QwenMedic-v1-gguf

Model Card: QwenMedic-v1

<p align="center"> <img src="https://huggingface.co/ross-dev/QwenMedic-v1/resolve/main/assets/model_image.png" alt="Model Image" width="350" /> </p>

Overview

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.

Base Model

• Architecture: Qwen3-1.7B (28 layers, 16 Q / 8 KV attention heads, 32 768-token context)
• Parameters: 1.7 billion
• Quantization: float16 and int4 supported

Fine-Tuning Data

1. Medical Reasoning SFT (FreedomIntelligence/medical-o1-reasoning-SFT)

   • Chain-of-thought reasoning examples on verifiable medical problems
   • Language: English
   • Split used: train

2. General Medical Instruction (jtatman/medical-sci-instruct-1m-sharegpt)

   • Conversational Q&A prompts across medical topics
   • Sampled first 100 000 examples via train[:100000]

Training Configuration

• Framework: PyTorch + Hugging Face Transformers
• Optimizer: AdamW
• Learning Rate: 2 × 10⁻⁵
• Batch Size: 16 (with gradient accumulation)
• Precision: bfloat16 mixed precision
• Hardware: NVIDIA RTX 3090 (24 GB)

Intended Uses

• Clinical question answering & differential diagnosis
• Summarization of patient notes
• Medical education & decision support

Limitations & Risks

• May produce hallucinations or plausible-sounding but incorrect advice
• Biases due to training-data coverage
• Not FDA-approved—should not replace professional medical judgment
• Avoid feeding patient-identifiable data without proper de-identification

Summary of Final Training Metrics

Inference Example

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/QwenMedic-v1"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
prompt = "A 55-year-old male with Type 2 diabetes presents with sudden chest pain "
    "and diaphoresis. What are the top differential diagnoses?"
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

# parsing thinking content
try:
    # rindex finding 151668 (</think>)
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0

thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")

print("thinking content:", thinking_content)
print("content:", content)

Contact

• Creator: Andre Ross
• Company: Ross Technologies
• Email: [email protected]