Qwenmedic V1 Gguf

🚀 QwenMedic-v1

QwenMedic-v1 is a specialized medical model fine - tuned from Qwen3 - 1.7B, designed for clinical reasoning and instruction - following tasks, enhancing diagnostic Q&A and clinical summarization.

🚀 Quick Start

The following is a quick overview of the QwenMedic-v1 model. For more detailed information, please refer to the subsequent sections.

✨ Features

• Medical Specialization: Adapted from the Qwen3 - 1.7B causal language model, specifically fine - tuned for medical tasks such as clinical reasoning, diagnostic Q&A, and clinical summarization.
• Data - Driven: Trained on two curated medical datasets to improve performance in medical scenarios.
• Flexible Quantization: Supports float16 and int4 quantization.

📦 Installation

The README does not provide installation steps, so this section is skipped.

💻 Usage Examples

Basic Usage

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)

📚 Documentation

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

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

⚠️ Important Note

• The model may produce hallucinations or plausible - sounding but incorrect advice.
• There may be biases due to training - data coverage.
• The model is Not FDA - approved and should not replace professional medical judgment.
• Avoid feeding patient - identifiable data without proper de - identification.

Summary of Final Training Metrics

Contact

• Creator: Andre Ross
• Company: Ross Technologies
• Email: devops.ross@gmail.com

📄 License

The model is released under the apache - 2.0 license.