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Model: Mix80/ClinicaQwen-MedQA Source: Original Platform
2026-07-24 01:56:11 +08:00
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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-1.5B
+tags:
+- medical
+- text-generation
+- fine-tuned
+- qa
+metrics:
+- perplexity
+pipeline_tag: text-generation
+---
+
+# ClinicaQwen-MedQA (1.5B)
+
+**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.
+
+## 🌟 Key Features
+- **Dual-Stage Optimized:** Trained sequentially for deep medical terminology comprehension and strict conversational QA alignment.
+- **Completion-Loss Masked:** Fine-tuned using masked training targets to eliminate prompt mirroring ("echo bugs").
+- **Ultra-Efficient:** Runs seamlessly in native `bfloat16` precision on consumer or serverless GPU instances (e.g., RunPod, vLLM).
+
+## 📊 Training & Evaluation Performance
+
+### Stage 1: Medical Knowledge Injection
+- **Dataset:** `gamino/wiki_medical_terms`
+- **Final Evaluation Loss:** 2.0314
+- **Perplexity:** 7.63
+
+### Stage 2: Conversational QA Alignment
+- **Dataset:** `medalpaca/medical_meadow_medical_flashcards`
+- **Final Evaluation Loss:** 0.9755
+- **Perplexity:** 2.65
+
+---
+
+## 🚀 How to Use (Python Example)
+
+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.
+
+### Installation
+```bash
+pip install torch transformers acceleration safetensors
+```
+
+### Inference Code
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Mix80/ClinicaQwen-MedQA"
+
+print("Loading ClinicaQwen-MedQA...")
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+def ask_clinica_qwen(question, max_new_tokens=150):
+    # Construct the strict conversational template
+    prompt = f"User: {question}\nBot:"
+    
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=max_new_tokens,
+        temperature=0.1,          # Crucial: Low temp ensures strict factual adherence
+        top_p=0.85,
+        do_sample=True,
+        repetition_penalty=1.3,   # Prevents linguistic loops
+        no_repeat_ngram_size=3,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    
+    # Extract only the generated answer tokens, skipping the prompt prefix
+    prompt_length = inputs.input_ids.shape[1]
+    generated_tokens = outputs[0][prompt_length:]
+    
+    answer = tokenizer.decode(generated_tokens, skip_special_tokens=True).strip()
+    return answer
+
+# Test the model
+sample_query = "What are the key differences between a tension headache and a migraine?"
+response = ask_clinica_qwen(sample_query)
+
+print(f"\nQuestion: {sample_query}")
+print(f"ClinicaQwen: {response}")
+```
+
+---
+
+## ⚠️ Disclaimer
+*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.*