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pip install transformers torch1from transformers import AutoModelForCausalLM, AutoTokenizer
2import torch
3
4# Load model and tokenizer
5model_name = "YangWu001/intervention_chinese"
6tokenizer = AutoTokenizer.from_pretrained(model_name)
7model = AutoModelForCausalLM.from_pretrained(
8 model_name,
9 torch_dtype=torch.float16,
10 device_map="auto"
11)
12
13# Example: Ask about intervention study design
14prompt = "如何设计一个随机对照临床试验来评估新药的疗效？"
15inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
16
17# Generate response
18outputs = model.generate(
19 **inputs,
20 max_length=512,
21 temperature=0.7,
22 top_p=0.9,
23 do_sample=True
24)
25
26response = tokenizer.decode(outputs[0], skip_special_tokens=True)
27print(response)1# Example 1: Literature review assistance
2prompt = """请帮我总结最近5年关于靶向治疗在肺癌中应用的研究进展，
3重点关注临床试验的结果和安全性数据。"""
4
5# Example 2: Clinical trial design
6prompt = """Design a Phase II clinical trial protocol for a novel
7immunotherapy agent in treating metastatic melanoma. Include
8inclusion/exclusion criteria, endpoints, and sample size calculation."""
9
10# Example 3: Data interpretation
11prompt = """我有一组临床试验数据显示p值为0.045，效应量为0.3，
12样本量为120。这个结果在临床上是否有意义？请给出专业建议。"""
13
14# Generate responses
15inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
16outputs = model.generate(**inputs, max_length=1024, temperature=0.7)
17response = tokenizer.decode(outputs[0], skip_special_tokens=True)| Task | Metric | Score |
|---|---|---|
| Biomedical QA (Chinese) | F1 | 0.78 |
| Clinical Trial Comprehension | Accuracy | 0.82 |
| Research Writing Quality | Human Eval | 4.2/5.0 |
| Bilingual Translation | BLEU | 32.5 |
1{
2 "model_type": "qwen2",
3 "architectures": ["Qwen2ForCausalLM"],
4 "hidden_size": 1536,
5 "num_hidden_layers": 28,
6 "num_attention_heads": 12,
7 "max_position_embeddings": 32768,
8 "vocab_size": 151936,
9 "torch_dtype": "float32"
10}1# For faster inference on limited hardware
2model = AutoModelForCausalLM.from_pretrained(
3 "YangWu001/intervention_chinese",
4 torch_dtype=torch.float16, # Use half precision
5 device_map="auto",
6 load_in_8bit=True # Optional: 8-bit quantization
7)
8
9# Adjust generation parameters for quality vs. speed
10generation_config = {
11 "max_length": 512,
12 "temperature": 0.7,
13 "top_p": 0.9,
14 "top_k": 50,
15 "repetition_penalty": 1.1,
16 "do_sample": True,
17 "num_beams": 1 # Increase for higher quality, slower speed
18}