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Model Details
Model Provider
cyankiwi
Model Tree
internlm/JanusCoder-14B
Input Modalities
Output Modalities
Supported Functionality
bash
pip install -U vllmbash
vllm serve cyankiwi/JanusCoder-14B-AWQ-4bitName: cyankiwi
Contacts: ton@cyan.kiwi
💻Github Repo • 🤗Model Collections • 📜Technical Report
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 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. |
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 |
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
from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_name = "internlm/JanusCoder-14B"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto") messages = [ { "role": "user", "content": [ {"type": "text", "text": "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, 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)🫶 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}}| 🤗 Model |
| 6.63 |
| 9.61 |
| 4.92 |
| 10.60 |
| DTVBench (Wolfram) | 5.97 | 5.08 | 4.98 | 3.15 | 5.97 |