Alpaca Lora 4bit

Made some adjust for the code in peft and gptq for llama, and make it possible for lora finetuning with a 4 bits base model. The same adjustment can be made for 2, 3 and 8 bits.

• Install Manual by s4rduk4r: https://github.com/s4rduk4r/alpaca_lora_4bit_readme/blob/main/README.md

Update Logs

• Resolved numerically unstable issue

• Reconstruct fp16 matrix from 4bit data and call torch.matmul largely increased the inference speed.

• Added install script for windows and linux.

• Added Gradient Checkpointing. Now It can finetune 30b model 4bit on a single GPU with 24G VRAM with Gradient Checkpointing enabled. (finetune.py updated) (but would reduce training speed, so if having enough VRAM this option is not needed)

• Added install manual by s4rduk4r

Requirements

gptq-for-llama: https://github.com/qwopqwop200/GPTQ-for-LLaMa
peft: https://github.com/huggingface/peft.git

Install

copy files from GPTQ-for-LLaMa into GPTQ-for-LLaMa path and re-compile cuda extension
copy files from peft/tuners/lora.py to peft path, replace it

Linux:

./install.sh

Windows:

./install.bat

Finetune

The same finetune script from https://github.com/tloen/alpaca-lora can be used.

After installation, this script can be used:

python finetune.py

Inference

After installation, this script can be used:

python inference.py

Text Generation Webui Monkey Patch

Clone the latest version of text generation webui and copy all the files into ./text-generation-webui/

git clone https://github.com/oobabooga/text-generation-webui.git

Open server.py and insert a line at the beginning

import custom_monkey_patch # apply monkey patch
import gc
import io
...

Use the command to run

python server.py