README.md

metadata

license: apache-2.0

Slightly modified mpt-30b, which has some updates to allow gradient checkpointing/etc., to be compatible with qlora training code.

Original model: https://huggingface.co/mosaicml/mpt-30b

My fork of qlora with mpt-30b support: https://github.com/jondurbin/qlora

Differences in the qlora scripts:

• requires adding --mpt True for mpt-based models
• uses --num_train_epochs instead of --max_steps
• uses airoboros prompt format (mostly 1:1 with vicuna) rather than alpaca, and expects an input file in JSONL format with "instruction" and "response"

Full example of tuning (used for airoboros-mpt-30b-gpt4-1.4):

source /workspace/venv/bin/activate

export WANDB_API_KEY=[redacted]
export WANDB_PROJECT=airoboros-mpt-30b-gpt4-1.4

python qlora.py \
    --model_name_or_path ./mpt-30b \
    --output_dir ./$WANDB_PROJECT-checkpoints \
    --num_train_epochs 3 \
    --logging_steps 1 \
    --save_strategy steps \
    --data_seed 11422 \
    --save_steps 75 \
    --save_total_limit 3 \
    --evaluation_strategy "no" \
    --eval_dataset_size 2 \
    --max_new_tokens 8192 \
    --dataloader_num_workers 3 \
    --logging_strategy steps \
    --remove_unused_columns False \
    --do_train \
    --lora_r 64 \
    --lora_alpha 16 \
    --lora_modules all \
    --double_quant \
    --quant_type nf4 \
    --bf16 \
    --bits 4 \
    --warmup_ratio 0.03 \
    --lr_scheduler_type constant \
    --dataset ./instructions.jsonl \
    --dataset_format airoboros \
    --model_max_len 8192 \
    --gradient_checkpointing \
    --per_device_train_batch_size 6 \
    --gradient_accumulation_steps 16 \
    --learning_rate 0.0001 \
    --adam_beta2 0.999 \
    --max_grad_norm 0.3 \
    --lora_dropout 0.05 \
    --weight_decay 0.0 \
    --seed 11422 \
    --trust_remote_code \
    --mpt True \
    --report_to wandb