Instructions to use kingabzpro/qwen36-medquad-quick with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kingabzpro/qwen36-medquad-quick with Transformers:

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

pipe = pipeline("text-generation", model="kingabzpro/qwen36-medquad-quick")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kingabzpro/qwen36-medquad-quick", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kingabzpro/qwen36-medquad-quick with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kingabzpro/qwen36-medquad-quick"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kingabzpro/qwen36-medquad-quick",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kingabzpro/qwen36-medquad-quick

SGLang

How to use kingabzpro/qwen36-medquad-quick 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 "kingabzpro/qwen36-medquad-quick" \
    --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": "kingabzpro/qwen36-medquad-quick",
		"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 "kingabzpro/qwen36-medquad-quick" \
        --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": "kingabzpro/qwen36-medquad-quick",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kingabzpro/qwen36-medquad-quick with Docker Model Runner:
```
docker model run hf.co/kingabzpro/qwen36-medquad-quick
```

Model Card for kingabzpro/qwen36-medquad

Small QLoRA medical QA adapter built on Qwen/Qwen3.6-35B-A3B, trained on a filtered quick-run subset of keivalya/MedQuad-MedicalQnADataset.

Model Details

Developed by: kingabzpro
Model type: Causal language model adapter
Language: English
License: Apache-2.0
Finetuned from model: Qwen/Qwen3.6-35B-A3B

Uses

Direct use: Short medical question-answering experiments and response-style adaptation.
Out-of-scope use: Clinical diagnosis, treatment decisions, or unsupervised medical advice.

Bias, Risks, and Limitations

This adapter was trained on a small filtered subset of MedQuad. Outputs can still be incomplete, generic, or outdated. It should be treated as an experimental adapter, not a medical authority.

Training Details

Training data: Filtered subset of keivalya/MedQuad-MedicalQnADataset
Training procedure: QLoRA with PEFT and TRL
Hardware: RunPod H100 SXM

Evaluation

Evaluation was qualitative using three held-out before/after comparisons.

Observed improvements:

The fine-tuned model aligned more closely with MedQuad answer style.
It matched the reference inheritance explanation for acral peeling skin syndrome.
It gave a more complete autosomal-dominant inheritance explanation for distal hereditary motor neuropathy type V.
For the glaucoma research example, the answer became more NIH/NEI-aligned, though narrower than the base model response.

📓 Notebook

To follow the full training workflow, see the notebook:

qwen36_medquad.ipynb

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