---
license: apache-2.0
pipeline_tag: image-text-to-text
library_name: transformers
---

# JanusCoderV-8B

[💻Github Repo](https://github.com/InternLM/JanusCoder) • [🤗Model Collections](https://huggingface.co/collections/internlm/januscoder) • [📜Technical Report](https://www.arxiv.org/abs/2510.23538)

## Introduction

We introduce JanusCoder and JanusCoderV, a suite of open-source foundational models designed to establish a unified visual-programmatic interface for code intelligence.
This model suite is built upon open-source language models (such as Qwen3-8B and 14B) and multimodal models (such as Qwen2.5-VL and InternVL3.5-8B). The JanusCoder series is trained on JANUSCODE-800K—the largest multimodal code corpus to date, generated by an innovative synthesis toolkit, covering everything from standard charts to complex interactive Web UIs and code-driven animations.
This enables the models to uniformly handle diverse visual-programmatic tasks, such as generating code from textual instructions, visual inputs, or a combination of both, rather than building specialized models for isolated tasks. JanusCoder excels at flexible content generation (like data visualizations and interactive front-ends) as well as precise, program-driven editing of visual effects and complex animation construction.

## Model Downloads

| Model Name | Description | Download |
| --- | --- | --- |
| JanusCoder-8B | 8B text model based on Qwen3-8B. | 🤗 [Model](https://huggingface.co/internlm/JanusCoder-8B) |
| JanusCoder-14B | 14B text model based on Qwen3-14B. | 🤗 [Model](https://huggingface.co/internlm/JanusCoder-14B) |
| JanusCoderV-7B | 7B multimodal model based on Qwen2.5-VL-7B. | 🤗 [Model](https://huggingface.co/internlm/JanusCoderV-7B) |
| 👉 **JanusCoderV-8B** | 8B multimodal model based on InternVL3.5-8B. | 🤗 [Model](https://huggingface.co/internlm/JanusCoderV-8B) |

## Performance

We evaluate the JanusCoderV model on various benchmarks that span multimodal code intelligence tasks on multiple PLs:

| Model | JanusCoderV-8B | Qwen2.5VL-7B-Instruct | InternVL3-8B | InternVL3.5-8B | MiniCPM-V-2-6 | Llama3.2-11B-Vision-Instruct | GPT-4o |
| --- | --- | --- | --- | --- | --- | --- | --- |
| ChartMimic (Customized) | 74.20 | 58.69 | 60.04 | 59.55 | 48.18 | 39.63 | 67.42 |
| DesignBench (Gen) | 68.86 | 72.73 | 69.34 | 71.73 | 66.25 | 62.24 | 76.83 |
| DesignBench (Edit) | 8.63 | 6.85 | 7.76 | 8.63 | 4.56 | 6.61 | 9.23 |
| WebCode2M | 18.28 | 12.83 | 12.40 | 11.95 | 9.73 | 6.57 | 13.00 |
| InteractScience (Func.) | 17.60 | 8.40 | 8.93 | 11.47 | 0.13 | 6.67 | 27.20 |
| InteractScience (Visual) | 33.32 | 19.83 | 53.35 | 24.17 | 7.70 | 13.24 | 46.01 |


## Quick Start

**Transformers**

The following provides demo code illustrating how to generate text using JanusCoderV-8B.

> Please use transformers >= 4.55.0 to ensure the model works normally.

```python
from transformers import AutoProcessor, AutoModelForCausalLM
import torch

model_name = "internlm/JanusCoderV-8B"
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
            {"type": "text", "text": "Please describe the image explicitly."},
        ],
    }
]

inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16)

generate_ids = model.generate(**inputs, max_new_tokens=32768)
decoded_output = processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
print(decoded_output)
```

## Citation
🫶  If you are interested in our work or find the repository / checkpoints / benchmark / data helpful, please consider using the following citation format when referencing our papers:

```bibtex
@article{sun2025januscoder,
  title={JanusCoder: Towards a Foundational Visual-Programmatic Interface for Code Intelligence},
  author={Sun, Qiushi and Gong, Jingyang and Liu, Yang and Chen, Qiaosheng and Li, Lei and Chen, Kai and Guo, Qipeng and Kao, Ben and Yuan, Fei},
  journal={arXiv preprint arXiv:2510.23538},
  year={2025}
}

@article{sun2024survey,
  title={A survey of neural code intelligence: Paradigms, advances and beyond},
  author={Sun, Qiushi and Chen, Zhirui and Xu, Fangzhi and Cheng, Kanzhi and Ma, Chang and Yin, Zhangyue and Wang, Jianing and Han, Chengcheng and Zhu, Renyu and Yuan, Shuai and others},
  journal={arXiv preprint arXiv:2403.14734},
  year={2024}
}

@article{chen2025interactscience,
  title={InteractScience: Programmatic and Visually-Grounded Evaluation of Interactive Scientific Demonstration Code Generation},
  author={Chen, Qiaosheng and Liu, Yang and Li, Lei and Chen, Kai and Guo, Qipeng and Cheng, Gong and Yuan, Fei},
  journal={arXiv preprint arXiv:2510.09724},
  year={2025}
}

@article{sun2025codeevo,
  title={CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback},
  author={Sun, Qiushi and Gong, Jinyang and Li, Lei and Guo, Qipeng and Yuan, Fei},
  journal={arXiv preprint arXiv:2507.22080},
  year={2025}
}
```