120 lines
3.9 KiB
Markdown
120 lines
3.9 KiB
Markdown
## 模型介绍
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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](http://fudan-disc.com)开源的[DISC-Med-SFT](https://huggingface.co/datasets/Flmc/DISC-Med-SFT)数据集。
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## 模型推理范例
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("模型路径")
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model = AutoModelForCausalLM.from_pretrained("模型路径", device_map={"":0})
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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？"
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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device = 'cuda'
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model_inputs = tokenizer([text], return_tensors="pt").to(device)
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generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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## 数据集预处理
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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：
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```
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{
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"instruction": "*********",
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"input": "*********",
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"output": "*********"
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}
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```
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```python
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from datasets import Dataset
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import pandas as pd
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def process_func(example):
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"""
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将数据集进行预处理
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"""
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MAX_LENGTH = 384
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input_ids, attention_mask, labels = [], [], []
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instruction = tokenizer(
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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",
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add_special_tokens=False,
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)
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response = tokenizer(f"{example['output']}", add_special_tokens=False)
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input_ids = instruction["input_ids"] + response["input_ids"] + [tokenizer.pad_token_id]
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attention_mask = (
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instruction["attention_mask"] + response["attention_mask"] + [1]
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)
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labels = [-100] * len(instruction["input_ids"]) + response["input_ids"] + [tokenizer.pad_token_id]
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return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels}
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train_df = pd.read_json("原数据集路径", lines=True)
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train_ds = Dataset.from_pandas(train_df)
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train_dataset = train_ds.map(process_func, remove_columns=train_ds.column_names)
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```
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## 模型微调范例
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使用LoRA技术对模型进行微调，以满足特定任务需求。
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```python
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from trl import SFTTrainer
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from peft import LoraConfig, TaskType
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from transformers import AutoModelForCausalLM, TrainingArguments, DataCollatorForSeq2Seq, AutoTokenizer
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from datasets import load_from_disk
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train_dataset = load_from_disk("处理过的数据集路径")
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tokenizer = AutoTokenizer.from_pretrained("模型路径")
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model = AutoModelForCausalLM.from_pretrained("模型路径", device_map={"":0})
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lora_config = LoraConfig(
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task_type=TaskType.CAUSAL_LM,
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target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
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inference_mode=False,
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r=8,
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lora_alpha=32,
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lora_dropout=0.1,
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)
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args = TrainingArguments(
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output_dir="自定义输出路径",
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per_device_train_batch_size=5,
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gradient_accumulation_steps=5,
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logging_steps=10,
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num_train_epochs=3,
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save_steps=100,
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learning_rate=1e-4,
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save_on_each_node=True,
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gradient_checkpointing=True,
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report_to="none",
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)
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trainer = SFTTrainer(
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model=model,
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args=args,
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train_dataset=train_dataset,
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data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),
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peft_config=lora_config,
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)
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trainer.train()
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```
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