Instructions to use zjunlp/DataMind-Analysis-Qwen2.5-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zjunlp/DataMind-Analysis-Qwen2.5-7B with Transformers:

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

pipe = pipeline("text-generation", model="zjunlp/DataMind-Analysis-Qwen2.5-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zjunlp/DataMind-Analysis-Qwen2.5-7B")
model = AutoModelForCausalLM.from_pretrained("zjunlp/DataMind-Analysis-Qwen2.5-7B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use zjunlp/DataMind-Analysis-Qwen2.5-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zjunlp/DataMind-Analysis-Qwen2.5-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zjunlp/DataMind-Analysis-Qwen2.5-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zjunlp/DataMind-Analysis-Qwen2.5-7B

SGLang

How to use zjunlp/DataMind-Analysis-Qwen2.5-7B 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 "zjunlp/DataMind-Analysis-Qwen2.5-7B" \
    --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": "zjunlp/DataMind-Analysis-Qwen2.5-7B",
		"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 "zjunlp/DataMind-Analysis-Qwen2.5-7B" \
        --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": "zjunlp/DataMind-Analysis-Qwen2.5-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zjunlp/DataMind-Analysis-Qwen2.5-7B with Docker Model Runner:
```
docker model run hf.co/zjunlp/DataMind-Analysis-Qwen2.5-7B
```

Improve model card: Add pipeline tag, library, correct license, paper abstract, and usage example

by nielsr HF Staff - opened Aug 6, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+72

-8

nielsr

Aug 6, 2025

This PR significantly enhances the model card for DataMind-Qwen2.5-7B by:

Updating metadata:
- Changing the license from mit to apache-2.0 as indicated in the official GitHub repository.
- Adding pipeline_tag: text-generation to ensure proper categorization and discoverability for this data analysis and code generation model.
- Adding library_name: transformers to enable the "Use in Transformers" widget, providing easy access to inference code.
- Adding relevant tags such as data-analysis, code-generation, and qwen.
Enriching content:
- Adding the paper title and its Hugging Face link for quick reference.
- Including the paper abstract to provide a comprehensive overview of the model's research context and findings.
- Adding a direct link to the GitHub repository.
- Adding a "Usage" section with a practical Python code example for text generation (specifically for data analysis queries) using the transformers library.

These improvements make the model card more informative, discoverable, and user-friendly on the Hugging Face Hub.

Improve model card: Add pipeline tag, library, correct license, paper abstract, and usage example78d0bd81

Yukirsh changed pull request status to merged Aug 12, 2025

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment