3355706e22
* auto gptq support * more tweaks and add yml * remove old gptq docker * don't need explicit peft install for tests * fix setup.py to use extra index url install torch for tests fix cuda version for autogptq index set torch in requirements so that it installs properly move gptq install around to work with github cicd * gptq doesn't play well with sample packing * address pr feedback * remove torch install for now * set quantization_config from model config * Fix the implementation for getting quant config from model config
Overview
This is an example of a llama-2 configuration for 7b and 13b. The yaml file contains configuration for the 7b variant, but you can just aswell use the same settings for 13b.
The 7b variant fits on any 24GB VRAM GPU and will take up about 17 GB of VRAM during training if using qlora and 20 GB if using lora. On a RTX 4090 it trains 3 epochs of the default dataset in about 15 minutes.
The 13b variant will fit if you change these settings to these values: gradient_accumulation_steps: 2 micro_batch_size: 1
accelerate launch scripts/finetune.py examples/llama-2/qlora.yml
or
accelerate launch scripts/finetune.py examples/llama-2/lora.yml