di37/finetuning-quantize-evaluate
Fine-Tune, Quantize, Evaluate: The Complete Guide — LLMs, VLMs, and Embedding Models
This project provides a comprehensive guide for AI practitioners looking to tailor advanced AI models for specific tasks. It explains how to adapt large language models (LLMs), vision-language models (VLMs), and embedding models using fine-tuning techniques, how to optimize them for deployment through quantization, and how to rigorously evaluate their performance. You'll gain practical knowledge to take raw models and domain-specific data, and produce highly specialized, efficient, and well-tested AI solutions.
Use this if you need to customize existing large AI models (like LLMs or VLMs) for your unique data or application, make them run faster, and confidently measure their real-world effectiveness.
Not ideal if you are looking for guidance on building AI models from scratch or implementing distributed training across many GPUs.
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Last pushed
Mar 18, 2026
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