- minimal-llama VS FlexGen
- minimal-llama VS visual-chatgpt
- minimal-llama VS whisper.cpp
- minimal-llama VS simple-llm-finetuner
- minimal-llama VS alpaca-lora
- minimal-llama VS GPTQ-for-LLaMa
- minimal-llama VS text-generation-webui
- minimal-llama VS WebChatRWKVstic
- minimal-llama VS OpenChatKit
- minimal-llama VS dalai
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- Show HN: Finetune LLaMA-7B on commodity GPUs using your own text
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Visual ChatGPT
I can't edit my comment now, but it's 30B that needs 18GB of VRAM.
LLaMA-13B, GPT-3 175B level, only needs 10GB of VRAM with the GPTQ 4bit quantization.
>do you think there's anything left to trim? like weight pruning, or LoRA, or I dunno, some kind of Huffman coding scheme that lets you mix 4-bit, 2-bit and 1-bit quantizations?
Absolutely. The GPTQ paper claims negligible output quality loss with 3-bit quantization. The GPTQ-for-LLaMA repo supports 3-bit quantization and inference. So this extra 25% savings is already possible.
As of right GPTQ-for-LLaMA is using a VRAM hungry attention method. Flash attention will reduce the requirements for 7B to 4GB and possibly fit 30B with a 2048 context window into 16GB, all before stacking 3-bit.
Pruning is a possibility but I'm not aware of anyone working on it yet.
LoRa has already been implemented. See https://github.com/zphang/minimal-llama#peft-fine-tuning-wit...
Stats
Basic minimal-llama repo stats
4
456
8.5
6 months ago
The primary programming language of minimal-llama is Python.
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