Instructions to use RUC-DataLab/DeepAnalyze-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RUC-DataLab/DeepAnalyze-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RUC-DataLab/DeepAnalyze-8B")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RUC-DataLab/DeepAnalyze-8B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use RUC-DataLab/DeepAnalyze-8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RUC-DataLab/DeepAnalyze-8B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RUC-DataLab/DeepAnalyze-8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RUC-DataLab/DeepAnalyze-8B
- SGLang
How to use RUC-DataLab/DeepAnalyze-8B 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 "RUC-DataLab/DeepAnalyze-8B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RUC-DataLab/DeepAnalyze-8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "RUC-DataLab/DeepAnalyze-8B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RUC-DataLab/DeepAnalyze-8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RUC-DataLab/DeepAnalyze-8B with Docker Model Runner:
docker model run hf.co/RUC-DataLab/DeepAnalyze-8B
Update README.md
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README.md
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datasets:
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- RUC-DataLab/DataScience-Instruct-500K
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pipeline_tag: table-question-answering
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datasets:
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- RUC-DataLab/DataScience-Instruct-500K
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pipeline_tag: table-question-answering
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---
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<p align="center" width="100%">
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<img src="assets/logo.png" alt="DeepAnalyze" style="width: 60%; min-width: 300px; display: block; margin: auto;">
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</p>
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# DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
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[](https://arxiv.org/abs/2510.16872)
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[](https://github.com/ruc-datalab/DeepAnalyze)
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[](https://ruc-deepanalyze.github.io/)
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[](https://huggingface.co/RUC-DataLab/DeepAnalyze-8B)
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[](https://huggingface.co/datasets/RUC-DataLab/DataScience-Instruct-500K)
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> **Authors**: **[Shaolei Zhang](https://zhangshaolei1998.github.io/), [Ju Fan*](http://iir.ruc.edu.cn/~fanj/), [Meihao Fan](https://scholar.google.com/citations?user=9RTm2qoAAAAJ), [Guoliang Li](https://dbgroup.cs.tsinghua.edu.cn/ligl/), [Xiaoyong Du](http://info.ruc.edu.cn/jsky/szdw/ajxjgcx/jsjkxyjsx1/js2/7374b0a3f58045fc9543703ccea2eb9c.htm)**
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**DeepAnalyze** is the first agentic LLM for autonomous data science. It can autonomously complete a wide range of data-centric tasks without human intervention, supporting:
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- 🛠 **Entire data science pipeline**: Automatically perform any data science tasks such as data preparation, analysis, modeling, visualization, and report generation.
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- 🔍 **Open-ended data research**: Conduct deep research on diverse data sources, including structured data (Databases, CSV, Excel), semi-structured data (JSON, XML, YAML), and unstructured data (TXT, Markdown), and finally produce analyst-grade research reports.
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- 📊 **Fully open-source**: The [model](https://huggingface.co/RUC-DataLab/DeepAnalyze-8B), [code](https://github.com/ruc-datalab/DeepAnalyze), [training data](https://huggingface.co/datasets/RUC-DataLab/DataScience-Instruct-500K), and [demo](https://huggingface.co/RUC-DataLab/DeepAnalyze-8B) of DeepAnalyze are all open-sourced, allowing you to deploy or extend your own data analysis assistant.
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<p align="center" width="100%">
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<img src="./assets/deepanalyze.jpg" alt="deepanalyze" style="width: 70%; min-width: 300px; display: block; margin: auto;">
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</p>
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More information refer to [DeepAnalyze's Repo](https://github.com/ruc-datalab/DeepAnalyze)
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