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internlm/JanusCoder-14B

sourceHugging Faceapache-2.0updated 11mo agoView on Hugging Face

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JanusCoder-14B

💻Github Repo • 🤗Model Collections • 📜Technical Report

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
👉 JanusCoder-14B	14B text model based on Qwen3-14B.	🤗 Model
JanusCoderV-7B	7B multimodal model based on Qwen2.5-VL-7B.	🤗 Model
JanusCoderV-8B	8B multimodal model based on InternVL3.5-8B.	🤗 Model

Performance

We evaluate the JanusCoder model on various benchmarks that span code interlligence tasks on multiple PLs:

Model	JanusCoder-14B	Qwen3-14B	Qwen2.5-Coder-32B-Instruct	LLaMA3-8B-Instruct	GPT-4o
PandasPlotBench (Task)	86	78	82	69	85
ArtifactsBench	41.1	36.5	35.5	36.5	37.9
DTVBench (Manim)	8.41	6.63	9.61	4.92	10.60
DTVBench (Wolfram)	5.97	5.08	4.98	3.15	5.97

Quick Start

Transformers

The following provides demo code illustrating how to generate text using JanusCoder-14B.

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

python

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "internlm/JanusCoder-14B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, device_map="auto", dtype="auto",
).eval()

messages = [
    {"role": "user", "content": "Create a line plot that illustrates function y=x."}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt"
).to(model.device)

with torch.inference_mode():
    generate_ids = model.generate(**inputs, max_new_tokens=200)
    decoded_output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True)

print(decoded_output[0])

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}
}