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Model: cyankiwi/JanusCoder-8B-AWQ-4bit
Source: Original Platform

README.md

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+---
+license: apache-2.0
+library_name: transformers
+base_model: internlm/JanusCoder-8B
+---
+
+# JanusCoder-8B AWQ - INT4
+
+## Model Details
+
+### Quantization Details
+
+- **Quantization Method:** AWQ
+- **Bits:** 4
+- **Group Size:** 32
+- **Calibration Dataset:** [nvidia/Llama-Nemotron-Post-Training-Dataset](https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset)
+- **Quantization Tool:** [llm-compressor](https://github.com/vllm-project/llm-compressor)
+
+## Get Started
+
+### Prerequisite
+
+```bash
+pip install -U vllm
+```
+
+### Basic Usage
+
+```bash
+vllm serve cyankiwi/JanusCoder-8B-AWQ-4bit
+```
+
+## Additional Information
+
+### Changelog
+
+- **v1.0.0** - Initial quantized release
+
+### Authors
+
+- **Name:** cyankiwi
+
+- **Contacts:** ton@cyan.kiwi
+
+# JanusCoder-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 JanusCoder model on various benchmarks that span code interlligence tasks on multiple PLs:
+
+| Model | JanusCoder-8B | Qwen3-8B | Qwen2.5-Coder-7B-Instruct | LLaMA3-8B-Instruct | GPT-4o |
+| --- | --- | --- | --- | --- | --- |
+| PandasPlotBench (Task) | 80 | 74 | 76 | 69 | 85 |
+| ArtifactsBench | 39.6 | 36.5 | 26.0 | 36.5 | 37.9 |
+| DTVBench (Manim) | 9.70 | 6.20 | 8.56 | 4.92 | 10.60 |
+| DTVBench (Wolfram) | 6.07 | 5.18 | 4.04 | 3.15 | 5.97 |
+
+
+## Quick Start
+
+**Transformers**
+
+The following provides demo code illustrating how to generate text using JanusCoder-8B.
+
+> Please use transformers >= 4.55.0 to ensure the model works normally.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_name = "internlm/JanusCoder-8B"
+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)
+```
+
+## 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}
+}
+```