alt.freeweights.glm / GLM-4.5V-FP8
From: zai-org <huggingface.co/zai-org>Newsgroups: alt.freeweights.glmSubject:

GLM-4.5V-FP8

Date: 2026-11-15Commit: 3ca028eSigned: 2DC28CC7B26A283B ✓

Image text to text model with 107.7B parameters. Safetensors weights, 110 GB in all. Published by zai-org under MIT.

107.7B params110GBGLM-4.5V-FP8safetensorspermissive
FactsParams107.7BSize110 GBFormatssafetensorsTaskimage text to textLicenseMITFrom sourcepermissive
$ fw get zai-org/GLM-4.5V-FP8
# manifest sha256 c2c28f99eeee… · 110 GB · 56 files
$ fw verify ./GLM-4.5V-FP8 zai-org/GLM-4.5V-FP8
From sourceHosted (signed)zai-org/GLM-4.5V-FP8Last check: 2026-11-15 05:08 UTC

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

Quantized of GLM-4.5-Air-Base.

Works with

Overview

Size
110 GB
Weights
110 GB (safetensors)
Parameters
107.7B
Formats
safetensors
Task
image text to text
License
MIT (permissive)
Files
56, each with a sha256
Publisher
zai-org
Added
2026-11-15
Section
alt.freeweights.glm
Lineage

Original release from the publisher.

Base model: zai-org/GLM-4.5-Air-Base

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

Sources are tried in order. A file counts only when its size and sha256 match the manifest. Sources are checked when you ask for them, not on a timer.

Sources
BadgeHostLast checkResult
From sourcehuggingface.co2026-11-15 05:08 UTCpassed

Model card

From the publisher at commit 3ca028e. Shown as plain text with links; images open at the publisher.

GLM-4.5V-FP8#

Image: image

👋 Join our Discord communities.

📖 Check out the paper.

📍 Access the GLM-V series models via API on the ZhipuAI Open Platform.

Introduction#

Vision-language models (VLMs) have become a key cornerstone of intelligent systems. As real-world AI tasks grow increasingly complex, VLMs urgently need to enhance reasoning capabilities beyond basic multimodal perception — improving accuracy, comprehensiveness, and intelligence — to enable complex problem solving, long-context understanding, and multimodal agents.

Through our open-source work, we aim to explore the technological frontier together with the community while empowering more developers to create exciting and innovative applications.

GLM-4.5V is based on ZhipuAI’s next-generation flagship text foundation model GLM-4.5-Air (106B parameters, 12B active). It continues the technical approach of GLM-4.1V-Thinking, achieving SOTA performance among models of the same scale on 42 public vision-language benchmarks. It covers common tasks such as image, video, and document understanding, as well as GUI agent operations.

Image: bench_45

Beyond benchmark performance, GLM-4.5V focuses on real-world usability. Through efficient hybrid training, it can handle diverse types of visual content, enabling full-spectrum vision reasoning, including:

  • Image reasoning (scene understanding, complex multi-image analysis, spatial recognition)
  • Video understanding (long video segmentation and event recognition)
  • GUI tasks (screen reading, icon recognition, desktop operation assistance)
  • Complex chart & long document parsing (research report analysis, information extraction)
  • Grounding (precise visual element localization)

The model also introduces a Thinking Mode switch, allowing users to balance between quick responses and deep reasoning. This switch works the same as in the GLM-4.5 language model.

The special tokens <|begin_of_box|> and <|end_of_box|> in the response mark the answer’s bounding box in the image. The bounding box is given as four numbers — for example [x1, y1, x2, y2], where (x1, y1) is the top-left corner and (x2, y2)` is the bottom-right corner. The bracket style may vary ([], [[]], (), <>, etc.), but the meaning is the same: it encloses the coordinates of the box. These coordinates are relative values between 0 and 1000, normalized to the image size.

For more code information, please visit our GitHub.

Sample Usage#

You can use the model with the transformers library as follows:

from transformers import AutoProcessor, AutoModelForConditionalGeneration
from PIL import Image
import requests
import torch

# Load model and processor
model_id = "zai-org/GLM-4.5V-FP8"
model = AutoModelForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

# Example image loading (replace with your image path or URL)
image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
image = Image.open(requests.get(image_url, stream=True).raw).convert("RGB")

# Prepare the prompt
prompt = "Describe this car in detail."
messages = [
    {"role": "user", "content": [{"type": "image", "image": image}, {"type": "text", "text": prompt}]}
]

# Apply chat template and preprocess image
input_ids = processor.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
pixel_values = processor.preprocess_images(image, return_tensors="pt")

# Generate response
with torch.no_grad():
    output_ids = model.generate(
        input_ids.to(model.device),
        pixel_values=pixel_values.to(model.device),
        max_new_tokens=512
    )

response = processor.decode(output_ids[0], skip_special_tokens=True)
print(response)

Citation#

If you use this model, please cite the following paper:

@misc{vteam2025glm45vglm41vthinkingversatilemultimodal,
      title={GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning}, 
      author={V Team and Wenyi Hong and Wenmeng Yu and Xiaotao Gu and Guo Wang and Guobing Gan and Haomiao Tang and Jiale Cheng and Ji Qi and Junhui Ji and Lihang Pan and Shuaiqi Duan and Weihan Wang and Yan Wang and Yean Cheng and Zehai He and Zhe Su and Zhen Yang and Ziyang Pan and Aohan Zeng and Baoxu Wang and Bin Chen and Boyan Shi and Changyu Pang and Chenhui Zhang and Da Yin and Fan Yang and Guoqing Chen and Jiazheng Xu and Jiale Zhu and Jiali Chen and Jing Chen and Jinhao Chen and Jinghao Lin and Jinjiang Wang and Junjie Chen and Leqi Lei and Letian Gong and Leyi Pan and Mingdao Liu and Mingde Xu and Mingzhi Zhang and Qinkai Zheng and Sheng Yang and Shi Zhong and Shiyu Huang and Shuyuan Zhao and Siyan Xue and Shangqin Tu and Shengbiao Meng and Tianshu Zhang and Tianwei Luo and Tianxiang Hao and Tianyu Tong and Wenkai Li and Wei Jia and Xiao Liu and Xiaohan Zhang and Xin Lyu and Xinyue Fan and Xuancheng Huang and Yanling Wang and Yadong Xue and Yanfeng Wang and Yanzi Wang and Yifan An and Yifan Du and Yiming Shi and Yiheng Huang and Yilin Niu and Yuan Wang and Yuanchang Yue and Yuchen Li and Yutao Zhang and Yuting Wang and Yu Wang and Yuxuan Zhang and Zhao Xue and Zhenyu Hou and Zhengxiao Du and Zihan Wang and Peng Zhang and Debing Liu and Bin Xu and Juanzi Li and Minlie Huang and Yuxiao Dong and Jie Tang},
      year={2025},
      eprint={2507.01006},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2507.01006}, 
}

Files

56 files · 110 GBWeights 110 GB (safetensors). Sizes and sha256 from the signed manifest.
Show all 56 files with their sha256
FileSizesha256
.gitattributes1.57 KB34448b82c17d60fec9b65b1f093c115ddbaadc04beb1b0140b6bfed2e012a930
README.md6.31 KB2d97af11c3c1a32c1dfaf2976891fff647499f3bacb638ad958c73cd95ae5a2c
chat_template.jinja3.86 KB999121db064241176a762275d08f382b79cc8e59c1ed956a1cc19242a1225394
config.json25.2 KB6a8d1876ce8d378b87e238ffe69bd2c53e98f304a2b3cc74805db13de79a3f01
generation_config.json230 Bfd8ab9568edf84b2690593c8a13d77f114d1f6f61fcc85d1b90708f269929c34
model-00001-of-00046.safetensors2.59 GB3c0026c1097650dede9bbc7893416a6c56fd86dcf32ef566ef195a1ffbb51bb3
model-00002-of-00046.safetensors2.35 GB2b499d123943a4fd52eb5e4891eb818b07532e29ce365a9a2f58508ccf0c81db
model-00003-of-00046.safetensors2.35 GB82727d15e28b67d590b476147797a034af5df1bda60afdaadd1d6b15717e0c10
model-00004-of-00046.safetensors2.35 GBaf67eecd864912aca8247d8ca4f9e6ae6229671f8addb2d30c091328d9548255
model-00005-of-00046.safetensors2.35 GBa498348dd97f4787defe9451a331cce25ebccd6b955b729e9363924b456ced22
model-00006-of-00046.safetensors2.35 GB9743eeba15d76650e49da82d6c0aa8cc899609bd7e7c8cdecf3b4bed9864c43d
model-00007-of-00046.safetensors2.35 GB22a4ea664ad038757fddee9a5ea51c94e935bc3913a9e8f061ed76a16660e600
model-00008-of-00046.safetensors2.35 GBd997cb2a4e73c7933622f7d054ad356a177296bd166a88a8385e1d4f16ef3add
model-00009-of-00046.safetensors2.35 GBd0417629496b9816d8b804c94961f270da7eba106432308c7f70e04fc3f6d16f
model-00010-of-00046.safetensors2.35 GB1fc5d0e0c6be9c991fb30838fb916f65b59a3001a6d45910ea2f2b5b5884467e
model-00011-of-00046.safetensors2.35 GB42ea51bd9237e8daf79ab29df7b3d4a761fdba43bf00721fd859538651dcb6ef
model-00012-of-00046.safetensors2.35 GB2f3c04ccf3e26d0eda5a67449c2ad29f01ac8e6472b02f239ae9246d4f89e443
model-00013-of-00046.safetensors2.35 GBc7e6b8fc35578a270a5c9eb87faadbe9cc22464f47418a4c1837f083d06680a8
model-00014-of-00046.safetensors2.35 GB36e86fd429e5f83a612be7ac9b1ab610666f9567cd8899d08a258ea6cdf0a691
model-00015-of-00046.safetensors2.35 GB4f0c2706171c49ffe01e95bd890fd15cf52552f6ddf5926fa9be1385fe0fc184
model-00016-of-00046.safetensors2.35 GB05fa041f2402fdf6f1d2dbb202513d1b17ca5de5f16a0d05d9b5b4dcbf3bfb45
model-00017-of-00046.safetensors2.35 GB081941f1d321b7bc08b543a96fe6c4f8733ff76c23b588329071c897a9947cf2
model-00018-of-00046.safetensors2.35 GB77d4a16e9f6b6228cfa9182af0485d18af15fa4909c66531e69c8f7b355a1258
model-00019-of-00046.safetensors2.35 GBd43582ed4756714587fd0a40b998bef7aa7730bb0c039888acfc2b14ead150d9
model-00020-of-00046.safetensors2.35 GB03428d53dd36f6794c46ee25c99a6c75bda1be39ab932a76880c836378e1eb6c
model-00021-of-00046.safetensors2.35 GBe2e1ddb79657265803c09bb0f9055e6dc7bb63bca50955fc69afb8c6a50eb613
model-00022-of-00046.safetensors2.35 GB69498b48c34dad39580154988e6b7e6c2eca094137cbe7073d4e4adf94aca460
model-00023-of-00046.safetensors2.35 GB22507d8b255ea3ed470a92d91c4ce80cc31ae4f2415b2330f06b3a94ffdc4442
model-00024-of-00046.safetensors2.35 GB5a041264a8f85653758b9d86415af6b0a69452635978f3992e2ec62b1d5ee587
model-00025-of-00046.safetensors2.35 GB818895f30d480a87af7e8e366391144b82efe08261a0416d79ecbc93f81cb193
model-00026-of-00046.safetensors2.35 GB90b82c161129374c39d8a9af9292b0c0c5267a0adabded559b5514a9acc2b48b
model-00027-of-00046.safetensors2.35 GB6db358d11a9ca48bceb512a7ed3112e64a10063467f7df7c5b3e9c1ae0e0b66d
model-00028-of-00046.safetensors2.35 GB69bb06e0eddba31f9b7158c4ab3bbd4027d9599ddccdcb3b2fa031e81977baa4
model-00029-of-00046.safetensors2.35 GBa18b70bbca580315a7c6cc02b9ad46f2d90e0edb69867b64fd2355f106aa0eab
model-00030-of-00046.safetensors2.35 GBb32f26dddfe6602af964a3b75399b13acd31d1a20acd723dc5fb2c608c2fb820
model-00031-of-00046.safetensors2.35 GB037e5337e1cfd3785be716b81577abbc448113d8acb4bd5663236cb0ed373d79
model-00032-of-00046.safetensors2.35 GB51d097c3d9ae3f5e1ee58b29ffe8406fed9e27bbbbf94d643c2d6187135383eb
model-00033-of-00046.safetensors2.35 GBeac2ec893d4c0865c9e0e010e37066b04b75428108532f9653435de681ff7eaa
model-00034-of-00046.safetensors2.35 GB71ad484ce5341cfbcc34e1c4f7561924f4fddc19a792af791a38fd0d7bae45f1
model-00035-of-00046.safetensors2.35 GB8921ab2e9a4caac733f36e75598cb31498e7b275263a5ac1aa6a3cd1016e2ad1
model-00036-of-00046.safetensors2.35 GB391e4d870d3cac92d92af66b29afdec74fd14920ec7e96187ac6509346879bfd
model-00037-of-00046.safetensors2.35 GB480e3f5a5e0a8e5e8ca54ae7450c7da61a5185bfa67b9b2417bdc4fafe075fe4
model-00038-of-00046.safetensors2.35 GB59a38f7f5663a3af97fee68b5c78305f110a2cbeb2dc3a5f1c4eedc584df1d75
model-00039-of-00046.safetensors2.35 GBfd378b918247789e9770ce4f56152d8f8e3775897dd644ab005de548633e76ab
model-00040-of-00046.safetensors2.35 GBcacc121a18318ac5e140ae9cf6d3711ee138e8bf570a33ebd3459e7941a54d61
model-00041-of-00046.safetensors2.35 GBe05919e962b44df34c66477a74cd01fc7dc780f68217e2584a25f80ef98a84ec
model-00042-of-00046.safetensors2.35 GB42f5879666cca387c0d593fdf522e5820880f6cd74ca90d244f418567cd0498c
model-00043-of-00046.safetensors2.35 GBd7a2d9af66d73ba137252b42c56e3123b0389c1e237461449e9e98ddabe11c5f
model-00044-of-00046.safetensors2.35 GB7e934098db242fa72a0088681769dfc6986162d678906f831e72ebab9d029044
model-00045-of-00046.safetensors3.03 GB413bd3fe7a33d0df18382d67d8fc1cc7bf2dd8fbe4e6dd29bf2a90b703496702
model-00046-of-00046.safetensors3.52 GBe42f8c540b7eaa349cc6dac69d9c066bc863328867c3291e1f47af20781b3f96
model.safetensors.index.json3.86 MB15590a75cad64b5066993921ce68c03f64144507d0dee60fb193919b34a1a8c3
preprocessor_config.json364 Bd71bebc1307f2d1e38e87563b50faa78c72fd2e4093b5c69c756f12c444fec6a
tokenizer.json20.0 MB9340665016419c825c4bdabbcc9acc43b7ca2c68ce142724afa829abb1be5efd
tokenizer_config.json7.34 KB13809913afa65678aff5d54d888720c30cb000d0e9a85799ef22d1ca9dfdf228
video_preprocessor_config.json365 B85d5ca37836f20a5edd11800e4380e731612946f8c7b70fba4d8f532fe752932
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
3ca028eac7 pinned now2026-11-15 05:08 UTC56 files · 110 GB

Use it

$ fw get zai-org/GLM-4.5V-FP8
# manifest sha256 c2c28f99eeee… · 110 GB · 56 files
$ fw verify ./GLM-4.5V-FP8 zai-org/GLM-4.5V-FP8
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/zai-org/GLM-4.5V-FP8.json --dir ./GLM-4.5V-FP8 $ python3 fw.py verify ./GLM-4.5V-FP8 ./zai-org__GLM-4.5V-FP8.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download zai-org/GLM-4.5V-FP8 --revision 3ca028eac7af91c53109dcfa865e5c8da7b1faf3 --local-dir ./GLM-4.5V-FP8
transformersLoad the weights in Python.
import os os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com" from transformers import AutoModelForImageTextToText, AutoProcessor processor = AutoProcessor.from_pretrained("zai-org/GLM-4.5V-FP8", revision="3ca028eac7af91c53109dcfa865e5c8da7b1faf3") model = AutoModelForImageTextToText.from_pretrained("zai-org/GLM-4.5V-FP8", revision="3ca028eac7af91c53109dcfa865e5c8da7b1faf3", device_map="auto")
vLLMServe an OpenAI-compatible API.
$ HF_ENDPOINT=https://freeweights.allternit.com vllm serve zai-org/GLM-4.5V-FP8 --revision 3ca028eac7af91c53109dcfa865e5c8da7b1faf3

License

Name
MIT License
Id
MIT
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
2d97af11c3c1a32c1dfaf2976891fff647499f3bacb638ad958c73cd95ae5a2c
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: zai-org <huggingface.co/zai-org>Newsgroups: alt.freeweights.glmSubject: GLM-4.5V-FP8Date: 2026-11-15Commit: 3ca028eSigned: 2DC28CC7B26A283B ✓
Upstream
zai-org/GLM-4.5V-FP8
Pinned commit
3ca028eac7af91c53109dcfa865e5c8da7b1faf3
Manifest
GLM-4.5V-FP8.json · .minisig
Manifest sha256
c2c28f99eeee44686df4d4029c6c97b0e45bd7f852d59ed8ff27ea330b42fd8c
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 05:08 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/zai-org/GLM-4.5V-FP8.json $ curl -O https://freeweights.allternit.com/manifests/zai-org/GLM-4.5V-FP8.json.minisig $ minisign -Vm GLM-4.5V-FP8.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

Parent

zai-org/GLM-4.5-Air-Base · this model is a quantization of it

No model in the catalog names this one as its parent.

Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download zai-org/GLM-4.5V-FP8
Open in Freeweights Studio

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

Signed manifest
minisign, key 2DC28CC7B26A283B. GLM-4.5V-FP8.json. Provenance. Takedown history: none.
From: bartowski <huggingface.co/bartowski>Subject: arcee-ai_GLM-4-32B-Base-32K-GGUF
From: ggml-org <huggingface.co/ggml-org>Subject: AutoGLM-Phone-9B-GGUF
From: zai-org <huggingface.co/zai-org>Subject: cogagent-chat-hf
From: zai-org <huggingface.co/zai-org>Subject: CogView3-Plus-3B
From: zai-org <huggingface.co/zai-org>Subject: cogvlm-base-490-hf
From: laion <huggingface.co/laion>Subject: dev_set_part1_10k_glm_4_7_traces_jupiter