Finetune LLMs on your own consumer hardware using tools from PyTorch and Hugging Face ecosystem | PyTorch

We demonstrate how to finetune a 7B parameter model on a typical consumer GPU (NVIDIA T4 16GB) with LoRA and tools from the PyTorch and Hugging Face ecosystem with complete reproducible Google Colab notebook.

We demonstrate how to finetune a 7B parameter model on a typical consumer GPU (NVIDIA T4 16GB) with LoRA and tools from the PyTorch and Hugging Face ecosystem with complete reproducible Google Colab notebook. Introduction Large Language Models (LLMs) have shown impressive capabilities in industrial applications. Often, developers seek to tailor these LLMs for specific use-cases and applications to fine-tune them for better performance. However, LLMs are large by design and require a large number of GPUs to be fine-tuned. Let’s focus on a specific example by trying to fine-tune a Llama model on

related reading

• LoRA Without Regret - Thinking Machines Labthinkingmachines.ai
• Efficient LLM Finetuning with Unsloth | Modal Docsmodal.com
• In-depth guide to fine-tuning LLMs with LoRA and QLoRA | Mercity Researchmercity.ai
• Fine-tuning LLMs Guide | Unsloth Documentationdocs.unsloth.ai
• Parameter-Efficient LLM Finetuning With Low-Rank Adaptation (LoRA) - Lightning AIlightning.ai
• Fine-tuning a LLM on my blog posts | Didier Lopesdidierlopes.com
• Fine-Tuning Llama-2: Tailoring Models to Unique Applicationsanyscale.com
• Accelerating LLaMA with Fabric: A Comprehensive Guide to Training and Fine-Tuning LLaMA - Lightning AIlightning.ai
• The Llama Hitchiking Guide to Local LLMs – hackerllamaosanseviero.github.io
• Tutorial: How to Finetune Llama-3 and Use In Ollama | Unsloth Documentationdocs.unsloth.ai
• GitHub - bobazooba/xllm: 🦖 X—LLM: Cutting Edge & Easy LLM Finetuning · GitHubgithub.com
• A Guide to Quantization in LLMs | Symbl.aisymbl.ai