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mellee030
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Model Details
Model Provider
mellee030
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Metric | Base | Fine-tuned | Δ |
|---|---|---|---|
| ROUGE-1 | 0.0721 | 0.0452 | -2.68% |
| ROUGE-2 | 0.0265 | 0.0249 | -0.16% |
| ROUGE-L | 0.0697 | 0.0434 | -2.63% |
| BERTScore | 0.5855 | 0.6571 | +7.16% |
BERTScore uses
bert-base-chinese. ROUGE uses character-level tokenization for Chinese. ROUGE decreases because the fine-tuned model learned to give concise, on-format answers rather than verbose outputs — BERTScore (semantic similarity) is the primary metric.
| Model | Dataset | Rank | BERTScore (ft) | Δ |
|---|---|---|---|---|
| MedQwen-1.5B (v2) | 80/10/10 split | r=16 | 0.6575 | +6.57% |
| MedQwen-3B (v2) | 80/10/10 split | r=16 | 0.6582 | +4.93% |
| MedQwen-7B (v2) | 80/10/10 split | r=16 | 0.6571 | +7.16% |
| Parameter | Value |
|---|---|
| Base model | Qwen2.5-7B-Instruct |
| LoRA rank | 16 |
| LoRA alpha | 32 |
| LoRA dropout | 0.05 |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Training samples | 24,531 (80% of 30,664) |
| Validation samples | 3,066 (10%) — used for early stopping |
| Test samples | 3,067 (10%) — held out for final evaluation |
| Max sequence length | 256 |
| Batch size (effective) | 8 |
| Learning rate | 2e-4 |
| LR schedule | Cosine with warmup |
| Epochs | 3 |
| Early stopping | patience=5, eval every 1000 steps |
| Hardware | GCP L4 24GB |
| Precision | FP16 |
python
from transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModelimport torch base_model_id = "Qwen/Qwen2.5-7B-Instruct"adapter_id = "mellee030/MedQwen-7B-LoRA-r16" tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( base_model_id, dtype=torch.float16, device_map="auto", trust_remote_code=True)model = PeftModel.from_pretrained(model, adapter_id)model.eval() messages = [ {"role": "system", "content": "你是一个专业的医疗问答助手，请根据用户的问题给出准确、简洁的医疗建议。"}, {"role": "user", "content": "糖尿病的早期症状有哪些？"},]text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(text, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, do_sample=True)print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))~30,664 Chinese medical Q&A pairs derived from a structured Chinese medical knowledge base containing disease records with fields such as name, description, symptoms, causes, and treatments. Split 80/10/10 with seed=42 for reproducibility.
This model is for research purposes only and should not be used as a substitute for professional medical advice.
from transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModelimport torch base_model_id = "Qwen/Qwen2.5-7B-Instruct"adapter_id = "mellee030/MedQwen-7B-LoRA-r16" tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( base_model_id, dtype=torch.float16, device_map="auto", trust_remote_code=True)model = PeftModel.from_pretrained(model, adapter_id)model.eval() messages = [ {"role": "system", "content": "你是一个专业的医疗问答助手，请根据用户的问题给出准确、简洁的医疗建议。"}, {"role": "user", "content": "糖尿病的早期症状有哪些？"},]text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(text, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, do_sample=True)print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))