kishore-fdi/finetuning-autotrain

This repo fine-tunes the Mistral 7B Sharded model using AutoTrain and QLoRA to create an Customized LLM. It was done with help of PyTorch, Transformers , etc. Contributions welcome! 😊

Fine-Tuning the Mistral 7B Sharded Model with AutoTrain and QLoRA

Welcome to our project! We're working on fine-tuning the Mistral 7B sharded model with a custom dataset and the orca dataset using AutoTrain and QLoRA. This project aims to create a powerful AI model capable of generating high-quality responses in a conversational setting.

Features

• Fine-Tuning: We're using the Mistral 7B sharded model as our base, and fine-tuning it to improve its performance on our specific task.
• AutoTrain: This feature allows us to automate the training process, making it easier to fine-tune our model.
• QLoRA: QLoRA, or Query Likelihood with Out-of-domain Relevance Augmentation, is a technique we're using to improve the relevance of our model's responses.

Technology Stack

Our project uses a variety of technologies to achieve its goals:

• Python: The main language used for developing our project.
• PyTorch: An open-source machine learning library for Python, used for applications such as natural language processing.
• Hugging Face Transformers: A library that provides general-purpose architectures for Natural Language Understanding (NLU) and Natural Language Generation (NLG).

How to Use

1. Prepare Your Dataset: Gather the data you'll be using to fine-tune the model. This could be a collection of dialogues, a list of question-answer pairs, or any other type of conversational data.
2. Fine-Tune the Model: Use the AutoTrain feature to fine-tune the Mistral 7B model on your dataset. This will adjust the model's parameters to better fit your data.
3. Generate Responses: Once the model has been fine-tuned, you can use it to generate responses. Just input a message, and the model will generate a relevant response!

Contributing

We welcome contributions! If you're interested in contributing, here are a few ways you can help:

• Data Collection: Help us gather more data to fine-tune our model.
• Model Training: Assist in the fine-tuning process by training the model on new data.
• Testing and Feedback: Use our model and provide feedback on its performance.

Contact

If you have any questions or suggestions, feel free to reach out to us. We'd love to hear from you!

Disclaimer

This project is for research purposes only. The generated responses are not meant to provide professional advice or recommendations. Always consult with a qualified professional for any serious matters.

Thank you for your interest in our project! We're excited to see what we can achieve together. 😊