Project Introduction to LLM-Finetuning
Overview of the PEFT Fine-Tuning Project
The PEFT (Pretraining-Evaluation Fine-Tuning) project is dedicated to efficiently fine-tuning large language models. This is accomplished using LoRA (Low-Rank Adaptation) and the Hugging Face Transformers library. The project aims to streamline the process of improving language models, making them more effective for specific tasks without the need to retrain from scratch.
Fine-Tuning Resources
The project offers a comprehensive range of informative Jupyter Notebooks, each focused on different aspects of language model fine-tuning. Here is an overview of the resources available:
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Efficiently Train Large Language Models with LoRA and Hugging Face: This notebook provides insights and code to efficiently train large language models using LoRA and the Hugging Face library. It serves as a fundamental guide for those interested in model adaptation.
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Fine-Tune Your Own Llama 2 Model in a Colab Notebook: This guide shows you how to fine-tune a Llama 2 model using Google Colab. It's designed for users looking to adapt an existing model for personal use.
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Guanaco Chatbot Demo with LLaMA-7B Model: This notebook offers a practical demonstration of a chatbot powered by the LLaMA-7B model, illustrating the capabilities of fine-tuned models in interactive applications.
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PEFT Fine-Tune-Bloom-560m-tagger: Details the fine-tuning process for the Bloom-560m-tagger, providing a specialized approach for this particular model.
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Finetune Meta OPT-6-1b Model: Provides guidance on leveraging PEFT and Bloom-560m-tagger to fine-tune the Meta OPT-6-1b Model.
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Finetune Falcon-7b with BNB Self Supervised Training: This section is dedicated to fine-tuning the Falcon-7b model through BNB's self-supervised training techniques.
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FineTune LLaMa2 with QLoRa: Offers instructions for fine-tuning the LLaMa 2 model using QLoRa, expanding on the techniques specific to this adaptation framework.
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Stable Vicuna13B 8bit in Colab: Introduces the fine-tuning process for the Vecuna 13B_8bit model, enabling efficient adjustments tailored to user needs.
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GPT-Neo-X-20B Training: Guides users in training the GPT-NeoX-20B model with bfloat16 precision, enhancing understanding of precision's impact on model performance.
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MPT-Instruct-30B Model Training: Describes how MosaicML's large language model, MPT-Instruct-30B, can be trained to tackle tasks such as instruction following and question answering.
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More notebooks continue in this fashion, covering a broad spectrum of models and methods including custom dataset training, fine-tuning OpenAI's GPT-3.5, and constructing knowledge graphs from textual data.
Collaboration and Contribution
The project is open to contributions. Whether through identifying issues or submitting pull requests, the community's input is welcomed. It provides an opportunity for collaborative development and innovation in the fine-tuning of language models.
Official Licensing
The LLM-Finetuning project is distributed under the MIT License. This open-sourced platform encourages creative freedom while ensuring the project's core principles are protected.
This project has been crafted with dedication by Ashish Patel, as evident in the practical guides and extensive resources offered to the community. Through this project, individuals and teams can explore, innovate, and implement efficient methods for optimizing language models to meet their unique needs.