mradermacher/GLM-4.5-Air-REAP-82B-A12B-GGUF overview
About < quantize version: 2 < output tensor quantised: 1 < convert type: hf < vocab type: < tags: < quants: x f16 Q4 K S Q2 K Q8 0 Q6 K Q3 K M Q3 K S Q3 K L Q4…
Runs locally from ~32.23 GB disk (32 GB+ VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| GLM-4.5-Air-REAP-82B-A12B.IQ4_XS.gguf | GGUF | GGUF | 43.47 GB | Download |
| GLM-4.5-Air-REAP-82B-A12B.Q2_K.gguf | GGUF | GGUF | 32.23 GB | Download |
| GLM-4.5-Air-REAP-82B-A12B.Q3_K_L.gguf | GGUF | GGUF | 42.62 GB | Download |
| GLM-4.5-Air-REAP-82B-A12B.Q3_K_M.gguf | GGUF | GGUF | 40.85 GB | Download |
| GLM-4.5-Air-REAP-82B-A12B.Q3_K_S.gguf | GGUF | GGUF | 37.50 GB | Download |
Model Details
| Model ID | mradermacher/GLM-4.5-Air-REAP-82B-A12B-GGUF |
|---|---|
| Author | mradermacher |
| Pipeline | — |
| License | mit |
| Base model | cerebras/GLM-4.5-Air-REAP-82B-A12B |
| Last modified | 2026-10-14T22:44:44.000Z |
Model README
---
base_model: cerebras/GLM-4.5-Air-REAP-82B-A12B
language:
- en
library_name: transformers
license: mit
license_link: https://huggingface.co/zai-org/GLM-4.5-Air/blob/main/LICENSE
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- glm
- MOE
- pruning
- compression
---
About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/cerebras/GLM-4.5-Air-REAP-82B-A12B
<!-- provided-files -->
For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants are available at https://huggingface.co/mradermacher/GLM-4.5-Air-REAP-82B-A12B-i1-GGUF
Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| GGUF | Q2_K | 34.7 | |
| GGUF | Q3_K_S | 40.4 | |
| GGUF | Q3_K_M | 44.0 | lower quality |
| GGUF | Q3_K_L | 45.9 | |
| GGUF | IQ4_XS | 46.8 | |
| PART 1 PART 2 | Q4_K_S | 51.5 | fast, recommended |
| PART 1 PART 2 | Q4_K_M | 56.0 | fast, recommended |
| PART 1 PART 2 | Q5_K_S | 60.3 | |
| PART 1 PART 2 | Q5_K_M | 64.2 | |
| PART 1 PART 2 | Q6_K | 76.1 | very good quality |
| PART 1 PART 2 | Q8_0 | 90.5 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
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