Learn to implement and run heavily quantized Large Language Models (LLMs) locally in this comprehensive tutorial that covers multiple quantization techniques including QLoRA, GPTQ, and Llamacpp. Explore the implementation of GPTQ through AutoGPTQ, work with llama.cpp using ggml.c and GGUL in C++, and compare these approaches with Hugging Face transformers in 4-bit quantization. Master the process of downloading and setting up Web UI wrappers for quantized LLMs across different platforms including PC, Linux, and Apple hardware with M1, M2, or M3 chips. Discover eight different Web UI options for GPTQ, llama.cpp, AutoGPTQ, exLLama, and GGUF.c, including popular interfaces like koboldcpp, oobabooga text-generation-webui, and ctransformers. Access essential resources and tools through provided links to repositories, documentation, and platforms such as lmstudio.ai, Google Cloud Model Garden, Hugging Face AutoTrain, and H2O.ai platform.
LLM Quantization Tutorial: QLoRA, GPTQ, and LLama.cpp Implementation for LLama 2
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Overview
Syllabus
New Tutorial on LLM Quantization w/ QLoRA, GPTQ and Llamacpp, LLama 2
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