Sculptor-Qwen3_Med-Reasoning

Sculptor-Qwen3_Med-Reasoning is a fine-tuned variant of the Qwen3-4B architecture, trained specifically on the Med Reason Dataset to maximize accurate medical and clinical reasoning. This model excels at structured diagnostic logic, symptom analysis, and treatment planning, while maintaining lightweight performance, making it ideal for healthcare, medical education, and clinical support applications.

[!GGUF] : https://huggingface.co/prithivMLmods/Sculptor-Qwen3_Med-Reasoning-Q4_K_M-GGUF

Key Features

1. Precision Medical Reasoning with Med Reason Dataset Tailored for clinical reasoning, medical question answering, and evidence-based analysis, powered by the specialized Med Reason fine-tuning.

2. Lightweight Clinical Code Understanding Capable of interpreting and generating medical-related code (e.g., for health data analysis in Python or R), optimized for concise, logic-oriented scripts.

3. Structured Output Formatting Produces well-organized responses in Markdown, JSON, LaTeX, and tabular formats suitable for electronic health records, research documentation, and structured reporting.

4. Instruction-Following Accuracy Tuned for consistent multi-step instruction adherence in clinical cases and decision-making workflows, enhancing reliability for educational and medical use.

5. Multilingual Medical Capabilities Supports clinical reasoning and documentation in over 20 languages, enabling accessibility for global healthcare professionals.

6. Efficient 4B Architecture Based on Qwen3-4B, offering a balanced tradeoff between inference speed and domain-specific accuracy—suitable for deployment on mid-tier GPUs or cloud-based systems.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Sculptor-Qwen3_Med-Reasoning"

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

prompt = "A 45-year-old male presents with chest pain and shortness of breath. List possible diagnoses and explain the reasoning."

messages = [
    {"role": "system", "content": "You are a clinical reasoning assistant trained on the Med Reason Dataset."},
    {"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)

Intended Use

• Clinical reasoning and diagnosis support
• Medical question answering and tutoring
• Structured documentation and case analysis
• JSON/Markdown/tabular medical summaries
• Education tools for healthcare professionals
• Multilingual medical documentation and Q&A

Limitations

• Not designed for open-domain creative generation
• Limited context length compared to larger LLMs
• Sensitive to ambiguous or poorly formatted inputs
• May produce errors in complex or adversarial medical prompts

References

1. Qwen2.5 Technical Report
2. YaRN: Context Window Extension for LLMs