Hello experts! Everyone's experimenting with different strategies for locally synthesizing/deploying large language models. Some prefer 4-bit quantization, while others go the LoRA fine-tuning route. There are even those focusing on model size reduction through distillation methods. Which method do you recommend? Which one delivers the most stable performance on small-scale hardware? Thanks!
What's the best approach for LLM synthesis?
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I started experimenting with a method called LoRA in my projects about three weeks ago. Let me share my firsthand experience. First, I fine-tuned Llama-2-7B using a simple LoRA model, then quantized it to 4-bit and ran it on a CPU. It worked stably even on an old laptop with 16GB RAM, with latency at an acceptable level. Of course, it didn’t perform as well as the full model, but it consumed less memory than the 4-bit quantization methods I’ve been using for years.
I also tried distilled models, which loaded faster on low-end hardware, but the response quality took a serious hit. With LoRA, I found a good balance between memory usage and quality. If your hardware is limited, give LoRA fine-tuning a shot with a lightweight quantized model—you’ll likely find the results satisfying.