Instructions to use prithivMLmods/Sculptor-Qwen3_Med-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Sculptor-Qwen3_Med-Reasoning with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/Sculptor-Qwen3_Med-Reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Sculptor-Qwen3_Med-Reasoning")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Sculptor-Qwen3_Med-Reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use prithivMLmods/Sculptor-Qwen3_Med-Reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Sculptor-Qwen3_Med-Reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Sculptor-Qwen3_Med-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Sculptor-Qwen3_Med-Reasoning

SGLang

How to use prithivMLmods/Sculptor-Qwen3_Med-Reasoning with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Sculptor-Qwen3_Med-Reasoning" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Sculptor-Qwen3_Med-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Sculptor-Qwen3_Med-Reasoning" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Sculptor-Qwen3_Med-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Sculptor-Qwen3_Med-Reasoning with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Sculptor-Qwen3_Med-Reasoning
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

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.
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.
Structured Output Formatting Produces well-organized responses in Markdown, JSON, LaTeX, and tabular formats suitable for electronic health records, research documentation, and structured reporting.
Instruction-Following Accuracy Tuned for consistent multi-step instruction adherence in clinical cases and decision-making workflows, enhancing reliability for educational and medical use.
Multilingual Medical Capabilities Supports clinical reasoning and documentation in over 20 languages, enabling accessibility for global healthcare professionals.
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)