LLM-FineTuning-Large-Language-Models

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LLM-FineTuning-Large-Language-Models is an Open Source & Models tool that provides projects and notes on practical techniques for fine-tuning large language models. It offers resources for AI developers and machine learning engineers.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is LLM-FineTuning-Large-Language-Models?

LLM-FineTuning-Large-Language-Models is a comprehensive GitHub repository dedicated to practical techniques and projects for fine-tuning large language models (LLMs). It serves as a valuable resource for AI developers and machine learning engineers seeking to customize and enhance LLMs for specific tasks. The repository includes various fine-tuning examples for models like Llama-2, Mistral, Falcon, and CodeLLaMA, utilizing methods such as PEFT, QLoRA, GPTQ, and DPO. It also covers essential LLM concepts like 4-bit quantization, rotary embeddings, and chat templates, providing both theoretical understanding and practical implementation. This collection aims to bridge the gap between theoretical knowledge and real-world application in LLM development.

Best used for

Ideal for developers and data scientists who need to fine-tune large language models, implement advanced quantization techniques, and develop custom conversational agents. Especially valuable for those looking to apply practical, hands-on methods to enhance LLM performance and build specialized AI applications.

Common actions

fine-tune LLMs

quantize models

implement DPO

develop conversational agents

optimize LLM performance

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Capabilities

Key features

Llama-3 fine-tuning
Mistral 7B fine-tuning
4-bit LLM quantization
DPO training
PEFT and QLoRA implementation
CodeLLaMA conversational agent
Yarn-Llama-2 inference

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of LLMs can be fine-tuned using the techniques in this repository?

This repository provides examples and techniques for fine-tuning a variety of large language models, including Llama-2, Llama-3, Mistral, Falcon, CodeLLaMA, Phi-1.5, and TinyLlama. It covers different architectures and sizes, offering broad applicability for various projects.

Does this repository include resources for model quantization?

Yes, the repository features detailed notes and implementations on model quantization techniques. Specifically, it covers 4-bit LLM quantization with GPTQ and methods for quantizing Hugging Face Transformers models, including Mixtral-8x7B for efficient GPU usage.

Are there examples of advanced fine-tuning methods like DPO and ORPO?

The repository includes practical examples of advanced fine-tuning methods such as Direct Preference Optimization (DPO) for Mistral 7B and Orthogonalized Representation Policy Optimization (ORPO) for Gemma-2b and Llama-3. These techniques are crucial for aligning models with human preferences.

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