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

GLM-4.5-Air-Base

Date: 2026-11-14Commit: 888c873Signed: 2DC28CC7B26A283B ✓

Text generation model with 110.5B parameters. Safetensors weights, 221 GB in all. Published by zai-org under MIT.

110.5B params221GBGLM-4.5-Air-Basesafetensorspermissive
FactsParams110.5BSize221 GBFormatssafetensorsTasktext generationLicenseMITFrom sourcepermissive
$ fw get zai-org/GLM-4.5-Air-Base
# manifest sha256 a2ccd11ff8ef… · 221 GB · 48 files
$ fw verify ./GLM-4.5-Air-Base zai-org/GLM-4.5-Air-Base
From sourceHosted (signed)zai-org/GLM-4.5-Air-BaseLast check: 2026-11-14 21:25 UTC

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

Works with

Overview

Size
221 GB
Weights
221 GB (safetensors)
Parameters
110.5B
Formats
safetensors
Task
text generation
License
MIT (permissive)
Files
48, each with a sha256
Publisher
zai-org
Added
2026-11-14
Section
alt.freeweights.glm
Lineage

Original release from the publisher.

Where these bytes come from
Publisher at the pinned commitโ†’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.co2026-11-14 21:25 UTCpassed

Model card

From the publisher at commit 888c873. Shown as plain text with links; images open at the publisher.

GLM-4.5-Air-Base#

Image: image

๐Ÿ‘‹ Join our Discord community.

๐Ÿ“– Check out the GLM-4.5 technical blog, technical report, and Zhipu AI technical documentation.

๐Ÿ“ Use GLM-4.5 API services on Zhipu AI Open Platform.

๐Ÿ‘‰ One click to GLM-4.5.

Model Introduction#

The GLM-4.5 series models are foundation models designed for intelligent agents. GLM-4.5 has 355 billion total parameters with 32 billion active parameters, while GLM-4.5-Air adopts a more compact design with 106 billion total parameters and 12 billion active parameters. GLM-4.5 models unify reasoning, coding, and intelligent agent capabilities to meet the complex demands of intelligent agent applications.

Both GLM-4.5 and GLM-4.5-Air are hybrid reasoning models that provide two modes: thinking mode for complex reasoning and tool usage, and non-thinking mode for immediate responses.

We have open-sourced the base models, hybrid reasoning models, and FP8 versions of the hybrid reasoning models for both GLM-4.5 and GLM-4.5-Air. They are released under the MIT open-source license and can be used commercially and for secondary development.

As demonstrated in our comprehensive evaluation across 12 industry-standard benchmarks, GLM-4.5 achieves exceptional performance with a score of 63.2, in the 3rd place among all the proprietary and open-source models. Notably, GLM-4.5-Air delivers competitive results at 59.8 while maintaining superior efficiency.

Image: bench

For more eval results, show cases, and technical details, please visit our technical blog. The technical report will be released soon.

The model code, tool parser and reasoning parser can be found in the implementation of transformers, vLLM and SGLang.

Quick Start#

Note: This is a base model, not for chat.

Please refer to our github page for more details.

Files

48 files ยท 221 GBWeights 221 GB (safetensors). Sizes and sha256 from the signed manifest.
Show all 48 files with their sha256
FileSizesha256
.gitattributes1.57 KB34448b82c17d60fec9b65b1f093c115ddbaadc04beb1b0140b6bfed2e012a930
README.md2.94 KBd67fc5a99462f4164a9ca9b027241fc20b89106472790214ae11bf61fce83c4f
config.json1.01 KBe59f21d7be4b4b917d80bb3a2d1211356d29cf3aeff93372523daf7d0ffb839d
model-00001-of-00042.safetensors5.36 GBf52d67fd6f3bfe3d52e95bbcdf769dadf8e1a10b40a83a78136f2bd8757f8b12
model-00002-of-00042.safetensors5.36 GB9c3c19e04a0a4a9626e7c7ebde5cf56c27057d587dc36e4d1f1c52ab51c90140
model-00003-of-00042.safetensors5.36 GBc96a9d31aac3ef994558372b2d902db1a685b23bb5d2d176b5ffa25c96136d48
model-00004-of-00042.safetensors5.36 GB367f1e77ae878ff7db43e03d6a2611206b1d3e8babad17b9c30cf2eb07db6c55
model-00005-of-00042.safetensors5.36 GB5b7d734c07f5f1bdf0ff7d1f416e16f712212dc8e35bf304eddf4c7b9e062d40
model-00006-of-00042.safetensors5.36 GBdd4f661bf11e972f9d0312ae915a67c3398134a96cd85456f94b4eafa51aba60
model-00007-of-00042.safetensors5.36 GB3355a5414acec2ec3c3e5dd46ac424c1895958a6bebd7e314eedf49752f1ef7a
model-00008-of-00042.safetensors5.36 GBcd7b8a580139ec57800548b13910b5769616d17a61bba912f7a403c830ad0a71
model-00009-of-00042.safetensors5.36 GB4f0c4e66057e92f1b7dda72d405ea99c6cb3eb6275b4fb1c715f063826a12eff
model-00010-of-00042.safetensors5.36 GBf30c60a4d4e7b7575fe9002b380fa4435c6f28372965286dc106eca54c81d179
model-00011-of-00042.safetensors5.33 GB859b8be9d8faaca681f7c7b9baadc9d6297b73898e584f1f33cac189a45ee03e
model-00012-of-00042.safetensors5.37 GB5a00561dd2839dea3351265c758de75901ad09be2c1b3491f93f7049ee9b6819
model-00013-of-00042.safetensors5.36 GB1ccd082b10de471dce62f6d827de5e4e1a188924482e4b3ac8c2dcdfd0cda0dd
model-00014-of-00042.safetensors5.36 GBcc2ab65f576261fe81f80d82c597fd33988eb06bf70407fb90d2d7ee8c4fd166
model-00015-of-00042.safetensors5.36 GBaac5761f998f4a915160decc30420091e66c7aa516d2cb66398917bed38961ae
model-00016-of-00042.safetensors5.36 GB4784bed1eaf00d3804e3d8273673f26c5ca9a7a0783824b6dbd8448aa79bbc6a
model-00017-of-00042.safetensors5.36 GB0aa673751c52654a164159c3640eadf6de7c5a0f8196995039ef02eca3053001
model-00018-of-00042.safetensors5.28 GBce01b760a9bb1ecda8d2b615ba960e0e108cddf0500eae3cd301de7a2ea50545
model-00019-of-00042.safetensors5.37 GB6ac7e427f10a50c2ab2f2a1c96513f2698ec5920edad24292ac93f56782bf16f
model-00020-of-00042.safetensors5.36 GB75375b4773b3885fd33f63a706809e22a0967dbdeec3f71be60406da32bc3f73
model-00021-of-00042.safetensors5.36 GB4520ed17a7b736e89dd95db37cf62c57082bf20e41d5fd9308dadc11f89d38ca
model-00022-of-00042.safetensors5.36 GB46ec2fcf9d4570929ab095726ab76056e1196e5b90def364c36b32d5e4812ca6
model-00023-of-00042.safetensors5.36 GB07a1ae51227071f50ef8f4499b75666dbcf9b7ecacc295ffd3e0d49cd74fc480
model-00024-of-00042.safetensors5.36 GB504dca906c3428c8a8f0cb67f62be81180c8b59d3ce789668dd56dc1d8673e1b
model-00025-of-00042.safetensors5.28 GBd7887dacd5bb5990c777d383cc83221f7996576497228dddc8298ec763151aef
model-00026-of-00042.safetensors5.37 GBc20e85f376d5893cac319eb278dc6fdaf693282bc1d6294193961f2a38c50a3e
model-00027-of-00042.safetensors5.36 GB2ca9802551e766d8d875dfe7210045b343530798eca7ed215bfe7bccbc79af6e
model-00028-of-00042.safetensors5.36 GB9b26d414039318c2c97dc70d21bf11ce7bb875547c221cb96578b52b97d481f2
model-00029-of-00042.safetensors5.36 GBbd3e252fc075806916810fdfa9fb0ad3242634e7fbc6aef9ca027b20ef9e246a
model-00030-of-00042.safetensors5.36 GBefb24efe3f4f6d08d8998b3891dfb1d130ac8d80031b44abf05b0ce2470dd586
model-00031-of-00042.safetensors5.36 GB735f98f0ec761700d735afd893751cd6874eec5fa092590e0ea70e9abdb24715
model-00032-of-00042.safetensors5.28 GBbfe9ce0a0c76b618e7f4e75be654ffd80d6d5fa2a59ef0c3446821d5903c1ebf
model-00033-of-00042.safetensors5.37 GB2a422750f6a9b10e89ed4e8a266bad099869cb54fdf1d8d1136fbc1a80838d0e
model-00034-of-00042.safetensors5.36 GB889998d4ee7af396c4f54f54c17ab8f4d011ba3d5e304fac6277f4d6e48b7a01
model-00035-of-00042.safetensors5.36 GB42c92dffcf2be0b11c0506e718819b21c5d5d8f9a5c26e2f246fbc3a583a195f
model-00036-of-00042.safetensors5.36 GB8143adf27b0d60a9a57f96c248edf3bf8b714e93ba6dbb7bc3041f7e1aee0c47
model-00037-of-00042.safetensors5.36 GB40cfc7048598cce68cb5449d7ab13282ad95282ba597dbccbef4f97fe0bdc509
model-00038-of-00042.safetensors5.36 GBd3c410fb5d7cd57839e6dc9beb00c3ab576a01d55b9b546eb50704fbc7c4b581
model-00039-of-00042.safetensors5.28 GB353b78b567f7486ef87a8f474ff167ac25773b136f8e2379177875e565c300d0
model-00040-of-00042.safetensors5.36 GBa8e9454564fd5e66ce9030146ae25c9163eaa657dc2cb9dd6ed48f0e518f6c14
model-00041-of-00042.safetensors5.36 GBc1a645d42472323dfefc4d19224b4cdf1196e5a0fce4c7966a499de8c9730bfa
model-00042-of-00042.safetensors1.41 GB1ee4f7c3bc9ad12a8c426db2fdecdc52addc7d551f330fd1dc65b2a7b28d3b3b
model.safetensors.index.json1.65 MB47821900d0ef049f42c065c81d5c151e0aa5596b2f3fb819e191698ea25b68c4
tokenizer.json20.0 MB9340665016419c825c4bdabbcc9acc43b7ca2c68ce142724afa829abb1be5efd
tokenizer_config.json7.31 KB27d01309c0ae7b5edb6511daabfcbf772b60e21c65cbb74d059acc76796f7897
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
888c873d4e pinned now manifest updated, same commit2026-11-14 21:25 UTC48 files ยท 221 GB
888c873d4e2026-11-14 21:25 UTC48 files ยท 221 GB

Use it

$ fw get zai-org/GLM-4.5-Air-Base
# manifest sha256 a2ccd11ff8ef… · 221 GB · 48 files
$ fw verify ./GLM-4.5-Air-Base zai-org/GLM-4.5-Air-Base
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-4.5-Air-Base.json --dir ./GLM-4.5-Air-Base $ python3 fw.py verify ./GLM-4.5-Air-Base ./zai-org__GLM-4.5-Air-Base.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download zai-org/GLM-4.5-Air-Base --revision 888c873d4eca81f28d0ef420aa2d96457c28b959 --local-dir ./GLM-4.5-Air-Base
transformersLoad the weights in Python.
import os os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com" from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("zai-org/GLM-4.5-Air-Base", revision="888c873d4eca81f28d0ef420aa2d96457c28b959") model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-4.5-Air-Base", revision="888c873d4eca81f28d0ef420aa2d96457c28b959", device_map="auto")
vLLMServe an OpenAI-compatible API.
$ HF_ENDPOINT=https://freeweights.allternit.com vllm serve zai-org/GLM-4.5-Air-Base --revision 888c873d4eca81f28d0ef420aa2d96457c28b959

License

Name
MIT License
Id
MIT
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-14
Text in
README.md
Text sha256
d67fc5a99462f4164a9ca9b027241fc20b89106472790214ae11bf61fce83c4f
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-4.5-Air-BaseDate: 2026-11-14Commit: 888c873Signed: 2DC28CC7B26A283B ✓
Upstream
zai-org/GLM-4.5-Air-Base
Pinned commit
888c873d4eca81f28d0ef420aa2d96457c28b959
Manifest
GLM-4.5-Air-Base.json ยท .minisig
Manifest sha256
a2ccd11ff8ef37cfe8b297ddd31dd6b6bd44342b6d6a0f485ea7bcfa04f19621
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-14 21:25 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

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

3 models in the catalog name this one as their parent.

Fine-tunes
2
Quantizations
1
Quantizations 1
Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download zai-org/GLM-4.5-Air-Base
Open in Freeweights Studio

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

Signed manifest
minisign, key 2DC28CC7B26A283B. GLM-4.5-Air-Base.json. Provenance. Takedown history: none.

Variants

1 quantized or adapter build of GLM-4.5-Air-Base. Each has its own license check and page.

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