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--- |
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model_name: QwenMedic-v1 |
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language: en |
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license: apache-2.0 |
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pipeline_tag: text-generation |
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tags: |
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- medical |
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- clinical |
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- question-answering |
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- summarization |
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- decision-support |
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datasets: |
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- FreedomIntelligence/medical-o1-reasoning-SFT |
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- jtatman/medical-sci-instruct-1m-sharegpt |
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--- |
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## Model Card: QwenMedic-v1 |
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<p align="center"> |
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<img |
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src="https://huggingface.co/ross-dev/QwenMedic-v1/resolve/main/assets/model_image.png" |
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alt="Model Image" |
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width="350" |
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/> |
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</p> |
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### Overview |
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QwenMedic-v1 is a medical-specialty adaptation of the Qwen3-1.7B causal language model, fine-tuned for clinical reasoning and instruction-following tasks. It was trained for **1 epoch** on two curated medical datasets to improve diagnostic Q&A and clinical summarization. |
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### Base Model |
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- **Architecture:** Qwen3-1.7B (28 layers, 16 Q / 8 KV attention heads, 32 768-token context) |
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- **Parameters:** 1.7 billion |
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- **Quantization:** float16 and int4 supported |
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### Fine-Tuning Data |
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1. **Medical Reasoning SFT** (`FreedomIntelligence/medical-o1-reasoning-SFT`) |
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- Chain-of-thought reasoning examples on verifiable medical problems |
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- Language: English |
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- Split used: `train` |
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2. **General Medical Instruction** (`jtatman/medical-sci-instruct-1m-sharegpt`) |
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- Conversational Q&A prompts across medical topics |
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- Sampled first 100 000 examples via `train[:100000]` |
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### Training Configuration |
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- **Framework:** PyTorch + Hugging Face Transformers |
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- **Optimizer:** AdamW |
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- **Learning Rate:** 2 × 10⁻⁵ |
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- **Batch Size:** 16 (with gradient accumulation) |
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- **Precision:** bfloat16 mixed precision |
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- **Hardware:** NVIDIA RTX 3090 (24 GB) |
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### Intended Uses |
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- Clinical question answering & differential diagnosis |
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- Summarization of patient notes |
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- Medical education & decision support |
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### Limitations & Risks |
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- May produce **hallucinations** or plausible-sounding but incorrect advice |
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- **Biases** due to training-data coverage |
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- **Not FDA-approved**—should not replace professional medical judgment |
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- Avoid feeding **patient-identifiable** data without proper de-identification |
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### Summary of Final Training Metrics |
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| Metric | Step | Smoothed | Raw Value | |
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|------------------:|-----:|---------:|----------:| |
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| **Epoch** | 1539 | 0.9979 | 0.9997 | |
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| **Gradient Norm** | 1539 | 0.3882 | 0.3974 | |
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| **Learning Rate** | 1539 | — | 0 | |
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| **Training Loss** | 1539 | 1.5216 | 1.4703 | |
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### Inference Example |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "Qwen/QwenMedic-v1" |
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# load the tokenizer and the model |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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# prepare the model input |
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prompt = "A 55-year-old male with Type 2 diabetes presents with sudden chest pain " |
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"and diaphoresis. What are the top differential diagnoses?" |
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messages = [ |
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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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enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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# conduct text completion |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=32768 |
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) |
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() |
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# parsing thinking content |
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try: |
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# rindex finding 151668 (</think>) |
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index = len(output_ids) - output_ids[::-1].index(151668) |
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except ValueError: |
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index = 0 |
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thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") |
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content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") |
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print("thinking content:", thinking_content) |
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print("content:", content) |
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``` |
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### Contact |
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- **Creator:** Andre Ross |
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- **Company:** Ross Technologies |
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- **Email:** [email protected] |
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