a1da39cd48
* Feat: Update to handle wandb env better * chore: rename wandb_run_id to wandb_name * feat: add new recommendation and update config * fix: indent and pop disabled env if project passed * feat: test env set for wandb and recommendation * feat: update to use wandb_name and allow id * chore: add info to readme
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 -m axolotl.cli.train examples/llama-2/qlora.yml
or
accelerate launch -m axolotl.cli.train examples/llama-2/lora.yml
To launch a full finetuning with 16-bit precision:
accelerate launch -m axolotl.cli.train examples/llama-2/fft_optimized.yml