FineTuningLLMs

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FineTuningLLMs is an Open Source research and education tool that provides a hands-on guide to fine-tuning large language models. It includes code examples and notebooks for practical application.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is FineTuningLLMs?

FineTuningLLMs is the official repository for the book "A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face." This resource offers comprehensive guidance and practical code examples for fine-tuning large language models. It covers essential concepts such as quantization, low-rank adapters (LoRA), and dataset formatting templates. The repository features Jupyter notebooks that can be easily run on Google Colab with GPU support, making it accessible for hands-on learning. It delves into topics like loading quantized models, fine-tuning with SFTTrainer, and deploying models locally using formats like GGUF with Ollama or llama.cpp. The guide is designed for an intermediate-level audience, assuming a foundational understanding of deep learning concepts.

Best used for

Ideal for students, teachers, and professors who need to learn the practical aspects of fine-tuning large language models, experiment with different techniques like LoRA and quantization, and deploy models locally. Especially valuable for those seeking a hands-on guide with code examples using PyTorch and Hugging Face.

Common actions

fine-tune LLMs

learn PyTorch

deploy AI models

understand Hugging Face

practice deep learning

low-code/no-codedeepfakeopen-sourcecollaborationautomated workflowgithub copilotworkflows"AI Agents"face swapping

Capabilities

Key features

Quantization techniques
Low-Rank Adaptation (LoRA)
Dataset formatting templates
SFTTrainer fine-tuning
Local model deployment
PyTorch code examples
Hugging Face integration

Target Audience

studentteacherprofessor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What foundational knowledge is recommended before using FineTuningLLMs?

This guide is for intermediate users. It's recommended to have a solid understanding of Transformers, attention mechanisms, Adam optimizers, tokens, embeddings, and GPUs to fully benefit from the content. A beginner-friendly series on Deep Learning with PyTorch is suggested as a prerequisite.

Can I run the FineTuningLLMs notebooks without a powerful local GPU?

Yes, the notebooks provided in the FineTuningLLMs repository can be easily loaded and run directly from GitHub using Google Colab. This allows you to leverage Google's GPU resources without needing a high-end local setup.

What specific fine-tuning techniques are covered in the book and repository?

The book and repository focus on key concepts for fine-tuning LLMs, including 8-bit and 4-bit quantization, Low-Rank Adaptation (LoRA) using the PEFT package, and various dataset formatting templates. It also covers training with SFTTrainer and local deployment strategies.

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Subcategories trending in Research & Education

Academic Research Knowledge Management Course Creation Scientific Computing Summarization Language Learning

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AI Agents & Automation › AI Frameworks & Infra Coding & Development › Documentation Coding & Development › Open Source & Models

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