CoPaw-Flash-9B-DataAnalyst-LoRA

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

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

Property	Value
Base Model	agentscope-ai/CoPaw-Flash-9B (Qwen3.5-9B architecture)
Task Type	Data Analysis Agent
LoRA Rank	64
LoRA Alpha	128
Precision	bfloat16
PEFT Version	0.18.1

Performance Benchmark

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

Metric	Qwen3.5-9B Base	DataAnalyst-LoRA	Improvement
Avg iterations	1.2	26.0	21.7x
Python files	0	100+	∞
Charts generated	0	290+	∞
Total tokens	~5K	18.5M	3700x
Natural completion rate*	0%	89.7%	+89.7pp
Hit turn limit	N/A	10.3%	-
Usable output	0/29 (0%)	26/29 (90%)	+90pp
User intervention	Required every step	Autonomous	Autonomous

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

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/CoPaw-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

Parameter	Description
--enable-lora	Enable LoRA adapter support
--lora-modules agent-lora=...	Load DataAnalyst-LoRA adapter
--max-lora-rank 64	LoRA rank (must match adapter)
--reasoning-parser qwen3	Enable reasoning process visibility
--enable-auto-tool-choice	Automatic tool selection
--tool-call-parser qwen3_xml	Parse XML-format tool calls
--gdn-prefill-backend triton	Optimize prefill with Triton

Hardware Requirements

Configuration	VRAM Required
Dual GPU (bf16, TP=2)	~11GB per GPU
Single GPU (bf16)	~22GB
8-bit quantized	~12GB
4-bit quantized	~6GB

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

Troubleshooting

Issue	Solution
FlashInfer errors	Add --gdn-prefill-backend triton
Out of memory	Reduce --max-model-len or --gpu-memory-utilization
Connection refused	Check netstat -tlnp | grep 8000

Acknowledgments

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

License

Apache 2.0