97 lines
3.2 KiB
Markdown
97 lines
3.2 KiB
Markdown
---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-1.5B
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tags:
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- medical
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- text-generation
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- fine-tuned
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- qa
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metrics:
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- perplexity
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pipeline_tag: text-generation
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---
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# ClinicaQwen-MedQA (1.5B)
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**ClinicaQwen-MedQA** is a lightweight, hyper-specialized medical question-answering assistant. It is built upon the robust `Qwen/Qwen2.5-1.5B` architecture and has undergone a rigorous dual-stage training pipeline to eliminate hallucinations and maximize clinical accuracy while remaining deployable on low-VRAM edge hardware.
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## 🌟 Key Features
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- **Dual-Stage Optimized:** Trained sequentially for deep medical terminology comprehension and strict conversational QA alignment.
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- **Completion-Loss Masked:** Fine-tuned using masked training targets to eliminate prompt mirroring ("echo bugs").
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- **Ultra-Efficient:** Runs seamlessly in native `bfloat16` precision on consumer or serverless GPU instances (e.g., RunPod, vLLM).
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## 📊 Training & Evaluation Performance
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### Stage 1: Medical Knowledge Injection
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- **Dataset:** `gamino/wiki_medical_terms`
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- **Final Evaluation Loss:** 2.0314
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- **Perplexity:** 7.63
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### Stage 2: Conversational QA Alignment
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- **Dataset:** `medalpaca/medical_meadow_medical_flashcards`
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- **Final Evaluation Loss:** 0.9755
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- **Perplexity:** 2.65
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---
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## 🚀 How to Use (Python Example)
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You can load and query **ClinicaQwen-MedQA** directly using the Hugging Face `transformers` library. Ensure your inference script uses low temperatures for maximum medical accuracy.
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### Installation
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```bash
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pip install torch transformers acceleration safetensors
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```
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### Inference Code
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "Mix80/ClinicaQwen-MedQA"
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print("Loading ClinicaQwen-MedQA...")
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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def ask_clinica_qwen(question, max_new_tokens=150):
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# Construct the strict conversational template
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prompt = f"User: {question}\nBot:"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=max_new_tokens,
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temperature=0.1, # Crucial: Low temp ensures strict factual adherence
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top_p=0.85,
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do_sample=True,
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repetition_penalty=1.3, # Prevents linguistic loops
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no_repeat_ngram_size=3,
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pad_token_id=tokenizer.eos_token_id
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)
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# Extract only the generated answer tokens, skipping the prompt prefix
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prompt_length = inputs.input_ids.shape[1]
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generated_tokens = outputs[0][prompt_length:]
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answer = tokenizer.decode(generated_tokens, skip_special_tokens=True).strip()
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return answer
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# Test the model
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sample_query = "What are the key differences between a tension headache and a migraine?"
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response = ask_clinica_qwen(sample_query)
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print(f"\nQuestion: {sample_query}")
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print(f"ClinicaQwen: {response}")
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```
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---
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## ⚠️ Disclaimer
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*ClinicaQwen-MedQA is an AI research model developed for educational and experimental data extraction purposes. It is not a licensed medical tool and should not be used as a substitute for professional clinical diagnosis, advice, or treatment workflows.*
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