This tutorial demonstrates how to fine-tune the Llama-2 7B Chat model for Python code generation using QLoRA, gradient checkpointing, and SFTTrainer with the Alpaca-14k dataset.
A comparison of frameworks, models, and costs for deploying Llama models locally and privately.
- Four tools were analyzed: HuggingFace, vLLM, Ollama, and llama.cpp.
- HuggingFace has a wide range of models but struggles with quantized models.
- vLLM is experimental and lacks full support for quantized models.
- Ollama is user-friendly but has some customization limitations.
- llama.cpp is preferred for its performance and customization options.
- The analysis focused on llama.cpp and Ollama, comparing speed and power consumption across different quantizations.
"This is one of the best 13B models I've tested. (for programming, math, logic, etc) speechless-llama2-hermes-orca-platypus-wizardlm-13b"
