alt.freeweights.text / JanusCoder-8B
From: internlm <huggingface.co/internlm>Newsgroups: alt.freeweights.textSubject:

JanusCoder-8B

Date: 2026-11-15Commit: 86a9030Signed: 2DC28CC7B26A283B ✓

Image text to text model with 8.2B parameters. Safetensors weights, 16.4 GB in all. Published by internlm under Apache-2.0.

8.2B params16.4GBJanusCoder-8Bsafetensorspermissive
FactsParams8.2BSize16.4 GBFormatssafetensorsTaskimage text to textLicenseApache-2.0From sourcepermissive
$ fw get internlm/JanusCoder-8B
# manifest sha256 ab0579aecb80… · 16.4 GB · 16 files
$ fw verify ./JanusCoder-8B internlm/JanusCoder-8B
From sourceHosted (signed)internlm/JanusCoder-8BLast check: 2026-11-15 03:54 UTC

Get help: custom fine-tune or private deploy. We review your setup before we quote.

Works with

Overview

Size
16.4 GB
Weights
16.4 GB (safetensors)
Parameters
8.2B
Formats
safetensors
Task
image text to text
License
Apache-2.0 (permissive)
Files
16, each with a sha256
Publisher
internlm
Added
2026-11-15
Section
alt.freeweights.text
Lineage

Original release from the publisher.

Where these bytes come from
Publisher at the pinned commit→You

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Sources
BadgeHostLast checkResult
From sourcehuggingface.co2026-11-15 03:54 UTCpassed

Model card

From the publisher at commit 86a9030. Shown as plain text with links; images open at the publisher.

JanusCoder-8B#

💻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 NameDescriptionDownload
👉 JanusCoder-8B8B text model based on Qwen3-8B.🤗 Model
JanusCoder-14B14B text model based on Qwen3-14B.🤗 Model
JanusCoderV-7B7B multimodal model based on Qwen2.5-VL-7B.🤗 Model
JanusCoderV-8B8B 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:

ModelJanusCoder-8BQwen3-8BQwen2.5-Coder-7B-InstructLLaMA3-8B-InstructGPT-4o
PandasPlotBench (Task)8074766985
ArtifactsBench39.636.526.036.537.9
DTVBench (Manim)9.706.208.564.9210.60
DTVBench (Wolfram)6.075.184.043.155.97

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.

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:

@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}
}

Files

16 files · 16.4 GBWeights 16.4 GB (safetensors). Sizes and sha256 from the signed manifest.
Hide all 16 files with their sha256
FileSizesha256
.gitattributes1.57 KB34448b82c17d60fec9b65b1f093c115ddbaadc04beb1b0140b6bfed2e012a930
README.md4.92 KB71473e8f94395fb6acd8567c1b1453134189ae97127e43ae84e33b977356eefd
added_tokens.json707 Bc0284b582e14987fbd3d5a2cb2bd139084371ed9acbae488829a1c900833c680
chat_template.jinja4.12 KB87a2728cb8dc9fe424d624542f6060ec05a1d285ebbec578bb078900e33396b5
config.json1.54 KB1d231db4b92b37ac068de0a385f7719a2fad4d13caf69e61fb8164733a31634b
generation_config.json117 Ba858bb8e93b6ebdfaf3912ec5af4f4daca183812aeeb7147e26e0a9555ce6d48
merges.txt1.67 MB8831e4f1a044471340f7c0a83d7bd71306a5b867e95fd870f74d0c5308a904d5
model-00001-of-00004.safetensors4.90 GB9face8f0a40fcf2cf7e786af0a099e809df94d5c7455898970b572494d85af80
model-00002-of-00004.safetensors4.92 GB5b1996dfa4952f140d9c27d37ae6918813441dc97bc602624ce5a89d5f519be4
model-00003-of-00004.safetensors4.98 GB3b73f9db324644c00c450ba14a6f342b98e55062673ac09cae122a52368532f1
model-00004-of-00004.safetensors1.58 GB3a88ff588383fab5f6b8b3713e82bb6550203901bdd49a5c3cf6b2d9ae570311
model.safetensors.index.json32.9 KBe7e4bdc58d3302c97357f27979b270987dd620cf0cd9b1c130e3a51c9d64df95
special_tokens_map.json613 B76862e765266b85aa9459767e33cbaf13970f327a0e88d1c65846c2ddd3a1ecd
tokenizer.json11.4 MBaeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
tokenizer_config.json5.43 KBcd5c1909da4950a610a9e3a6cb6763cff0381d91c6f0838855db8d5ae83d6c9f
vocab.json2.78 MBca10d7e9fb3ed18575dd1e277a2579c16d108e32f27439684afa0e10b1440910
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
86a9030406 pinned now2026-11-15 03:54 UTC16 files · 16.4 GB

Use it

$ fw get internlm/JanusCoder-8B
# manifest sha256 ab0579aecb80… · 16.4 GB · 16 files
$ fw verify ./JanusCoder-8B internlm/JanusCoder-8B
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/internlm/JanusCoder-8B.json --dir ./JanusCoder-8B $ python3 fw.py verify ./JanusCoder-8B ./internlm__JanusCoder-8B.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download internlm/JanusCoder-8B --revision 86a9030406ab822464c7ad0f7b10766ca9fffcca --local-dir ./JanusCoder-8B
transformersLoad the weights in Python.
import os os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com" from transformers import AutoModelForImageTextToText, AutoProcessor processor = AutoProcessor.from_pretrained("internlm/JanusCoder-8B", revision="86a9030406ab822464c7ad0f7b10766ca9fffcca") model = AutoModelForImageTextToText.from_pretrained("internlm/JanusCoder-8B", revision="86a9030406ab822464c7ad0f7b10766ca9fffcca", device_map="auto")
vLLMServe an OpenAI-compatible API.
$ HF_ENDPOINT=https://freeweights.allternit.com vllm serve internlm/JanusCoder-8B --revision 86a9030406ab822464c7ad0f7b10766ca9fffcca

License

Name
Apache License 2.0
Id
Apache-2.0
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-15
Text in
README.md
Text sha256
71473e8f94395fb6acd8567c1b1453134189ae97127e43ae84e33b977356eefd
Terms gate
No extra terms beyond the license.

Read the license text

Licenses are checked against the license table. A custom license passes only after a person reads it.

Provenance

From: internlm <huggingface.co/internlm>Newsgroups: alt.freeweights.textSubject: JanusCoder-8BDate: 2026-11-15Commit: 86a9030Signed: 2DC28CC7B26A283B ✓
Upstream
internlm/JanusCoder-8B
Pinned commit
86a9030406ab822464c7ad0f7b10766ca9fffcca
Manifest
JanusCoder-8B.json · .minisig
Manifest sha256
ab0579aecb805a89338937d5273116a31a51a8902ecc3ad42807226fb2e19e5e
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 03:54 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/internlm/JanusCoder-8B.json $ curl -O https://freeweights.allternit.com/manifests/internlm/JanusCoder-8B.json.minisig $ minisign -Vm JanusCoder-8B.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

Model tree

1 model in the catalog name this one as their parent.

Quantizations
1
Quantizations 1
Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download internlm/JanusCoder-8B
Open in Freeweights Studio

Pull, verify and run it in the Allternit app on Desktop, web or phone.

Signed manifest
minisign, key 2DC28CC7B26A283B. JanusCoder-8B.json. Provenance. Takedown history: none.

Variants

1 quantized or adapter build of JanusCoder-8B. Each has its own license check and page.

From: laion <huggingface.co/laion>Subject: a2-rl-e2egit_large-50-32B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_exp_rpt_e2egit-large-15-8B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_exp_rpt_e2egit-v2-10-8B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_exp_rpt_pymethods2test-large-80-8B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_exp_rpt_unitsyn-python-large-10-8B
From: laion <huggingface.co/laion>Subject: a3-rl-DCAgent_mix_h2_language_proportional-65-8B