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Model: Shanghai_AI_Laboratory/JanusCoderV-7B
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+pipeline_tag: image-text-to-text
+library_name: transformers
+---
+
+# JanusCoderV-7B
+
+[💻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-7B | Qwen2.5VL-7B-Instruct | InternVL3-8B | InternVL3.5-8B | MiniCPM-V-2-6 | Llama3.2-11B-Vision-Instruct | GPT-4o |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| ChartMimic (Customized) | 72.77 | 58.69 | 60.04 | 59.55 | 48.18 | 39.63 | 67.42 |
+| DesignBench (Gen) | 73.31 | 72.73 | 69.34 | 71.73 | 66.25 | 62.24 | 76.83 |
+| DesignBench (Edit) | 8.79 | 6.85 | 7.76 | 8.63 | 4.56 | 6.61 | 9.23 |
+| WebCode2M | 26.21 | 12.83 | 12.40 | 11.95 | 9.73 | 6.57 | 13.00 |
+| InteractScience (Func.) | 17.73 | 8.40 | 8.93 | 11.47 | 0.13 | 6.67 | 27.20 |
+| InteractScience (Visual) | 27.67 | 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-7B.
+
+> Please use transformers >= 4.55.0 to ensure the model works normally.
+
+```python
+from transformers import AutoProcessor, AutoModelForCausalLM
+import torch
+
+model_name = "internlm/JanusCoderV-7B"
+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}
+}
+```

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chat_template.jinja

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+{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system
+You are a helpful assistant.<|im_end|>
+{% endif %}<|im_start|>{{ message['role'] }}
+{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
+{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>
+{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
+{% endif %}

config.json

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preprocessor_config.json

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special_tokens_map.json

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tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
+size 11421896

tokenizer_config.json

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+}

video_preprocessor_config.json

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+}