Instructions to use zero9tech/Qwen3-4B-Data-Science-Insight-TR-7.6K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zero9tech/Qwen3-4B-Data-Science-Insight-TR-7.6K 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-TR-7.6K")
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-TR-7.6K")
model = AutoModelForCausalLM.from_pretrained("zero9tech/Qwen3-4B-Data-Science-Insight-TR-7.6K")
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-TR-7.6K 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-TR-7.6K"
# 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-TR-7.6K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zero9tech/Qwen3-4B-Data-Science-Insight-TR-7.6K

SGLang

How to use zero9tech/Qwen3-4B-Data-Science-Insight-TR-7.6K 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-TR-7.6K" \
    --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-TR-7.6K",
		"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-TR-7.6K" \
        --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-TR-7.6K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zero9tech/Qwen3-4B-Data-Science-Insight-TR-7.6K with Docker Model Runner:
```
docker model run hf.co/zero9tech/Qwen3-4B-Data-Science-Insight-TR-7.6K
```

murataksit34 commited on Apr 13

Commit

a6139a4

verified ·

1 Parent(s): 941721c

Update model card: Zero9 Tech copyright, Apache-2.0, dataset links and key test highlights

Browse files

Files changed (1) hide show

README.md +32 -13

README.md CHANGED Viewed

@@ -1,21 +1,40 @@
 ---
-base_model: unsloth/Qwen3-4B-unsloth-bnb-4bit
 tags:
-- text-generation-inference
-- transformers
-- unsloth
 - qwen3
-license: apache-2.0
-language:
-- en
 ---
-# Uploaded finetuned  model
-- **Developed by:** zero9tech
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Qwen3-4B-unsloth-bnb-4bit
-This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 ---
+language:
+- tr
+license: apache-2.0
+library_name: transformers
 tags:
 - qwen3
+- turkish
+- data-mining
+- data-science
+- instruction-tuning
+- sft
+datasets:
+- wikimedia/wikipedia
+- murataksit34/veri-bilimci-diyalog-8k-tr
 ---
+# Qwen3-4B-DataScience-TR
+Bu model, veri madenciliği ve applied data science karar desteği için geliştirilmiştir.
+## Eğitim Kurgusu
+1. Türkçe düşünme adaptasyonu: `wikimedia/wikipedia` ile yaklaşık `%10` ön eğitim/adaptasyon.
+2. Alan uzmanlığı SFT: `murataksit34/veri-bilimci-diyalog-8k-tr`.
+## Veri Seti Test Özeti (murataksit34/veri-bilimci-diyalog-8k-tr)
+- Toplam kayıt: `2,689`
+- Split: `train: 2,166 · test: 257`
+- Purity violations: `train=5, test=1`
+- assistant_first_unique_ratio: `0.7977`
+- assistant_final_unique_ratio: `0.9996`
+## Kullanım Notu
+Model karar odaklı yanıt üretimi için optimize edilmiştir (yöntem seçimi, alternatif kıyas, risk sinyali, doğrulama adımı).
+## Copyright
+Copyright (c) Zero9 Tech
+## License
+Apache-2.0