alt.freeweights.glm / arcee-ai_GLM-4-32B-Base-32K-GGUF

From: bartowski <huggingface.co/bartowski>Newsgroups: alt.freeweights.glmSubject:

arcee-ai_GLM-4-32B-Base-32K-GGUF

Date: 2026-11-15Commit: 162faaaSigned: 2DC28CC7B26A283B ✓

Text generation model. Gguf weights, 530 GB in all. Published by bartowski under MIT.

glm530GBarcee-ai_GLM-4-32B-Base-32K-GGUFggufpermissive

FactsParamsnot statedSize530 GBFormatsggufTasktext generationLicenseMITFrom sourcepermissive

$ fw get bartowski/arcee-ai_GLM-4-32B-Base-32K-GGUF
# manifest sha256 107adb30fd09… · 530 GB · 31 files
$ fw verify ./arcee-ai_GLM-4-32B-Base-32K-GGUF bartowski/arcee-ai_GLM-4-32B-Base-32K-GGUF

From sourceHosted (signed)bartowski/arcee-ai_GLM-4-32B-Base-32K-GGUFLast check: 2026-11-15 04:59 UTC

Run at home Get help

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

Quantized of GLM-4-32B-Base-32K.

Works with

Run14 Convert1 Evaluate13 Scan12 Apps58

Runs with

Ranked by fit to this model (gguf), then by GitHub stars.

Text Generation WebUI mistral.rs

Ollama

llama.cpp LocalAI

BitNet llamafile gpu-unlock KoboldCpp PocketPal AI llama-swap ik_llama.cpp

Show all 14

RamaLama Maid

Convert with

ggml

Evaluate with

Opik DeepEval Ragas lm-evaluation-harness OpenCompass SWE-bench

simple-evals TruLens HELM

LightEval

Evaluate Weave

Show all 13

Autoevals

Scan with

promptfoo garak Guardrails NeMo Guardrails Giskard DeepTeam AI Fairness 360 Fairlearn Detoxify MCP Scanner AgentDojo ModelScan

Overview Model card Files Use it License Provenance Model tree

Overview

Size: 530 GB
Weights: 8.97 GB (smallest of 27 GGUF variants)
Parameters: not stated
Formats: gguf
Task: text generation
License: MIT (permissive)
Files: 31, each with a sha256
Publisher: bartowski
Added: 2026-11-15
Section: alt.freeweights.glm

Lineage

Original release from the publisher.

Base model: arcee-ai/GLM-4-32B-Base-32K

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

Badge	Host	Last check	Result
From source	huggingface.co	2026-11-15 04:59 UTC	passed

Model card

From the publisher at commit 162faaa. Shown as plain text with links; images open at the publisher.

Llamacpp imatrix Quantizations of GLM-4-32B-Base-32K by arcee-ai#

Using llama.cpp release b5697 for quantization.

Original model: https://huggingface.co/arcee-ai/GLM-4-32B-Base-32K

All quants made using imatrix option with dataset from here

Run them in LM Studio

Run them directly with llama.cpp, or any other llama.cpp based project

Prompt format#

No prompt format found, check original model page

Download a file (not the whole branch) from below:#

Filename	Quant type	File Size	Split	Description
GLM-4-32B-Base-32K-bf16.gguf	bf16	65.14GB	true	Full BF16 weights.
GLM-4-32B-Base-32K-Q8_0.gguf	Q8_0	34.61GB	false	Extremely high quality, generally unneeded but max available quant.
GLM-4-32B-Base-32K-Q6_K_L.gguf	Q6_K_L	27.18GB	false	Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended.
GLM-4-32B-Base-32K-Q6_K.gguf	Q6_K	26.73GB	false	Very high quality, near perfect, recommended.
GLM-4-32B-Base-32K-Q5_K_L.gguf	Q5_K_L	23.67GB	false	Uses Q8_0 for embed and output weights. High quality, recommended.
GLM-4-32B-Base-32K-Q5_K_M.gguf	Q5_K_M	23.10GB	false	High quality, recommended.
GLM-4-32B-Base-32K-Q5_K_S.gguf	Q5_K_S	22.53GB	false	High quality, recommended.
GLM-4-32B-Base-32K-Q4_1.gguf	Q4_1	20.55GB	false	Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon.
GLM-4-32B-Base-32K-Q4_K_L.gguf	Q4_K_L	20.37GB	false	Uses Q8_0 for embed and output weights. Good quality, recommended.
GLM-4-32B-Base-32K-Q4_K_M.gguf	Q4_K_M	19.68GB	false	Good quality, default size for most use cases, recommended.
GLM-4-32B-Base-32K-Q4_K_S.gguf	Q4_K_S	18.70GB	false	Slightly lower quality with more space savings, recommended.
GLM-4-32B-Base-32K-Q4_0.gguf	Q4_0	18.63GB	false	Legacy format, offers online repacking for ARM and AVX CPU inference.
GLM-4-32B-Base-32K-IQ4_NL.gguf	IQ4_NL	18.58GB	false	Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference.
GLM-4-32B-Base-32K-Q3_K_XL.gguf	Q3_K_XL	18.03GB	false	Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability.
GLM-4-32B-Base-32K-IQ4_XS.gguf	IQ4_XS	17.60GB	false	Decent quality, smaller than Q4_K_S with similar performance, recommended.
GLM-4-32B-Base-32K-Q3_K_L.gguf	Q3_K_L	17.21GB	false	Lower quality but usable, good for low RAM availability.
GLM-4-32B-Base-32K-Q3_K_M.gguf	Q3_K_M	15.89GB	false	Low quality.
GLM-4-32B-Base-32K-IQ3_M.gguf	IQ3_M	14.82GB	false	Medium-low quality, new method with decent performance comparable to Q3_K_M.
GLM-4-32B-Base-32K-Q3_K_S.gguf	Q3_K_S	14.37GB	false	Low quality, not recommended.
GLM-4-32B-Base-32K-IQ3_XS.gguf	IQ3_XS	13.66GB	false	Lower quality, new method with decent performance, slightly better than Q3_K_S.
GLM-4-32B-Base-32K-Q2_K_L.gguf	Q2_K_L	13.20GB	false	Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable.
GLM-4-32B-Base-32K-IQ3_XXS.gguf	IQ3_XXS	12.78GB	false	Lower quality, new method with decent performance, comparable to Q3 quants.
GLM-4-32B-Base-32K-Q2_K.gguf	Q2_K	12.29GB	false	Very low quality but surprisingly usable.
GLM-4-32B-Base-32K-IQ2_M.gguf	IQ2_M	11.27GB	false	Relatively low quality, uses SOTA techniques to be surprisingly usable.
GLM-4-32B-Base-32K-IQ2_S.gguf	IQ2_S	10.42GB	false	Low quality, uses SOTA techniques to be usable.
GLM-4-32B-Base-32K-IQ2_XS.gguf	IQ2_XS	9.90GB	false	Low quality, uses SOTA techniques to be usable.
GLM-4-32B-Base-32K-IQ2_XXS.gguf	IQ2_XXS	8.97GB	false	Very low quality, uses SOTA techniques to be usable.

Embed/output weights#

Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

Downloading using huggingface-cli#

Click to view download instructions

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download bartowski/arcee-ai_GLM-4-32B-Base-32K-GGUF --include "arcee-ai_GLM-4-32B-Base-32K-Q4_K_M.gguf" --local-dir ./

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download bartowski/arcee-ai_GLM-4-32B-Base-32K-GGUF --include "arcee-ai_GLM-4-32B-Base-32K-Q8_0/*" --local-dir ./

You can either specify a new local-dir (arcee-ai_GLM-4-32B-Base-32K-Q8_0) or download them all in place (./)

ARM/AVX information#

Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.

Now, however, there is something called "online repacking" for weights. details in this PR. If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.

As of llama.cpp build b4282 you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.

Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to this PR which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.

Click to view Q4_0_X_X information (deprecated

I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.

Click to view benchmarks on an AVX2 system (EPYC7702)

model	size	params	backend	threads	test	t/s	% (vs Q4_0)
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	pp512	204.03 ± 1.03	100%
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	pp1024	282.92 ± 0.19	100%
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	pp2048	259.49 ± 0.44	100%
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	tg128	39.12 ± 0.27	100%
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	tg256	39.31 ± 0.69	100%
qwen2 3B Q4_0	1.70 GiB	3.09 B	CPU	64	tg512	40.52 ± 0.03	100%
qwen2 3B Q4_K_M	1.79 GiB	3.09 B	CPU	64	pp512	301.02 ± 1.74	147%
qwen2 3B Q4_K_M	1.79 GiB	3.09 B	CPU	64	pp1024	287.23 ± 0.20	101%
qwen2 3B Q4_K_M	1.79 GiB	3.09 B	CPU	64	pp2048	262.77 ± 1.81	101%
qwen2 3B Q4_K_M	1.79 GiB	3.09 B	CPU	64	tg128	18.80 ± 0.99	48%
qwen2 3B Q4_K_M	1.79 GiB	3.09 B	CPU	64	tg256	24.46 ± 3.04	83%
qwen2 3B Q4_K_M	1.79 GiB	3.09 B	CPU	64	tg512	36.32 ± 3.59	90%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	pp512	271.71 ± 3.53	133%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	pp1024	279.86 ± 45.63	100%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	pp2048	320.77 ± 5.00	124%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	tg128	43.51 ± 0.05	111%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	tg256	43.35 ± 0.09	110%
qwen2 3B Q4_0_8_8	1.69 GiB	3.09 B	CPU	64	tg512	42.60 ± 0.31	105%

Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation

Which file should I choose?#

Click here for details

A great write up with charts showing various performances is provided by Artefact2 here

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.

If you want to get more into the weeds, you can check out this extremely useful feature chart:

llama.cpp feature matrix

But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.

These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

Credits#

Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.

Thank you ZeroWw for the inspiration to experiment with embed/output.

Thank you to LM Studio for sponsoring my work.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

Files

31 files · 530 GBWeights 8.97 GB (smallest of 27 GGUF variants). Sizes and sha256 from the signed manifest.

Show all 31 files with their sha256

File	Size	sha256
.gitattributes	3.80 KB	748a9240cb33712cbc8e18ee0e1c3795324e2e739decd6fa35d038952bbcf3f0
README.md	14.5 KB	a25c922049567dc5bbfed3a7c4ad2fc422f84f0e4ee0886fa2bbbeb2ce3fab5a
arcee-ai_GLM-4-32B-Base-32K-IQ2_M.gguf	11.3 GB	3d9f399c579e5a575590f0a9e617911551d155df4e415b54ed0cd305e3756a07
arcee-ai_GLM-4-32B-Base-32K-IQ2_S.gguf	10.4 GB	8651c43a0d1e3cbf878d0daf5ee558c32241297fe4e7bf3a4ed4a4f42b8d4fec
arcee-ai_GLM-4-32B-Base-32K-IQ2_XS.gguf	9.90 GB	7c90867e2f3664410d3afc9e52e54f4b0e423a39d40e5a6065906d45c35df1cf
arcee-ai_GLM-4-32B-Base-32K-IQ2_XXS.gguf	8.97 GB	30fba03b56338f43a951ed9c2260e6069d9a3796dc9cb26afea3518c6b148115
arcee-ai_GLM-4-32B-Base-32K-IQ3_M.gguf	14.8 GB	5d6bbf34041e27c10d332b8fcb28205aa6d76e31f6785303a31c8cddfc1b360a
arcee-ai_GLM-4-32B-Base-32K-IQ3_XS.gguf	13.7 GB	41d0423ce173811c337a2faa11df84e166ce82798c72aa0ce82520535a31e333
arcee-ai_GLM-4-32B-Base-32K-IQ3_XXS.gguf	12.8 GB	25d76a84d4b90a364d2ef2b7d9f2b54f911170b65f99dd3a7ef1412233595d06
arcee-ai_GLM-4-32B-Base-32K-IQ4_NL.gguf	18.6 GB	3fc1dde9de8e840d42e3c5aad0df62342c2def859e00ca70a9ec4a8e02f84011
arcee-ai_GLM-4-32B-Base-32K-IQ4_XS.gguf	17.6 GB	7a1fc50c49ce0444c9acda943ae808c710a860ab628fd9f1d4d54cda5676b856
arcee-ai_GLM-4-32B-Base-32K-Q2_K.gguf	12.3 GB	aa8096159e31680e5cc3b6df0fe6250c93bf2b7f5d611884518153cbe0edd58b
arcee-ai_GLM-4-32B-Base-32K-Q2_K_L.gguf	13.2 GB	97529a17e23f9164e34bf429b0d363c3609e2f62cbc2df29113ccef6ab51e30f
arcee-ai_GLM-4-32B-Base-32K-Q3_K_L.gguf	17.2 GB	3d4c8cf2517baeffd8ca71bb17279ff87c2d530862333ff49b5337174be56585
arcee-ai_GLM-4-32B-Base-32K-Q3_K_M.gguf	15.9 GB	66bec6c2578ec76d8f135b674b112119fe7f8b06dea21ce6bccc914f49796503
arcee-ai_GLM-4-32B-Base-32K-Q3_K_S.gguf	14.4 GB	aac67b597780be89067424a6bfa039426279ddeef4e22ba718e634b4b86bff48
arcee-ai_GLM-4-32B-Base-32K-Q3_K_XL.gguf	18.0 GB	bad4f69e3b9587177aaa106e14bf619b99680466017f101c74f7fc5b1e14e6c4
arcee-ai_GLM-4-32B-Base-32K-Q4_0.gguf	18.6 GB	19ec253a5abe0b5003faa14f555f15f132c6113978a03758284c90b2329fc31b
arcee-ai_GLM-4-32B-Base-32K-Q4_1.gguf	20.5 GB	4fade7e9cfdb140fde24794a63fef859f35e144f3bcaf2c8e378d73f94ddfd24
arcee-ai_GLM-4-32B-Base-32K-Q4_K_L.gguf	20.4 GB	df772c3d5e985ed1f07bfe547f49b6837bce4b38727cca3643e71fe70d031f31
arcee-ai_GLM-4-32B-Base-32K-Q4_K_M.gguf	19.7 GB	f6fdc0854ed4ed33bc18190bca8a4b2a808a84884c697ca07bbedbff99dbc7f0
arcee-ai_GLM-4-32B-Base-32K-Q4_K_S.gguf	18.7 GB	e3ced95942f710f9a366097f06f559aee7f2a5ee24e279826b3eba4530cc52cb
arcee-ai_GLM-4-32B-Base-32K-Q5_K_L.gguf	23.7 GB	22cb1adad435cf3ea764d3cbd0081ed0b31aa5c8ea2b16534cc9df5c8b4f5d6c
arcee-ai_GLM-4-32B-Base-32K-Q5_K_M.gguf	23.1 GB	69b43d1597fb54ed48759a253340ba2a3ffbeac241a320083587b93ed59fb14a
arcee-ai_GLM-4-32B-Base-32K-Q5_K_S.gguf	22.5 GB	54ee0b83c5eea510968cd57b47abb4c0fddad40456cac8162cd35dd2a84d4f1e
arcee-ai_GLM-4-32B-Base-32K-Q6_K.gguf	26.7 GB	aee8180a40e4b85e08e405b3cb8785638c1bbb8f5c09c25b5889322b0d832d4f
arcee-ai_GLM-4-32B-Base-32K-Q6_K_L.gguf	27.2 GB	90da920bf39e39962a34c7b5b0b3ef28f5c2475c83920d15a996519973733cf4
arcee-ai_GLM-4-32B-Base-32K-Q8_0.gguf	34.6 GB	39d8396bac41296378e33d3e8f6d71523f31151d1d86731bc2756e4697b8b36c
arcee-ai_GLM-4-32B-Base-32K-bf16/arcee-ai_GLM-4-32B-Base-32K-bf16-00001-of-00002.gguf	40.0 GB	2ac72b5c6b4c233c51f948fb048d0cea236dd1d937ace8d7d1dbad8eb7178f82
arcee-ai_GLM-4-32B-Base-32K-bf16/arcee-ai_GLM-4-32B-Base-32K-bf16-00002-of-00002.gguf	25.2 GB	8d7f0e62e27a77ae26439e05caffd6e1f8942b4c43b36fa43cd25777feae88bc
arcee-ai_GLM-4-32B-Base-32K.imatrix	13.1 MB	80c0cfc912a66acf9a1e8c2ed62e140d5010210dd211b8d178644e84d3ae691b

VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.

Upstream commit	Date	Contents
162faaa510 pinned now	2026-11-15 04:59 UTC	31 files · 530 GB

Use it

$ fw get bartowski/arcee-ai_GLM-4-32B-Base-32K-GGUF
# manifest sha256 107adb30fd09… · 530 GB · 31 files
$ fw verify ./arcee-ai_GLM-4-32B-Base-32K-GGUF bartowski/arcee-ai_GLM-4-32B-Base-32K-GGUF

fw.pyChecks the signature and every sha256, tries each source in order, resumes.

$ python3 fw.py get https://freeweights.allternit.com/manifests/bartowski/arcee-ai_GLM-4-32B-Base-32K-GGUF.json --dir ./arcee-ai_GLM-4-32B-Base-32K-GGUF
$ python3 fw.py verify ./arcee-ai_GLM-4-32B-Base-32K-GGUF ./bartowski__arcee-ai_GLM-4-32B-Base-32K-GGUF.json

hfThe Hugging Face CLI with one setting. Same repo id.

$ HF_ENDPOINT=https://freeweights.allternit.com hf download bartowski/arcee-ai_GLM-4-32B-Base-32K-GGUF --revision 162faaa510cf521bd9766d5536249ee7a1e0ba86 --local-dir ./arcee-ai_GLM-4-32B-Base-32K-GGUF

llama.cppDownload one GGUF variant (arcee-ai_GLM-4-32B-Base-32K-IQ2_XXS, 8.97 GB), then serve it.

$ HF_ENDPOINT=https://freeweights.allternit.com hf download bartowski/arcee-ai_GLM-4-32B-Base-32K-GGUF --include "arcee-ai_GLM-4-32B-Base-32K-IQ2_XXS*" --local-dir ./arcee-ai_GLM-4-32B-Base-32K-GGUF
$ llama-server -m ./arcee-ai_GLM-4-32B-Base-32K-GGUF/arcee-ai_GLM-4-32B-Base-32K-IQ2_XXS.gguf

OllamaMake an Ollama model from the downloaded file.

$ printf 'FROM ./arcee-ai_GLM-4-32B-Base-32K-GGUF/arcee-ai_GLM-4-32B-Base-32K-IQ2_XXS.gguf\n' > Modelfile
$ ollama create arcee-ai-glm-4-32b-base-32k-gguf -f Modelfile

Run at home

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: a25c922049567dc5bbfed3a7c4ad2fc422f84f0e4ee0886fa2bbbeb2ce3fab5a
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: bartowski <huggingface.co/bartowski>Newsgroups: alt.freeweights.glmSubject: arcee-ai_GLM-4-32B-Base-32K-GGUFDate: 2026-11-15Commit: 162faaaSigned: 2DC28CC7B26A283B ✓

Upstream: bartowski/arcee-ai_GLM-4-32B-Base-32K-GGUF
Pinned commit: 162faaa510cf521bd9766d5536249ee7a1e0ba86
Manifest: arcee-ai_GLM-4-32B-Base-32K-GGUF.json · .minisig
Manifest sha256: 107adb30fd094cf4495580014f34bf5218a25e72e90c420bc441bee2dba208d5
Signature: minisign, Ed25519, key 2DC28CC7B26A283B
Created: 2026-11-15 04:59 UTC
Generator: freeweights-manifest/0.1.0
Takedowns: None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/bartowski/arcee-ai_GLM-4-32B-Base-32K-GGUF.json
$ curl -O https://freeweights.allternit.com/manifests/bartowski/arcee-ai_GLM-4-32B-Base-32K-GGUF.json.minisig
$ minisign -Vm arcee-ai_GLM-4-32B-Base-32K-GGUF.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

arcee-ai/GLM-4-32B-Base-32K · 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 bartowski/arcee-ai_GLM-4-32B-Base-32K-GGUF

Open in Freeweights Studio

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

Signed manifest

minisign, key 2DC28CC7B26A283B. arcee-ai_GLM-4-32B-Base-32K-GGUF.json. Provenance. Takedown history: none.

Source

huggingface.co/bartowski/arcee-ai_GLM-4-32B-Base-32K-GGUF

Related models

All of alt.freeweights.glm →

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

From: laion <huggingface.co/laion>Subject: dev_set_part1_10k_glm_4_7_traces_jupiter_cleaned