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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

QWEN-4B-ALL

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 75bceac verified

Update README.md bdbec51 verified

Upload tokenizer 3f2672d verified

Upload Qwen3ForCausalLM 95776a6 verified

initial commit 055da45 verified

Update README.md

75bceac
verified

Update README.md

bdbec51
verified

Upload tokenizer

3f2672d
verified

Upload Qwen3ForCausalLM

95776a6
verified

initial commit

055da45
verified