Practical overview
When it fits
Use DeepAnalyze for data-science work that may span preparation, analysis, modeling, visualization and report writing, including research across multiple data formats.
Who it is for
- Data analysts and researchers exploring datasets across multiple formats.
- Developers looking to deploy or extend an AI data-analysis assistant.
Documented uses
- Prepare datasets for analysis.
- Analyze and model data from databases and structured files.
- Research structured, semi-structured and unstructured data sources.
- Create visualizations and generate analyst-style reports.
Requirements & limits
The public GitHub repository lists an MIT license and Python as its primary language. The project links to its model and training dataset on Hugging Face.
- The stated workflow covers data preparation, analysis, modeling, visualization and report generation.
- The listed source types range from databases and spreadsheets to structured text formats and plain-text documents.
- The repository identifies Python as the primary language and MIT as the license.
- The project links to a model and training dataset, but the available description does not specify setup, hardware or deployment requirements.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install the inference dependencies
Prepare a Python environment with the listed packages for DeepAnalyze inference.
Before you start
- Python 3.12
- Conda
- The DeepAnalyze repository, including its requirements.txt
Steps
Create a Conda environment named deepanalyze with Python 3.12.
Activate the deepanalyze environment.
Install the repository dependencies with pip install -r requirements.txt.
Expected outcome
A Python environment is prepared with the listed inference dependencies.
Guide limitations
- The instructions do not describe how to install or obtain Conda.
- Training has separate dependency instructions and is not covered by this setup recipe.
- The README recommends separating inference and training environments to avoid dependency conflicts.
Start the DeepAnalyze-8B model with vLLM
Launch the model with a documented vLLM command and memory configuration.
Before you start
- DeepAnalyze-8B model files available locally
- vLLM installed; the requirements list specifies vllm>=0.8.5
- An 80GB GPU, matching the documented original-model configuration
Steps
Use the original DeepAnalyze-8B model; the README recommends a maximum model length of 131072 for 80GB GPU memory.
Run vLLM with the documented settings: python -m vllm.entrypoints.openai.api_server --model /path/to/original/model --served-model-name DeepAnalyze-8B --max-model-len 131072 --gpu-memory-utilization 0.95 --port 8000 --trust-remote-code.
Expected outcome
The command starts a vLLM API server configured to serve DeepAnalyze-8B on port 8000, if the model path and environment are valid.
Guide limitations
- Replace /path/to/original/model with the actual model path.
- The README provides configuration recommendations and a launch command, not a successful execution test.
- The documented model-length recommendations vary by GPU memory and model quantization; this recipe covers only the 80GB original-model configuration.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 7 October 2026
- Original website title
- ruc-datalab/DeepAnalyze · Official homepage title · retained in its source language