llama-factory

Using Quantization to speed up and slim down your LLM

llm, Python, AI, inference optimization, quantization, bitsandbytes, autogptq, autoround, Deep Learning, deployment, llama-factory
Summary Large Language Models (LLMs) are powerful, but their size can lead to slow inference speeds and high memory consumption, hindering real-world deployment. Quantization, a technique that reduces the precision of model weights, offers a powerful solution. This post will explore how to use quantization techniques like bitsandbytes, AutoGPTQ, and AutoRound to dramatically improve LLM inference performance. What is Quantization? Quantization reduces the computational and storage demands of a model by representing its weights with lower-precision data types.

Mastering LLM Fine-Tuning: A Practical Guide with LLaMA-Factory and LoRA

fine-tuning, llama-factory, lora
Summary Large Language Models (LLMs) offer immense potential, but realizing that potential often requires fine-tuning them on task-specific data. This guide provides a comprehensive overview of LLM fine-tuning, focusing on practical implementation with LLaMA-Factory and the powerful LoRA technique. What is Fine-Tuning? Fine-tuning adapts a pre-trained model to a new, specific task or dataset. It leverages the general knowledge already learned by the model from a massive dataset (source domain) and refines it with a smaller, more specialized dataset (target domain).