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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MedInjection/QWEN-4B-TRAD-SYN"
# 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-TRAD-SYN",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

QWEN-4B-TRAD-SYN

Commit History

Update README.md

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ik-ram28 commited on Mar 13

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MedInjection-FR commited on Oct 22, 2025

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initial commit

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MedInjection-FR commited on Oct 22, 2025

Commit History

Update README.md c5afa13 verified

Update README.md ba3ebbb verified

Upload tokenizer 91fe609 verified

Upload Qwen3ForCausalLM ce254be verified

initial commit 9951388 verified

Update README.md

c5afa13
verified

Update README.md

ba3ebbb
verified

Upload tokenizer

91fe609
verified

Upload Qwen3ForCausalLM

ce254be
verified

initial commit

9951388
verified