17.635 | 0.964 | {"license": "mit", "tags": ["denoising", "speech enhancement", "speech separation", "noise suppression", "realtime"], "pipeline_tag": "audio-to-audio"} | audio-to-audio | fronx/Fast-FullSubNet | [
"denoising",
"speech enhancement",
"speech separation",
"noise suppression",
"realtime",
"audio-to-audio",
"arxiv:2212.09019",
"license:mit",
"region:us"
] | 2024-02-09T20:42:41+00:00 | [
"2212.09019"
] | [] | TAGS
#denoising #speech enhancement #speech separation #noise suppression #realtime #audio-to-audio #arxiv-2212.09019 #license-mit #region-us
| This is a pre-trained version of Fast FullSubNet, a real-time denoising model trained on the Deep Noise Suppression Challenge dataset of 2020 (DNS-INTERSPEECH-2020).
How to run
----------
URL
Code
----
URL
Note: The code doesn't support real-time streaming out of the box. See issue-67 for details.
Paper
-----
Fast FullSubNet: Accelerate Full-band and Sub-band Fusion Model for Single-channel Speech Enhancement, Xiang Hao, Xiaofei Li
>
> For many speech enhancement applications, a key feature is that system runs on a real-time, latency-sensitive, battery-powered platform, which strictly limits the algorithm latency and computational complexity. In this work, we propose a new architecture named Fast FullSubNet dedicated to accelerating the computation of FullSubNet. Specifically, Fast FullSubNet processes sub-band speech spectra in the mel-frequency domain by using cascaded linear-to-mel full-band, sub-band, and mel-to-linear full-band models such that frequencies involved in the sub-band computation are vastly reduced. After that, a down-sampling operation is proposed for the sub-band input sequence to further reduce the computational complexity along the time axis. Experimental results show that, compared to FullSubNet, Fast FullSubNet has only 13% computational complexity and 16% processing time, and achieves comparable or even better performance.
>
>
>
Performance
-----------
| [] | [
"TAGS\n#denoising #speech enhancement #speech separation #noise suppression #realtime #audio-to-audio #arxiv-2212.09019 #license-mit #region-us \n"
] | [
50
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null | null | diffusers | # Ava
<Gallery />
## Trigger words
You should use `Ava` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/zzed/AAA/tree/main) them in the Files & versions tab.
| {"tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "best quality, masterpiece, still frame from a horror movie", "parameters": {"negative_prompt": "easynegative, bad-hands-5, [negativebs:0.2], (low quality, worst quality:1.3)"}, "output": {"url": "images/Random_image.png"}}], "base_model": "stablediffusionapi/epicdream", "instance_prompt": "Ava"} | text-to-image | zzed/AAA | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stablediffusionapi/epicdream",
"region:us"
] | 2024-02-09T20:49:38+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stablediffusionapi/epicdream #region-us
| # Ava
<Gallery />
## Trigger words
You should use 'Ava' to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"# Ava\n\n<Gallery />",
"## Trigger words\n\nYou should use 'Ava' 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-stablediffusionapi/epicdream #region-us \n",
"# Ava\n\n<Gallery />",
"## Trigger words\n\nYou should use 'Ava' 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."
] | [
50,
7,
17,
28
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stablediffusionapi/epicdream #region-us \n# Ava\n\n<Gallery />## Trigger words\n\nYou should use 'Ava' 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 |
<!-- 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. -->
# SumCase_3.0
This model is a fine-tuned version of [TheBloke/zephyr-7B-beta-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.1 | {"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "TheBloke/zephyr-7B-beta-GPTQ", "model-index": [{"name": "SumCase_3.0", "results": []}]} | null | Bhagya17/SumCase_3.0 | [
"peft",
"safetensors",
"mistral",
"trl",
"sft",
"generated_from_trainer",
"base_model:TheBloke/zephyr-7B-beta-GPTQ",
"license:mit",
"4-bit",
"region:us"
] | 2024-02-09T20:50:21+00:00 | [] | [] | TAGS
#peft #safetensors #mistral #trl #sft #generated_from_trainer #base_model-TheBloke/zephyr-7B-beta-GPTQ #license-mit #4-bit #region-us
|
# SumCase_3.0
This model is a fine-tuned version of TheBloke/zephyr-7B-beta-GPTQ on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.1 | [
"# SumCase_3.0\n\nThis model is a fine-tuned version of TheBloke/zephyr-7B-beta-GPTQ on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.2\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #safetensors #mistral #trl #sft #generated_from_trainer #base_model-TheBloke/zephyr-7B-beta-GPTQ #license-mit #4-bit #region-us \n",
"# SumCase_3.0\n\nThis model is a fine-tuned version of TheBloke/zephyr-7B-beta-GPTQ on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.2\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
57,
37,
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"passage: TAGS\n#peft #safetensors #mistral #trl #sft #generated_from_trainer #base_model-TheBloke/zephyr-7B-beta-GPTQ #license-mit #4-bit #region-us \n# SumCase_3.0\n\nThis model is a fine-tuned version of TheBloke/zephyr-7B-beta-GPTQ on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.2\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | Americo/phi2-finetued-farma-2epochs | [
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T20:56:07+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #phi #text-generation #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Uses",
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"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #phi #text-generation #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #phi #text-generation #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | null |
# LGM
This model contains the pretrained weights for *LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation*.
- https://huggingface.co/ashawkey/LGM/tree/main
### [Project Page](https://me.kiui.moe/lgm/) | [Arxiv](https://arxiv.org/abs/2402.05054) | [Weights](https://huggingface.co/ashawkey/LGM)
## Introduction
LGM can generate 3D objects from image or text within 5 seconds at high-resolution based on Gaussian Splatting.
<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/63367f9a9895307563659be6/9CVJZ5ZXkhheDPKl3M0pM.mp4"></video>
<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/63367f9a9895307563659be6/6DM_hNEDLRJOz95pgVjek.mp4"></video>
## Model Details
The model is trained on a ~80K subset of [Objaverse](https://huggingface.co/datasets/allenai/objaverse).
For more details, please refer to our paper.
## Usage
To download the model:
```python
from huggingface_hub import hf_hub_download
ckpt_path = hf_hub_download(repo_id="ashawkey/LGM", filename="model_fp16.safetensors")
```
Please refer to our [repo](https://github.com/3DTopia/LGM) for more details on loading and inference.
## Citation
```
@article{tang2024lgm,
title={LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation},
author={Tang, Jiaxiang and Chen, Zhaoxi and Chen, Xiaokang and Wang, Tengfei and Zeng, Gang and Liu, Ziwei},
journal={arXiv preprint arXiv:2402.05054},
year={2024}
}
```
| {"license": "mit", "tags": ["image-to-3d", "text-to-3d"], "pipeline_tag": "text-to-3d"} | text-to-3d | JCTN/LGM | [
"safetensors",
"image-to-3d",
"text-to-3d",
"arxiv:2402.05054",
"license:mit",
"region:us"
] | 2024-02-09T20:56:27+00:00 | [
"2402.05054"
] | [] | TAGS
#safetensors #image-to-3d #text-to-3d #arxiv-2402.05054 #license-mit #region-us
|
# LGM
This model contains the pretrained weights for *LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation*.
- URL
### Project Page | Arxiv | Weights
## Introduction
LGM can generate 3D objects from image or text within 5 seconds at high-resolution based on Gaussian Splatting.
<video controls autoplay src="URL
<video controls autoplay src="URL
## Model Details
The model is trained on a ~80K subset of Objaverse.
For more details, please refer to our paper.
## Usage
To download the model:
Please refer to our repo for more details on loading and inference.
| [
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"### Project Page | Arxiv | Weights",
"## Introduction\nLGM can generate 3D objects from image or text within 5 seconds at high-resolution based on Gaussian Splatting.\n\n<video controls autoplay src=\"URL\n\n<video controls autoplay src=\"URL",
"## Model Details\nThe model is trained on a ~80K subset of Objaverse.\nFor more details, please refer to our paper.",
"## Usage\n\nTo download the model:\n\nPlease refer to our repo for more details on loading and inference."
] | [
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"### Project Page | Arxiv | Weights",
"## Introduction\nLGM can generate 3D objects from image or text within 5 seconds at high-resolution based on Gaussian Splatting.\n\n<video controls autoplay src=\"URL\n\n<video controls autoplay src=\"URL",
"## Model Details\nThe model is trained on a ~80K subset of Objaverse.\nFor more details, please refer to our paper.",
"## Usage\n\nTo download the model:\n\nPlease refer to our repo for more details on loading and inference."
] | [
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"passage: TAGS\n#safetensors #image-to-3d #text-to-3d #arxiv-2402.05054 #license-mit #region-us \n# LGM\n\nThis model contains the pretrained weights for *LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation*.\n\n- URL### Project Page | Arxiv | Weights## Introduction\nLGM can generate 3D objects from image or text within 5 seconds at high-resolution based on Gaussian Splatting.\n\n<video controls autoplay src=\"URL\n\n<video controls autoplay src=\"URL## Model Details\nThe model is trained on a ~80K subset of Objaverse.\nFor more details, please refer to our paper.## Usage\n\nTo download the model:\n\nPlease refer to our repo for more details on loading and inference."
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null | null | transformers | <img src="https://huggingface.co/cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser/resolve/main/fc-dolphin.jpg" width="600" />
by David, Fernando and Eric
Sponsored by: [VAGO Solutions](https://vago-solutions.de) and [HyperSpace.Ai](https://hyperspace.computer/)
Join our Discord! https://discord.gg/cognitivecomputations
A function calling version of [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser)
It follows the implementation of laserRMT @ https://github.com/cognitivecomputations/laserRMT and the novel training technique - we partially freeze the model according to a laser-like analysis (Official Paper soon)
which effectively prevents the significant problem of language models forgetting previously acquired knowledge. This aspect is particularly crucial when attempting to teach the model specific skills, such as function calling.
We intend to be the first of a family of experimentations being carried out @ Cognitive Computations. | {"license": "apache-2.0"} | text-generation | cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T21:00:29+00:00 | [] | [] | TAGS
#transformers #pytorch #mistral #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| <img src="URL width="600" />
by David, Fernando and Eric
Sponsored by: VAGO Solutions and HyperSpace.Ai
Join our Discord! URL
A function calling version of cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
It follows the implementation of laserRMT @ URL and the novel training technique - we partially freeze the model according to a laser-like analysis (Official Paper soon)
which effectively prevents the significant problem of language models forgetting previously acquired knowledge. This aspect is particularly crucial when attempting to teach the model specific skills, such as function calling.
We intend to be the first of a family of experimentations being carried out @ Cognitive Computations. | [] | [
"TAGS\n#transformers #pytorch #mistral #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
54
] | [
"passage: TAGS\n#transformers #pytorch #mistral #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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] |
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