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Koshkasa/MuXodious_GLM-4.7-Flash-absolute-heresy-APEX-GGUF overview

PSA While APEX in general has previously shown itself to be rather reliable in everyday application, I doubt the mainline APEX repo allocations of attention pr…

llama.cppggufhereticuncensoreddecensoredabliteratedroleplaynatural languagequantizedAPEXtext-generationenzhbase_model:MuXodious/GLM-4.7-Flash-absolute-heresybase_model:quantized:MuXodious/GLM-4.7-Flash-absolute-heresylicense:mitendpoints_compatibleregion:usimatrixconversational

Runs locally from ~10.56 GB disk (12 GB VRAM class GPUs with llama.cpp / guIDE).

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Pipeline
text-generation
Author

Repository Files & Downloads

4 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
GLM-4.7-Flash-absolute-heresy-APEX-iBalanced(Q5_K_M).ggufGGUFGGUF20.21 GBDownload
GLM-4.7-Flash-absolute-heresy-APEX-iCompact(Q3_K_M).ggufGGUFGGUF13.15 GBDownload
GLM-4.7-Flash-absolute-heresy-APEX-iMini(IQ3_XXS).ggufGGUFGGUF10.56 GBDownload
GLM-4.7-Flash-absolute-heresy-APEX-iQuality(Q4_K_M).ggufGGUFGGUF17.75 GBDownload

Model Details

Model IDKoshkasa/MuXodious_GLM-4.7-Flash-absolute-heresy-APEX-GGUF
AuthorKoshkasa
Pipelinetext-generation
Licensemit
Base modelMuXodious/GLM-4.7-Flash-absolute-heresy
Last modified2026-10-13T08:02:42.000Z

Model README

---

language:

  • en
  • zh

library_name: llama.cpp

license: mit

pipeline_tag: text-generation

tags:

  • heretic
  • uncensored
  • decensored
  • abliterated
  • roleplay
  • natural language
  • gguf
  • quantized
  • APEX

base_model:

  • MuXodious/GLM-4.7-Flash-absolute-heresy

base_model_relation: quantized

quantized_by: Koshkasa

---

PSA

While APEX in general has previously shown itself to be rather reliable in everyday application, I doubt the mainline APEX repo allocations of attention precision in particular. GLM-4.7-Flash has rather lean attention tensors. For example, attn_kv_a_mqa, which is of paramount importance, would contibute 16515072 b at BF16 instead of 5308416 b at Q4_K. That's a 61 MB (total, not per layer!) diff in filesize. There are similar gains to be had in other attention tensors.

I'm providing the original APEX allocations in this repo, but an artifact with more conservative attention allocation is in the works. Stay tuned.

What's that?

APEX (Adaptive Precision for EXpert Models) quantizations of MuXodious/GLM-4.7-Flash-absolute-heresy using default configurations provided in the APEX repo as of 10/13/2026.

using imatrix by mradermacher found here

quantized with llama.cpp #1692f9e50

For roleplaying, I personally strongly recommend nohurry's SillyTavern presets for GLM 4.7 Flash models.

Available Files

| File | Profile | Size | Label

|------|---------|------|------|

| GLM-4.7-Flash-absolute-heresy-APEX-iBalanced.gguf | I-Balanced | 21 GB | Q5_K_M |

| GLM-4.7-Flash-absolute-heresy-APEX-iQuality.gguf | I-Quality | 18 GB | Q4_K_M |

| GLM-4.7-Flash-absolute-heresy-APEX-iCompact.gguf | I-Compact | 14 GB | Q3_K_M |

| GLM-4.7-Flash-absolute-heresy-APEX-iMini.gguf | I-Mini | 12 GB | IQ3_XXS |

Disclosure

My only contribution is compute. This is neither my abliteration artifact, nor my quantization methodology. WYSIWYG. Have fun.

Cheers

Z.ai - the base model.

MuXodious - for teaching the model to swear.

EmanuelOverride - for sparking my interest in local quantizations and APEX in particular.

mradermacher - for the imatrix + myriads of quants we all benefit from.

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