--- title: CoPaw-Flash-9B-DataAnalyst-LoRA canonical_url: "https://www.modelscope.cn/models/LocoreMind/CoPaw-Flash-9B-DataAnalyst-LoRA" md_url: "https://www.modelscope.cn/models/LocoreMind/CoPaw-Flash-9B-DataAnalyst-LoRA.md" repository: LocoreMind/CoPaw-Flash-9B-DataAnalyst-LoRA last_updated: 2026-04-16 license: apache-2.0 pipeline_tag: image-text-to-text tasks: - image-text-to-text base_model: - agentscope-ai/CoPaw-Flash-9B base_model_relation: adapter parameters: 173.1M tensor_type: - BF16 library_name: - lora - safetensors - pytorch frameworks: - pytorch language: - en - zh downloads: 151 stars: 1 tags: - agent - tool-use - lora - vllm - qwen3.5 --- # CoPaw-Flash-9B-DataAnalyst-LoRA > CoPaw-Flash-9B-DataAnalyst-LoRA - LocoreMind 在 ModelScope 开源的模型。CoPaw-Flash-9B-DataAnalyst-LoRA LocoreMind/CoPaw-Flash-9B-DataAnalyst-LoRA 是 ModelScope 魔搭社区上的 173.1M 参数image-text-to-text模型，采用 apache-2.0 许可，基于 agentscope-ai/CoPaw-Flash-9B 构建。 - **Repository**: LocoreMind/CoPaw-Flash-9B-DataAnalyst-LoRA - **License**: apache-2.0 - **Tasks**: image-text-to-text - **Parameters**: 173.1M - **Base model**: agentscope-ai/CoPaw-Flash-9B - **Tags**: agent, tool-use, lora, vllm, qwen3.5 - **Downloads**: 151 - **Stars**: 1 - **Last updated**: 2026-04-16 Source: https://www.modelscope.cn/models/LocoreMind/CoPaw-Flash-9B-DataAnalyst-LoRA --- # CoPaw-Flash-9B-DataAnalyst-LoRA [![Demo Video](https://img.shields.io/badge/Demo_Video-YouTube-red?style=for-the-badge&logo=youtube)](https://youtu.be/A0WBHV-DzIE?si=_sHpjOUuhzjmN43p) [![Showcase](https://img.shields.io/badge/Showcase-29_Datasets-blue?style=for-the-badge&logo=google-chrome&logoColor=white)](https://dataanalyst.locoremind.com/) [![Framework](https://img.shields.io/badge/Framework-Data_Analyst-green?style=for-the-badge&logo=github)](https://github.com/IIIIQIIII/data-analyst) [![vLLM](https://img.shields.io/badge/Deployment-vLLM-orange?style=for-the-badge)](https://docs.vllm.ai/) **Agentic Data Analyst that autonomously explores, analyzes, and visualizes your datasets.**

Data Analyst Demo

## 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](https://github.com/IIIIQIIII/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

Performance Benchmark: Base Model vs DataAnalyst-LoRA

### 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 ```bash 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 ```bash git clone https://github.com/IIIIQIIII/data-analyst.git cd data-analyst bun install ``` Configure `.env`: ```bash CLAUDE_CODE_USE_OPENAI=1 OPENAI_BASE_URL=http://localhost:8000/v1 OPENAI_API_KEY=unused OPENAI_MODEL=agent-lora ``` ### Step 3: Start Analyzing ```bash 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](https://huggingface.co/agentscope-ai/QwenPaw-Flash-9B) — Base model by AgentScope AI - [Brev.dev](https://brev.nvidia.com/) — GPU cloud infrastructure by NVIDIA - [LocoreMind](https://locoremind.com/) — Research and development ## License Apache 2.0