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Overview

This is a fork of QLoRA

Differences from original

Since I am the creator of the various airoboros models, this fork is made specifically for airoboros, and does slightly differ from the main upstream repo.

airoboros chat support

airoboros datasets starting from 3.0 onwards are using conversational style datasets (e.g. sharegpt)

Add --dataset_format airoboros_chat

This uses llama-2 chat prompt format!

legacy airoboros support

For versions 2.2.1 and earlier

The instructions.jsonl file (or whatever filename you are using), should be a single JSON string per line, newline separated, with "instruction" and "response" values.

Add: --dataset_format airoboros

epochs instead of steps

I prefer using a fixed number of epochs in training rather than trying to stop are a particular step count. I removed the --max_steps parameter in favor of --num_train_epochs (which I usually set to 3)

Full, non-(q)lora fine-tune example

Example used for the llama-2 7b airoboros, version 3.0:

export BASE_DIR=/workspace
export WANDB_API_KEY=[redacted]
export WANDB_PROJECT=airoboros-l2-7b-3.0

torchrun --nnodes=1 --nproc_per_node=7 $BASE_DIR/qlora/train.py \
  --model_name_or_path $BASE_DIR/llama-2-7b-hf \
  --working_dir $BASE_DIR/$WANDB_PROJECT-checkpoints \
  --output_dir $BASE_DIR/$WANDB_PROJECT \
  --num_train_epochs 5 \
  --logging_steps 1 \
  --save_strategy steps \
  --save_steps 15 \
  --save_total_limit 1 \
  --data_seed 11422 \
  --evaluation_strategy steps \
  --eval_dataset_size 0.02 \
  --eval_steps 5 \
  --max_new_tokens 4096 \
  --dataloader_num_workers 3 \
  --logging_strategy steps \
  --optim adamw_torch \
  --do_train \
  --full_finetune \
  --bits 16 \
  --bf16 \
  --dataset $BASE_DIR/conversations.json \
  --dataset_format airoboros_chat \
  --model_max_len 4096 \
  --per_device_train_batch_size 12 \
  --learning_rate 2e-5 \
  --lr_scheduler_type cosine \
  --warmup_ratio 0.005 \
  --weight_decay 0.0 \
  --seed 11422 \
  --report_to wandb \
  --deepspeed deepspeed-7b.json \
  --gradient_checkpointing \
  --use_flash_attention_2

deepspeed-7b.json

{
  "gradient_accumulation_steps": "auto",
  "gradient_clipping": "auto",
  "train_batch_size": "auto",
  "train_micro_batch_size_per_gpu": "auto",
  "bf16": {
    "enabled": true
  },
  "zero_optimization": {
    "stage": 2,
    "contiguous_gradients": true,
    "overlap_comm": true,
    "reduce_scatter": true,
    "reduce_bucket_size": 5e8,
    "allgather_bucket_size": 5e8
  }
}

QLoRA example

Script used for llama-2 70b airoboros, version 3.0:

export BASE_DIR=/workspace
export WANDB_API_KEY=[redacted]
export WANDB_PROJECT=airoboros-l2-70b-3.0

accelerate launch $BASE_DIR/qlora/train.py \
  --model_name_or_path $BASE_DIR/llama-2-70b-hf \
  --working_dir $BASE_DIR/$WANDB_PROJECT-checkpoints \
  --output_dir $BASE_DIR/$WANDB_PROJECT-peft \
  --merged_output_dir $BASE_DIR/$WANDB_PROJECT \
  --num_train_epochs 5 \
  --logging_steps 1 \
  --save_strategy steps \
  --save_steps 75 \
  --save_total_limit 3 \
  --data_seed 11422 \
  --evaluation_strategy steps \
  --per_device_eval_batch_size 2 \
  --eval_dataset_size 0.01 \
  --eval_steps 75 \
  --max_new_tokens 4096 \
  --dataloader_num_workers 3 \
  --logging_strategy steps \
  --do_train \
  --lora_r 64 \
  --lora_alpha 16 \
  --lora_modules all \
  --bf16 \
  --bits 4 \
  --double_quant \
  --quant_type nf4 \
  --lr_scheduler_type constant \
  --dataset $BASE_DIR/conversations.json \
  --dataset_format airoboros_chat \
  --model_max_len 4096 \
  --per_device_train_batch_size 2 \
  --learning_rate 0.00008 \
  --adam_beta2 0.999 \
  --max_grad_norm 0.3 \
  --lora_dropout 0.0 \
  --weight_decay 0.0 \
  --seed 11422 \
  --report_to wandb \
  --gradient_checkpointing \
  --use_flash_attention_2 \
  --ddp_find_unused_parameters False

Requirements for the old, llama-1 models

For the original llama models (not llama-2), I was using one of these:

I replaced special_tokens_map.json and tokenizer_config.json within the base models with the versions found in llama-1-patch in this repo.