github上下載qwen1_5源碼

修改finetun.sh

然后在路徑qwen1_5/examples/sft下修改finetun.sh, 內容如下

#!/bin/bash
export CUDA_DEVICE_MAX_CONNECTIONS=1
DIR=`pwd`# Guide:
# This script supports distributed training on multi-gpu workers (as well as single-worker training).
# Please set the options below according to the comments.
# For multi-gpu workers training, these options should be manually set for each worker.
# After setting the options, please run the script on each worker.# Number of GPUs per GPU worker
GPUS_PER_NODE=$(python -c 'import torch; print(torch.cuda.device_count())')# Number of GPU workers, for single-worker training, please set to 1
NNODES=${NNODES:-1}# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
NODE_RANK=${NODE_RANK:-0}# The ip address of the rank-0 worker, for single-worker training, please set to localhost
MASTER_ADDR=${MASTER_ADDR:-localhost}# The port for communication
MASTER_PORT=${MASTER_PORT:-6010}MODEL="Qwen/Qwen1.5-7B" # Set the path if you do not want to load from huggingface directly
# ATTENTION: specify the path to your training data, which should be a json file consisting of a list of conversations.
# See the section for finetuning in README for more information.
DATA="path_to_data"
DS_CONFIG_PATH="finetune/ds_config_zero3.json"
USE_LORA=False
Q_LORA=Falsefunction usage() {echo '
Usage: bash finetune/finetune_lora_ds.sh [-m MODEL_PATH] [-d DATA_PATH] [--deepspeed DS_CONFIG_PATH] [--use_lora USE_LORA] [--q_lora Q_LORA]
'
}while [[ "$1" != "" ]]; docase $1 in-m | --model )shiftMODEL=$1;;-d | --data )shiftDATA=$1;;--deepspeed )shiftDS_CONFIG_PATH=$1;;--use_lora  )shiftUSE_LORA=$1;;--q_lora    )shiftQ_LORA=$1;;-h | --help )usageexit 0;;* )echo "Unknown argument ${1}"exit 1;;esacshift
doneDISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE \--nnodes $NNODES \--node_rank $NODE_RANK \--master_addr $MASTER_ADDR \--master_port $MASTER_PORT
"torchrun $DISTRIBUTED_ARGS finetune.py \--model_name_or_path $MODEL \--data_path $DATA \--bf16 True \--output_dir output_qwen \--num_train_epochs 5 \--per_device_train_batch_size 2 \--per_device_eval_batch_size 1 \--gradient_accumulation_steps 8 \--evaluation_strategy "no" \--save_strategy "steps" \--save_steps 10 \--save_total_limit 10 \--learning_rate 3e-4 \--weight_decay 0.01 \--adam_beta2 0.95 \--warmup_ratio 0.01 \--lr_scheduler_type "cosine" \--logging_steps 1 \--report_to "none" \--model_max_length 512 \--lazy_preprocess True \--use_lora ${USE_LORA} \--q_lora ${Q_LORA} \--gradient_checkpointing \--deepspeed ${DS_CONFIG_PATH}

訓練

（在qwen1_5/examples/sft路徑下開個bash里運行finetune.sh，不要在jupyter里跑）

pip install transformers==4.37.0# 要用命令行運行
# 不想用多卡訓練的時候，先 export CUDA_VISIBLE_DEVICE=0
bash finetune.sh -m "/opt/app-root/src/Qwen1.5-14B-Chat" -d "./data/traindata.jsonl" --deepspeed "ds_config_zero3.json" --use_lora True

預測

（在qwen1_5/examples/sft路徑下建個inference.py）

pip install transformers==4.33.0

from transformers import AutoModelForCausalLM, AutoTokenizer
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
device = "cuda" # the device to load the model onto
path = "output_qwen/checkpoint-70"
model = AutoModelForCausalLM.from_pretrained(path,torch_dtype="auto",device_map="cuda:0"
)
tokenizer = AutoTokenizer.from_pretrained(path)def predict_answer(messages):text = tokenizer.apply_chat_template(messages,tokenize=False,add_generation_prompt=True)model_inputs = tokenizer([text], return_tensors="pt").to(device)generated_ids = model.generate(model_inputs.input_ids,max_new_tokens=512,)generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]return responsetext = "xxxx"
messages = [{"role": "user", "content": "我需要起草投標文件中的一個章節，章節內容為：\n\n\n{}\n\n\n\n請將章節內容拆分成多個小節，每個小節覆蓋一個信息點，形成一份本章節的提綱。注意，要覆蓋所有信息點，不要使用‘同上、略’等省略表述，盡可能保持原文的措詞。".format(text)}]
response = predict_answer(messages)
print(response)

訓練數據格式

格式為jsonl，每行一條json，位于qwen1_5/examples/sft/data下，不妨命名為traindata.jsonl

{"type": "chatml", "messages": [{"role": "user", "content": "PROMPT"}, {"role": "assistant", "content": "ANSWER"}], "source": "self-made"}