TIGER-Lab/VisCoder2-7B

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Oct 29, 2025License:apache-2.0Architecture:Transformer0.0K Open Weights Warm

VisCoder2-7B by TIGER-Lab is a lightweight multi-language visualization coding model based on Qwen2.5-Coder-7B-Instruct, designed for executable code generation, rendering, and iterative self-debugging. It is trained on the VisCode-Multi-679K dataset, covering 12 programming languages for visualization tasks. This model excels at generating semantically consistent visual outputs by aligning natural language instructions with rendering results. Its primary strength lies in multi-language visualization code generation with self-debugging capabilities.

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VisCoder2-7B: Multi-Language Visualization Coding Model

VisCoder2-7B, developed by TIGER-Lab, is a specialized model for generating executable visualization code across multiple programming languages. Built upon the Qwen2.5-Coder-7B-Instruct base model, it focuses on creating code that not only executes successfully but also produces visually accurate and semantically consistent outputs from natural language instructions.

Key Capabilities & Features

  • Multi-Language Support: Trained on the extensive VisCode-Multi-679K dataset, which includes 679,000 instruction-tuning examples across 12 programming languages for visualization tasks.
  • Executable Code Generation: Designed to generate code that is directly executable, addressing a core challenge in visualization.
  • Rendering & Self-Debugging: Incorporates capabilities for rendering visualization code and performing iterative self-debugging to refine outputs.
  • Performance on VisPlotBench: Evaluated on VisPlotBench, a benchmark of 888 executable visualization tasks across 8 languages, demonstrating consistent performance and notable improvements with multi-round self-debugging.

Training Details

  • Base Model: Qwen2.5-Coder-7B-Instruct.
  • Tuning Method: Full-parameter supervised fine-tuning (SFT).
  • Dataset: VisCode-Multi-679K, specifically curated for multi-language executable visualization.

Ideal Use Cases

  • Automated Visualization: Generating visualization code from natural language descriptions.
  • Multi-Language Development: Projects requiring visualization code generation in various programming environments.
  • Iterative Code Refinement: Scenarios benefiting from self-debugging capabilities to improve visualization outputs.

Finetunes
1 models