Instructions to use MedInjection/QWEN-4B-NAT-TRAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MedInjection/QWEN-4B-NAT-TRAD with Transformers:

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

pipe = pipeline("text-generation", model="MedInjection/QWEN-4B-NAT-TRAD")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MedInjection/QWEN-4B-NAT-TRAD")
model = AutoModelForCausalLM.from_pretrained("MedInjection/QWEN-4B-NAT-TRAD")
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 MedInjection/QWEN-4B-NAT-TRAD with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MedInjection/QWEN-4B-NAT-TRAD"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MedInjection/QWEN-4B-NAT-TRAD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MedInjection/QWEN-4B-NAT-TRAD

SGLang

How to use MedInjection/QWEN-4B-NAT-TRAD 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 "MedInjection/QWEN-4B-NAT-TRAD" \
    --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": "MedInjection/QWEN-4B-NAT-TRAD",
		"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 "MedInjection/QWEN-4B-NAT-TRAD" \
        --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": "MedInjection/QWEN-4B-NAT-TRAD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MedInjection/QWEN-4B-NAT-TRAD with Docker Model Runner:
```
docker model run hf.co/MedInjection/QWEN-4B-NAT-TRAD
```

QWEN-4B-NAT-TRAD

Commit History

Update README.md

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MedInjection-FR commited on Oct 22, 2025

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MedInjection-FR commited on Oct 22, 2025

Commit History

Update README.md bf6252d verified

Update README.md a816818 verified

Upload tokenizer 6179d3a verified

Upload Qwen3ForCausalLM 79ab98b verified

initial commit bb0a6f3 verified

Update README.md

bf6252d
verified

Update README.md

a816818
verified

Upload tokenizer

6179d3a
verified

Upload Qwen3ForCausalLM

79ab98b
verified

initial commit

bb0a6f3
verified