环境安装（在安装好cann的容器执行）

git clone https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory
pip install -e ".[torch-npu,metrics]"

# 查看安装的版本
llamafactory-cli env

全流程实践

原始模型推理

cd LLaMA-Factory

# 需要提前下载好模型权重：如/weights/Qwen1.5-0.5B-Chat/ 
ASCEND_RT_VISIBLE_DEVICES=0 llamafactory-cli chat --model_name_or_path /weights/Qwen1.5-0.5B-Chat/  --template qwen

# 模型部署好可以进行对话

基于LORA的sft指令微调

• 创建yaml文件，如qwen.yaml

### model
model_name_or_path: /weights/Qwen1.5-0.5B-Chat

### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj

### ddp（开启deepspeed需要跑多卡）
#ddp_timeout: 180000000
#deepspeed: examples/deepspeed/ds_z0_config.json

### dataset
dataset: identity,alpaca_en_demo
template: qwen
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16

### output
output_dir: saves/Qwen1.5-0.5B-Chat/lora/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true

### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 2
learning_rate: 0.0001
num_train_epochs: 1.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true

### eval
val_size: 0.1
per_device_eval_batch_size: 1
#evaluation_strategy: steps
eval_steps: 500

• 执行微调

cd LLaMA-Factory
ASCEND_RT_VISIBLE_DEVICES=0 llamafactory-cli train qwen1_5_lora_sft_ds.yaml

动态合并LoRA的推理

ASCEND_RT_VISIBLE_DEVICES=0 llamafactory-cli chat --model_name_or_path /weights/Qwen1.5-0.5B-Chat/  --template qwen --adapter_name_or_path saves/Qwen1.5-0.5B-Chat/lora/sft --finetuning_type lora

benchmark评测

llamafactory-cli eval --model_name_or_path /weights/Qwen1.5-0.5B-Chat/ --template fewshot --task mmlu_test --lang en  --n_shot 5 --batch_size 1 --trust_remote_code true