Instructions to use zero9tech/Qwen2.5-Coder-3B-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/Qwen2.5-Coder-3B-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/Qwen2.5-Coder-3B-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/Qwen2.5-Coder-3B-Data-Science-Insight-TR-7.6K")
model = AutoModelForCausalLM.from_pretrained("zero9tech/Qwen2.5-Coder-3B-Data-Science-Insight-TR-7.6K", 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 zero9tech/Qwen2.5-Coder-3B-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/Qwen2.5-Coder-3B-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/Qwen2.5-Coder-3B-Data-Science-Insight-TR-7.6K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zero9tech/Qwen2.5-Coder-3B-Data-Science-Insight-TR-7.6K

SGLang

How to use zero9tech/Qwen2.5-Coder-3B-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/Qwen2.5-Coder-3B-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/Qwen2.5-Coder-3B-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/Qwen2.5-Coder-3B-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/Qwen2.5-Coder-3B-Data-Science-Insight-TR-7.6K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zero9tech/Qwen2.5-Coder-3B-Data-Science-Insight-TR-7.6K with Docker Model Runner:
```
docker model run hf.co/zero9tech/Qwen2.5-Coder-3B-Data-Science-Insight-TR-7.6K
```

murataksit34 commited on Apr 13

Commit

c8bd71b

verified ·

1 Parent(s): d0de1d2

Align README format with core 4 model cards

Browse files

Files changed (1) hide show

README.md +14 -8

README.md CHANGED Viewed

@@ -7,26 +7,32 @@ tags:
 - qwen2.5
 - coder
 - turkish
-- data-science
 - data-mining
 - sft
 datasets:
 - wikimedia/wikipedia
 - murataksit34/veri-bilimci-diyalog-8k-tr
 ---
-# Qwen2.5-Coder-3B-DataScience-TR - Turkce Kodlu Analiz Asistani
-## Egitim Kurgusu
-1. Turkce dusunme adaptasyonu: wikimedia/wikipedia uzerinde yaklasik %10 on egitim/adaptasyon (48,148 kayit).
-2. Uzmanlik SFT: murataksit34/veri-bilimci-diyalog-8k-tr.
-## Kullanilan Dataset Ozeti
-- Toplam kayit: 7,656
-- Split: train 6,124 ; test 1,532
 - assistant_first_unique_ratio: 0.7034
 - assistant_final_unique_ratio: 0.8723
 ## Copyright
 Copyright (c) Zero9 Tech

 - qwen2.5
 - coder
 - turkish
 - data-mining
+- data-science
+- instruction-tuning
 - sft
 datasets:
 - wikimedia/wikipedia
 - murataksit34/veri-bilimci-diyalog-8k-tr
 ---
+# Qwen2.5-Coder-3B-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 (48,148 kayıt).
+2. Alan uzmanlığı SFT: murataksit34/veri-bilimci-diyalog-8k-tr.
+## Veri Seti Test Özeti (murataksit34/veri-bilimci-diyalog-8k-tr)
+- Toplam kayıt: 7,656
+- Split: train: 6,124 · test: 1,532
 - assistant_first_unique_ratio: 0.7034
 - assistant_final_unique_ratio: 0.8723
+## Kullanım Notu
+Model karar odaklı yanıt üretimi için optimize edilmiştir (yöntem seçimi, alternatif kıyas, risk sinyali, doğrulama adımı).
 ## Copyright
 Copyright (c) Zero9 Tech