Reward modeling Fine tuning

Reward modeling involves training a model to predict the desirability of different outputs based on human feedback. This trained reward model can then guide the fine-tuning of language models, ensuring that their outputs align with human expectations and values.

dataset example

{"prompt": "What are the health benefits of regular exercise?", "chosen": "Regular exercise improves cardiovascular health, strengthens muscles, enhances flexibility, boosts mental health, and aids in weight management.", "rejected": "Regular exercise is good."}

Implementation Steps

1. Data Collection:

   • Gather Human Preferences: Collect datasets where human evaluators have ranked or rated model outputs based on their quality or relevance. For example, pairs of prompts with multiple responses ranked by preference.

2. Training the Reward Model:

   • Model Selection: Choose a base model architecture suitable for regression tasks, such as a transformer-based model.
   • Training Process: Train the model to predict human preference scores from the collected data. This involves minimizing a loss function that measures the difference between the model's predictions and the actual human ratings.

3. Fine-Tuning with Reinforcement Learning:

   • Integration: Use the trained reward model to fine-tune your language model. This can be achieved through reinforcement learning algorithms that optimize the language model's outputs to maximize the reward predicted by the reward model.

Practical Resources

• Code Implementations:

  • The Self-Rewarding Language Model repository provides an implementation of a training framework where a language model is trained to align with its own generated rewards.

• Tutorials:

  • The Reinforcement Learning (DQN) Tutorial by PyTorch offers insights into implementing reinforcement learning algorithms, which can be adapted for fine-tuning language models using reward models.

Gemma2(9b) Llama3-8B-Finetune-and-RAG

This repository contains code for fine-tuning the Llama3 8B model and implementing Retrieval-Augmented Generation (RAG) on the Kaggle platform. Additionally, it includes work with the Gemma2 model, which has 9 billion parameters.

Overview

Llama3-8B-Finetune-and-RAG focuses on fine-tuning the Llama3 model and utilizing RAG for enhanced performance in various tasks. The implementation leverages Kaggle's computational resources and provides Jupyter notebooks for easy replication and adaptation.

What is Llama3 8B?

Llama3 8B is a powerful language model developed by Meta, containing 8 billion parameters. It is designed to understand and generate human-like text, making it useful for a wide range of natural language processing tasks.

What is Retrieval-Augmented Generation (RAG)?

RAG is a technique that combines retrieval-based and generative models to produce more accurate and contextually relevant text. It retrieves relevant documents from a knowledge base and uses this information to generate responses, improving the quality and relevance of the output.

What is Semantic Cache?

Semantic caching is a technique used to store and reuse the results of previous queries to improve the efficiency of data retrieval. In the context of RAG, it helps in quickly accessing relevant information without the need to fetch it repeatedly from the knowledge base, thereby speeding up the generation process.

What is Gemma2 9B?

Gemma2 9B is another advanced language model included in this repository. It has 9 billion parameters, providing even greater capability for understanding and generating text. The inclusion of Gemma2 offers additional options for fine-tuning and implementing RAG.

Features

• Fine-tuning Llama3 8B model.
• Implementing RAG for improved generation tasks.
• Semantic caching for efficient data retrieval.
• Sample code and notebooks for experimentation.
• Integration with Gemma2 9B model for enhanced performance.

Installation

Clone the repository:

git clone https://github.com/Hemanthkumar2112/Llama3-8B-Finetune-and-RAG.git

Usage

1. Navigate to the repository directory.
2. Open the Jupyter notebooks and follow the instructions provided.

Files

• meta-llama-3-8b.ipynb: Notebook for initial setup and configuration.
• meta-llama-3_fine_tune_with_ORPO.ipynb: Notebook for fine-tuning using ORPO.
• meta-llama3-8b-fine-tuning.ipynb: General fine-tuning notebook.
• tamil_llama3-SFT_test_existing_tokenizer.ipynb: Notebook for testing the existing tokenizer.
• gemma2-9b.ipynb: Notebook for working with the Gemma2 9B model.

License

This project is licensed under the Apache-2.0 License. See the LICENSE file for details.

Contributing

Contributions are welcome. Please fork the repository and create a pull request with your changes.

Contact

For any questions or issues, please open an issue on GitHub.

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