# Fine tuning progress validation - RedPajama 3B, StableLM Alpha 7B, Open-LLaMA

This repository contains the progress of fine-tuning models: RedPajama 3B, StableLM Alpha 7B, Open-LLaMA. These models have been fine-tuned on a specific text dataset and the results of the fine-tuning process are provided in the text file included in this repository.

## Fine-Tuning Details

- **Model: RedPajama 3B, size: 3 billion parameters, method: adapter**
- **Model: StableLM Alpha 7B, size: 7 billion parameters, method: adapter**
- **Model: Open-LLaMA 7B 300B, size: 7 billion parameters (300B tokens), method: LoRA**
- **Model: Open-LLaMA 7B 300B, size: 7 billion parameters (300B tokens), method: adapter**

## Dataset

The text source used for fine-tuning these models has a size of 25MB, which has been split into 174,000 data inputs.

## Fine-Tuning Process

The fine-tuning process was conducted with the following details:

- **Epochs:** 1
- **Validation Frequency:** Every 1% of the training data
- **Training Data:** 174,000 data inputs

## Acknowledgments #1

I would like to acknowledge @stabilityai, @togethercompute and OpenLM Research for providing the base models. Their groundbreaking work in the field of natural language processing has made projects like this possible.

## Acknowledgments #2

I would like to acknowledge @LightningAI for providing the lit-parrot fine-tuning framework.

## Disclaimer

There might be NSFW results in the results.

## License

This repository and the fine-tuned models are licensed under the [MIT License](LICENSE). Feel free to modify and use them according to the terms of the license.
task_categories:text-generation size_categories:n<1K language:en license:mit stories region:us
{"language": ["en"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "pretty_name": "LLM Comparison", "tags": ["stories"]}
TAGS
#task_categories-text-generation #size_categories-n<1K #language-English #license-mit #stories #region-us 

# Fine tuning progress validation - RedPajama 3B, StableLM Alpha 7B, Open-LLaMA

This repository contains the progress of fine-tuning models: RedPajama 3B, StableLM Alpha 7B, Open-LLaMA. These models have been fine-tuned on a specific text dataset and the results of the fine-tuning process are provided in the text file included in this repository.

## Fine-Tuning Details

- Model: RedPajama 3B, size: 3 billion parameters, method: adapter
- Model: StableLM Alpha 7B, size: 7 billion parameters, method: adapter
- Model: Open-LLaMA 7B 300B, size: 7 billion parameters (300B tokens), method: LoRA
- Model: Open-LLaMA 7B 300B, size: 7 billion parameters (300B tokens), method: adapter

## Dataset

The text source used for fine-tuning these models has a size of 25MB, which has been split into 174,000 data inputs.

## Fine-Tuning Process

The fine-tuning process was conducted with the following details:

- Epochs: 1
- Validation Frequency: Every 1% of the training data
- Training Data: 174,000 data inputs

## Acknowledgments #1

I would like to acknowledge @stabilityai, @togethercompute and OpenLM Research for providing the base models. Their groundbreaking work in the field of natural language processing has made projects like this possible.

## Acknowledgments #2

I would like to acknowledge @LightningAI for providing the lit-parrot fine-tuning framework.

## Disclaimer

There might be NSFW results in the results.

## License

This repository and the fine-tuned models are licensed under the MIT License. Feel free to modify and use them according to the terms of the license.
# Fine tuning progress validation - RedPajama 3B, StableLM Alpha 7B, Open-LLaMA

This repository contains the progress of fine-tuning models: RedPajama 3B, StableLM Alpha 7B, Open-LLaMA. These models have been fine-tuned on a specific text dataset and the results of the fine-tuning process are provided in the text file included in this repository. ## Fine-Tuning Details

- Model: RedPajama 3B, size: 3 billion parameters, method: adapter
- Model: StableLM Alpha 7B, size: 7 billion parameters, method: adapter
- Model: Open-LLaMA 7B 300B, size: 7 billion parameters (300B tokens), method: LoRA
- Model: Open-LLaMA 7B 300B, size: 7 billion parameters (300B tokens), method: adapter ## Dataset

The text source used for fine-tuning these models has a size of 25MB, which has been split into 174,000 data inputs. ## Fine-Tuning Process

The fine-tuning process was conducted with the following details:

- Epochs: 1
- Validation Frequency: Every 1% of the training data
- Training Data: 174,000 data inputs ## Acknowledgments #1

I would like to acknowledge @stabilityai, @togethercompute and OpenLM Research for providing the base models. Their groundbreaking work in the field of natural language processing has made projects like this possible. ## Acknowledgments #2

I would like to acknowledge @LightningAI for providing the lit-parrot fine-tuning framework. ## Disclaimer

There might be NSFW results in the results. ## License

This repository and the fine-tuned models are licensed under the MIT License. Feel free to modify and use them according to the terms of the license.
TAGS
#task_categories-text-generation #size_categories-n<1K #language-English #license-mit #stories #region-us 
 # Fine tuning progress validation - RedPajama 3B, StableLM Alpha 7B, Open-LLaMA

This repository contains the progress of fine-tuning models: RedPajama 3B, StableLM Alpha 7B, Open-LLaMA. These models have been fine-tuned on a specific text dataset and the results of the fine-tuning process are provided in the text file included in this repository. ## Fine-Tuning Details

- Model: RedPajama 3B, size: 3 billion parameters, method: adapter
- Model: StableLM Alpha 7B, size: 7 billion parameters, method: adapter
- Model: Open-LLaMA 7B 300B, size: 7 billion parameters (300B tokens), method: LoRA
- Model: Open-LLaMA 7B 300B, size: 7 billion parameters (300B tokens), method: adapter ## Dataset

The text source used for fine-tuning these models has a size of 25MB, which has been split into 174,000 data inputs. ## Fine-Tuning Process

The fine-tuning process was conducted with the following details:

- Epochs: 1
- Validation Frequency: Every 1% of the training data
- Training Data: 174,000 data inputs ## Acknowledgments #1

I would like to acknowledge @stabilityai, @togethercompute and OpenLM Research for providing the base models. Their groundbreaking work in the field of natural language processing has made projects like this possible. ## Acknowledgments #2

I would like to acknowledge @LightningAI for providing the lit-parrot fine-tuning framework. ## Disclaimer

There might be NSFW results in the results. ## License

This repository and the fine-tuned models are licensed under the MIT License. Feel free to modify and use them according to the terms of the license.
passage: TAGS
#task_categories-text-generation #size_categories-n<1K #language-English #license-mit #stories #region-us 
# Fine tuning progress validation - RedPajama 3B, StableLM Alpha 7B, Open-LLaMA

This repository contains the progress of fine-tuning models: RedPajama 3B, StableLM Alpha 7B, Open-LLaMA. These models have been fine-tuned on a specific text dataset and the results of the fine-tuning process are provided in the text file included in this repository.## Fine-Tuning Details

- Model: RedPajama 3B, size: 3 billion parameters, method: adapter
- Model: StableLM Alpha 7B, size: 7 billion parameters, method: adapter
- Model: Open-LLaMA 7B 300B, size: 7 billion parameters (300B tokens), method: LoRA
- Model: Open-LLaMA 7B 300B, size: 7 billion parameters (300B tokens), method: adapter## Dataset

The text source used for fine-tuning these models has a size of 25MB, which has been split into 174,000 data inputs.## Fine-Tuning Process

The fine-tuning process was conducted with the following details:

- Epochs: 1
- Validation Frequency: Every 1% of the training data
- Training Data: 174,000 data inputs## Acknowledgments #1

I would like to acknowledge @stabilityai, @togethercompute and OpenLM Research for providing the base models. Their groundbreaking work in the field of natural language processing has made projects like this possible.## Acknowledgments #2

I would like to acknowledge @LightningAI for providing the lit-parrot fine-tuning framework.## Disclaimer

There might be NSFW results in the results.## License

This repository and the fine-tuned models are licensed under the MIT License. Feel free to modify and use them according to the terms of the license.