Canum-med-Qwen3-Reasoning (Experimental)

Canum-med-Qwen3-Reasoning is an experimental medical reasoning and advisory model fine-tuned on Qwen/Qwen3-1.7B using the MTEB/raw_medrxiv dataset. It is designed to support clinical reasoning, biomedical understanding, and structured advisory outputs, making it a useful tool for researchers, educators, and medical professionals in experimental workflows.

GGUF: https://huggingface.co/prithivMLmods/Canum-med-Qwen3-Reasoning-GGUF

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Key Features

1. Medical Reasoning Focus Fine-tuned on MTEB/raw_medrxiv, enabling strong performance in biomedical literature understanding, diagnostic reasoning, and structured medical advisory tasks.

2. Clinical Knowledge Extraction Summarizes, interprets, and explains medical research papers, case studies, and treatment comparisons.

3. Step-by-Step Advisory Provides structured reasoning chains for symptom analysis, medical explanations, and advisory workflows.

4. Evidence-Aware Responses Optimized for scientific precision and evidence-driven output, suitable for research assistance and medical tutoring.

5. Structured Output Mastery Capable of producing results in LaTeX, Markdown, JSON, and tabular formats, supporting integration into research and healthcare informatics systems.

6. Optimized for Mid-Scale Deployment Balanced efficiency for research clusters, academic labs, and edge deployments in healthcare AI prototypes.

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Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Canum-med-Qwen3-Reasoning"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Summarize the findings of a study on the effectiveness of mRNA vaccines for COVID-19."

messages = [
    {"role": "system", "content": "You are a medical reasoning assistant that explains biomedical studies and provides structured clinical insights."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

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Intended Use

• Medical research summarization and literature review
• Diagnostic reasoning assistance for educational or research purposes
• Clinical advisory explanations in structured step-by-step format
• Biomedical tutoring for students and researchers
• Integration into experimental healthcare AI pipelines

Limitations

• ⚠️ Not a replacement for medical professionals – should not be used for direct clinical decision-making
• Training limited to research text corpora – may not capture rare or real-world patient-specific contexts
• Context length limits restrict multi-document medical record analysis
• Optimized for reasoning and structure, not empathetic or conversational dialogue