Instructions to use shaafsalman/MedQWEN-2.5-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shaafsalman/MedQWEN-2.5-32B with Transformers:

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

pipe = pipeline("text-generation", model="shaafsalman/MedQWEN-2.5-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shaafsalman/MedQWEN-2.5-32B")
model = AutoModelForCausalLM.from_pretrained("shaafsalman/MedQWEN-2.5-32B")
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 shaafsalman/MedQWEN-2.5-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shaafsalman/MedQWEN-2.5-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shaafsalman/MedQWEN-2.5-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shaafsalman/MedQWEN-2.5-32B

SGLang

How to use shaafsalman/MedQWEN-2.5-32B 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 "shaafsalman/MedQWEN-2.5-32B" \
    --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": "shaafsalman/MedQWEN-2.5-32B",
		"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 "shaafsalman/MedQWEN-2.5-32B" \
        --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": "shaafsalman/MedQWEN-2.5-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shaafsalman/MedQWEN-2.5-32B with Docker Model Runner:
```
docker model run hf.co/shaafsalman/MedQWEN-2.5-32B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MedQWEN-2.5-32B

A 32B medical language model created by SLERP-merging Qwen2.5-32B-Instruct with a medical domain fine-tune (shaafsalman/qwen-2.5-32B), using mergekit.

The merge retains the instruction-following capability of the base instruct model while blending in clinical domain knowledge from the fine-tuned variant.

Model Details

Field	Value
Architecture	Qwen2.5 (64 layers, 32B parameters)
Merge Method	SLERP
Base Model	Qwen/Qwen2.5-32B-Instruct
Domain Fine-tune	shaafsalman/qwen-2.5-32B
Dtype	bfloat16
Library	Transformers

Merge Configuration

slices:
  - sources:
      - model: Qwen/Qwen2.5-32B-Instruct
        layer_range: [0, 64]
      - model: shaafsalman/qwen-2.5-32B
        layer_range: [0, 64]
merge_method: slerp
base_model: Qwen/Qwen2.5-32B-Instruct
tokenizer_source: Qwen/Qwen2.5-32B-Instruct
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

SLERP interpolation weights by layer type:

Layer type	Interpolation pattern
Self-attention	`[0, 0.5, 0.3, 0.7, 1]` — sweeps from base to fine-tune
MLP	`[1, 0.5, 0.7, 0.3, 0]` — inverse sweep
Other (norms, embeddings)	`0.5` — equal blend

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "shaafsalman/MedQWEN-2.5-32B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="bfloat16",
    device_map="auto",
)

messages = [{"role": "user", "content": "What are the symptoms of hypomagnesemia?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Evaluation

Evaluated on MedAgentBench — a FHIR-based clinical agentic benchmark covering 300 tasks across 10 clinical task types including lab retrieval, medication ordering, and referral management.

Downloads last month: 3

Safetensors

Model size

33B params

Tensor type

BF16

Model tree for shaafsalman/MedQWEN-2.5-32B

Base model

Qwen/Qwen2.5-32B

Finetuned

Qwen/Qwen2.5-32B-Instruct

Finetuned

(1218)

this model

Quantizations

3 models