CoPaw-Flash-9B-DataAnalyst-LoRA

CoPaw-Flash-9B-DataAnalyst-LoRA

Agentic Data Analyst that autonomously explores, analyzes, and visualizes your datasets.

<p align="center"> <img src="dataanalyst-demo.gif" alt="Data Analyst Demo" width="800"> </p>

What It Does

This model functions as an autonomous data analyst:

• 📂 Loads and explores datasets (CSV, Excel, JSON)
• 🔍 Performs statistical analysis and data profiling
• 📊 Creates visualizations (matplotlib, seaborn, plotly)
• 🐍 Writes and executes Python analysis scripts
• 📝 Generates summary reports and insights
• 🔄 Iterates through multi-step analysis workflows
• 🎯 Completes 90% of tasks autonomously (no human intervention)

Model Details

Performance Benchmark

Tested on 29 real Kaggle datasets with Data Analyst framework (max_turns=50, context=128K):

*Natural completion = Model autonomously outputs final summary report within 50 turns

<p align="center"> <img src="benchmark-chart.png" alt="Performance Benchmark: Base Model vs DataAnalyst-LoRA" width="800"> </p>

Key Findings

Base Model (Qwen3.5-9B):

• ❌ Understands tool call format but cannot execute autonomously
• ❌ Stops after 1-2 iterations
• ❌ Requires continuous user "continue" prompts
• ❌ Produces zero analysis output
• ❌ Not usable for real data analysis tasks

CoPaw-Flash-9B-DataAnalyst-LoRA:

• ✅ Fully autonomous execution (26 iterations average)
• ✅ Generates complete analysis pipelines
• ✅ Creates visualizations and reports
• ✅ 90% success rate on real-world datasets
• ✅ Production-ready for data analysis workflows

Conclusion: LoRA training is essential, not optional. Base model lacks autonomous data analyst capabilities despite understanding the tool calling format. This LoRA transforms the base model into a production-ready AI data analyst that can handle real-world datasets independently.

Quick Start

Step 1: Deploy with vLLM

export HF_TOKEN=your_huggingface_token

CUDA_VISIBLE_DEVICES=0,1 vllm serve agentscope-ai/QwenPaw-Flash-9B \
  --enable-lora \
  --lora-modules agent-lora=jason1966/CoPaw-Flash-9B-DataAnalyst-LoRA \
  --max-lora-rank 64 \
  --tensor-parallel-size 2 \
  --gpu-memory-utilization 0.85 \
  --max-model-len 131072 \
  --gdn-prefill-backend triton \
  --trust-remote-code \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_xml \
  --port 8000

Step 2: Setup Data Analyst Framework

git clone https://github.com/IIIIQIIII/data-analyst.git
cd data-analyst
bun install

Configure .env:

CLAUDE_CODE_USE_OPENAI=1
OPENAI_BASE_URL=http://localhost:8000/v1
OPENAI_API_KEY=unused
OPENAI_MODEL=agent-lora

Step 3: Start Analyzing

bun run start

Then input your analysis task:

Analyze sales_2024.csv and identify trends

The model will autonomously load data, perform analysis, create visualizations, and generate reports—all without requiring manual "continue" prompts.

vLLM Parameters

Hardware Requirements

Tested: 2x NVIDIA H200, vLLM 0.19.1, CUDA 13.0, Python 3.12

Troubleshooting

Acknowledgments

• CoPaw-Flash-9B — Base model by AgentScope AI
• Brev.dev — GPU cloud infrastructure by NVIDIA
• LocoreMind — Research and development

License

Apache 2.0