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LLM Human Alignment Training Documentation

Table of Contents

Environment Preparation

GPU devices: A10, 3090, V100, A100 are all acceptable. For GPUs with memory <=24G, at least a dual-card environment is required. Since human alignment training loads two models on one card, it occupies more memory than fine-tuning due to an additional inference model's memory consumption.

# Install ms-swift
git clone https://github.com/modelscope/swift.git
cd swift
pip install -e '.[llm]'

# Environment alignment (usually not necessary. If you encounter errors, you can run the following code, the repository uses the latest environment for testing)
pip install -r requirements/framework.txt  -U
pip install -r requirements/llm.txt  -U

Human Alignment Training

The following shell script runs a human alignment training. First, you need to switch to the runtime directory:

cd examples/pytorch/llm

Run the following command:

# Experimental environment: 4*A100
# Memory usage: 4 * 20G, dual-card device_map * 2ddp
nproc_per_node=2

CUDA_VISIBLE_DEVICES=0,1,2,3 \
NPROC_PER_NODE=$nproc_per_node \
MASTER_PORT=29500 \
swift dpo \
    --model_type  yi-6b-chat \
    --ref_model_type  yi-6b-chat \
    --model_revision  master \
    --sft_type  lora \
    --tuner_backend  swift \
    --dtype  AUTO  \
    --output_dir  output  \
    --dataset  hh-rlhf-cn:harmless_base_cn  \
    --num_train_epochs  3  \
    --max_length  1024  \
    --max_prompt_length  512  \
    --check_dataset_strategy  none  \
    --lora_rank  8  \
    --lora_alpha  32  \
    --lora_dropout_p  0.05  \
    --lora_target_modules  ALL  \
    --gradient_checkpointing  true  \
    --batch_size  1  \
    --weight_decay  0.1  \
    --learning_rate  5e-5  \
    --gradient_accumulation_steps  $(expr 16 / $nproc_per_node)  \
    --max_grad_norm  1.0  \
    --warmup_ratio  0.03  \
    --eval_steps  2000  \
    --save_steps  2000  \
    --save_total_limit  2  \
    --logging_steps  10 \

Shell Script

The sh script can be viewed here.

# The following script needs to be executed in this directory
cd examples/pytorch/llm

Tips:

  • We default to setting --gradient_checkpointing true during training to save memory, which will slightly reduce training speed.
  • If you are using older GPUs such as V100, you need to set --dtype AUTO or --dtype fp16, because they do not support bf16.
  • If your machine has high-performance graphics cards like A100 and you are using the qwen series models, we recommend installing flash-attn, which will speed up training and inference as well as reduce memory usage (A10, 3090, V100, etc. graphics cards do not support training with flash-attn). Models that support flash-attn can be viewed in LLM Supported Models
  • If you need to train offline, please use --model_id_or_path <model_dir> and set --check_model_is_latest false. For specific parameter meanings, please see Command Line Arguments.
  • If you want to push weights to the ModelScope Hub during training, you need to set --push_to_hub true.
# dpo training for mistral-7b max_length=1024, bs=1
# Recommended experimental environment: V100, A10, 3090, 2 cards, 4 cards or 8 cards
bash scripts/dpo/lora_ddp_mp/dpo.sh
bash scripts/dpo/lora_ddp_mp/infer.sh

Since DPO training will result in a complete model or adapter weights, the steps for LoRA merging and inference are the same as for fine-tuning, so please refer to the corresponding steps in the Fine-tuning Documentation.