Instructions to use MLLM-CL/Qwen3-4B-Instruct-2507-SFT-Math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MLLM-CL/Qwen3-4B-Instruct-2507-SFT-Math with Transformers:

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

pipe = pipeline("text-generation", model="MLLM-CL/Qwen3-4B-Instruct-2507-SFT-Math")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MLLM-CL/Qwen3-4B-Instruct-2507-SFT-Math")
model = AutoModelForCausalLM.from_pretrained("MLLM-CL/Qwen3-4B-Instruct-2507-SFT-Math", 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 MLLM-CL/Qwen3-4B-Instruct-2507-SFT-Math with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MLLM-CL/Qwen3-4B-Instruct-2507-SFT-Math"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MLLM-CL/Qwen3-4B-Instruct-2507-SFT-Math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MLLM-CL/Qwen3-4B-Instruct-2507-SFT-Math

SGLang

How to use MLLM-CL/Qwen3-4B-Instruct-2507-SFT-Math 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 "MLLM-CL/Qwen3-4B-Instruct-2507-SFT-Math" \
    --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": "MLLM-CL/Qwen3-4B-Instruct-2507-SFT-Math",
		"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 "MLLM-CL/Qwen3-4B-Instruct-2507-SFT-Math" \
        --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": "MLLM-CL/Qwen3-4B-Instruct-2507-SFT-Math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MLLM-CL/Qwen3-4B-Instruct-2507-SFT-Math with Docker Model Runner:
```
docker model run hf.co/MLLM-CL/Qwen3-4B-Instruct-2507-SFT-Math
```

Qwen3-4B-Instruct-2507-SFT-Math / trainer_log.jsonl

Moenupa

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	{"current_steps": 10, "total_steps": 50, "loss": 3.3526, "lr": 9.806308479691595e-06, "epoch": 0.2, "percentage": 20.0, "elapsed_time": "0:00:09", "remaining_time": "0:00:38"}
	{"current_steps": 20, "total_steps": 50, "loss": 0.5978, "lr": 7.795964517353734e-06, "epoch": 0.4, "percentage": 40.0, "elapsed_time": "0:00:15", "remaining_time": "0:00:23"}
	{"current_steps": 30, "total_steps": 50, "loss": 0.6108, "lr": 4.477357683661734e-06, "epoch": 0.6, "percentage": 60.0, "elapsed_time": "0:00:24", "remaining_time": "0:00:16"}
	{"current_steps": 40, "total_steps": 50, "loss": 0.6467, "lr": 1.4033009983067454e-06, "epoch": 0.8, "percentage": 80.0, "elapsed_time": "0:00:30", "remaining_time": "0:00:07"}
	{"current_steps": 50, "total_steps": 50, "loss": 0.6008, "lr": 1.2179748700879013e-08, "epoch": 1.0, "percentage": 100.0, "elapsed_time": "0:00:35", "remaining_time": "0:00:00"}
	{"current_steps": 50, "total_steps": 50, "epoch": 1.0, "percentage": 100.0, "elapsed_time": "0:01:11", "remaining_time": "0:00:00"}