Instructions to use khazarai/ClinicalReasoning-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use khazarai/ClinicalReasoning-0.6B with Transformers:

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

pipe = pipeline("text-generation", model="khazarai/ClinicalReasoning-0.6B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("khazarai/ClinicalReasoning-0.6B")
model = AutoModelForCausalLM.from_pretrained("khazarai/ClinicalReasoning-0.6B", device_map="auto")
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 Settings

vLLM

How to use khazarai/ClinicalReasoning-0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "khazarai/ClinicalReasoning-0.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "khazarai/ClinicalReasoning-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/khazarai/ClinicalReasoning-0.6B

SGLang

How to use khazarai/ClinicalReasoning-0.6B 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 "khazarai/ClinicalReasoning-0.6B" \
    --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": "khazarai/ClinicalReasoning-0.6B",
		"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 "khazarai/ClinicalReasoning-0.6B" \
        --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": "khazarai/ClinicalReasoning-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use khazarai/ClinicalReasoning-0.6B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for khazarai/ClinicalReasoning-0.6B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for khazarai/ClinicalReasoning-0.6B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for khazarai/ClinicalReasoning-0.6B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="khazarai/ClinicalReasoning-0.6B",
    max_seq_length=2048,
)

Docker Model Runner
How to use khazarai/ClinicalReasoning-0.6B with Docker Model Runner:
```
docker model run hf.co/khazarai/ClinicalReasoning-0.6B
```

Model Card: khazarai/ClinicalReasoning-0.6B

Model Description

Model Name: ClinicalReasoning-0.6B
Base Model: Qwen-3 0.6B
Fine-Tuning Dataset: 4,000 examples of medical reasoning clinical vignettes including multiple-choice questions, with step-by-step reasoning and answer labels.
Task: Multiple-choice medical reasoning with reasoning explanation. The model provides reasoning in tags and the final answer in tags.

Intended Use:

Assisting medical students and professionals in practicing clinical reasoning.
Educational tools for medical multiple-choice question solving.
Research purposes in medical NLP and reasoning tasks.

Not Intended For:

Direct medical diagnosis or patient care. Outputs should not replace professional medical advice.

Limitations:

Limited to the scope of the training dataset (4K examples).
May not generalize to rare or complex medical scenarios.
Should not be used as a substitute for professional medical advice.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("khazarai/ClinicalReasoning-0.6B")
model = AutoModelForCausalLM.from_pretrained(
    "khazarai/ClinicalReasoning-0.6B",
    device_map={"": 0}
)

system = "Please answer with one of the option in the bracket. Write reasoning in between <analysis></analysis>. Write answer in between <answer></answer>."

question = """
A 44-year-old female is admitted to the neurological service. You examine her chart and note that after admission she was started on nimodipine. Which of the following pathologies would benefit from this pharmacologic therapy??
{'A': 'Pseudotumor cerebri', 'B': 'Thromboembolic stroke', 'C': 'Epidural hematoma', 'D': 'Subdural hematoma', 'E': 'Subarachnoid hemorrhage'}
"""

messages = [
    {"role" : "system", "content" : system},
    {"role" : "user",   "content" : question}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True, 
    enable_thinking = False,
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 512,
    temperature = 0.6,
    top_p = 0.95,
    top_k = 20,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

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Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for khazarai/ClinicalReasoning-0.6B

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

unsloth/Qwen3-1.7B

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ClinicalReasoning-0.6B