Instructions to use zero9tech/Qwen3-4B-Data-Science-Insight-16.5K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zero9tech/Qwen3-4B-Data-Science-Insight-16.5K with Transformers:

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

pipe = pipeline("text-generation", model="zero9tech/Qwen3-4B-Data-Science-Insight-16.5K")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zero9tech/Qwen3-4B-Data-Science-Insight-16.5K")
model = AutoModelForCausalLM.from_pretrained("zero9tech/Qwen3-4B-Data-Science-Insight-16.5K")
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 zero9tech/Qwen3-4B-Data-Science-Insight-16.5K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zero9tech/Qwen3-4B-Data-Science-Insight-16.5K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zero9tech/Qwen3-4B-Data-Science-Insight-16.5K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zero9tech/Qwen3-4B-Data-Science-Insight-16.5K

SGLang

How to use zero9tech/Qwen3-4B-Data-Science-Insight-16.5K 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 "zero9tech/Qwen3-4B-Data-Science-Insight-16.5K" \
    --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": "zero9tech/Qwen3-4B-Data-Science-Insight-16.5K",
		"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 "zero9tech/Qwen3-4B-Data-Science-Insight-16.5K" \
        --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": "zero9tech/Qwen3-4B-Data-Science-Insight-16.5K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zero9tech/Qwen3-4B-Data-Science-Insight-16.5K with Docker Model Runner:
```
docker model run hf.co/zero9tech/Qwen3-4B-Data-Science-Insight-16.5K
```

Qwen3-4B-Data-Science-Insight-16.5K

This model is tuned for decision-oriented data mining and applied data science assistance.

Training Setup

Domain SFT: zero9tech/data-scientist-insight-dialog-en-16.5k.

Dataset Test Highlights

Total records: 16,463
Split: train: 14,021 · validation: 801 · test: 1,641
assistant_first_unique_ratio: 0.8408
assistant_final_unique_ratio: 1.0000

Usage Note

Model behavior is optimized for decision-focused responses (method choice, alternatives, risk signals, validation planning).

Copyright

License

Apache-2.0

Downloads last month: 217

Safetensors

Model size

4B params

Tensor type

BF16

zero9tech
/

Qwen3-4B-Data-Science-Insight-16.5K