模型介绍
Doctor-Qwen2是一个为医疗健康对话场景而打造的领域大模型，该模型基于Qwen2-1.5B-Instruct进行微调得来，使用的数据集是复旦大学数据智能与社会计算实验室开源的DISC-Med-SFT数据集。
模型推理范例
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("模型路径")
model = AutoModelForCausalLM.from_pretrained("模型路径", device_map={"":0})
prompt = "医生您好，我最近睡眠质量很差，晚上经常醒来，而且早上起来也疲惫不堪，我该如何才能改善睡眠质量？"
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
device = 'cuda'
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]
print(response)
数据集预处理
假设原数据集里包括许多一轮对话，每一段对话的格式如下：
{
"instruction": "*********",
"input": "*********",
"output": "*********"
}
from datasets import Dataset
import pandas as pd
def process_func(example):
"""
将数据集进行预处理
"""
MAX_LENGTH = 384
input_ids, attention_mask, labels = [], [], []
instruction = tokenizer(
f"<|im_start|>system\n你是一个医疗领域的专家，你会接收到病人的提问，请输出合适的答案<|im_end|>\n<|im_start|>user\n{example['input']}<|im_end|>\n<|im_start|>assistant\n",
add_special_tokens=False,
)
response = tokenizer(f"{example['output']}", add_special_tokens=False)
input_ids = instruction["input_ids"] + response["input_ids"] + [tokenizer.pad_token_id]
attention_mask = (
instruction["attention_mask"] + response["attention_mask"] + [1]
)
labels = [-100] * len(instruction["input_ids"]) + response["input_ids"] + [tokenizer.pad_token_id]
return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels}
train_df = pd.read_json("原数据集路径", lines=True)
train_ds = Dataset.from_pandas(train_df)
train_dataset = train_ds.map(process_func, remove_columns=train_ds.column_names)
模型微调范例
使用LoRA技术对模型进行微调，以满足特定任务需求。
from trl import SFTTrainer
from peft import LoraConfig, TaskType
from transformers import AutoModelForCausalLM, TrainingArguments, DataCollatorForSeq2Seq, AutoTokenizer
from datasets import load_from_disk
train_dataset = load_from_disk("处理过的数据集路径")
tokenizer = AutoTokenizer.from_pretrained("模型路径")
model = AutoModelForCausalLM.from_pretrained("模型路径", device_map={"":0})
lora_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
inference_mode=False,
r=8,
lora_alpha=32,
lora_dropout=0.1,
)
args = TrainingArguments(
output_dir="自定义输出路径",
per_device_train_batch_size=5,
gradient_accumulation_steps=5,
logging_steps=10,
num_train_epochs=3,
save_steps=100,
learning_rate=1e-4,
save_on_each_node=True,
gradient_checkpointing=True,
report_to="none",
)
trainer = SFTTrainer(
model=model,
args=args,
train_dataset=train_dataset,
data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),
peft_config=lora_config,
)
trainer.train()
评论