README.md

metadata

license: mit
base_model: zandenAI/GLM-5.2-FP8-Uncensored
base_model_relation: quantized
pipeline_tag: text-generation
library_name: gguf
language:
  - en
  - zh
tags:
  - gguf
  - glm
  - glm-5.2
  - moe
  - abliterated
  - uncensored
  - quantized
  - imatrix
extra_gated_prompt: >-
  This is a quantized redistribution of a gated research artifact with reduced
  refusal behavior. By requesting access you affirm that you are of legal age in
  your jurisdiction, that you will not use it to generate CSAM, CBRN, or
  mass-harm content, and that you accept the original model license and
  disclaimer.
extra_gated_fields:
  I am of legal age in my jurisdiction: checkbox
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  I accept the original license and disclaimer: checkbox

GLM-5.2-Uncensored - GGUF

As a reaction to the CEO of a frontier model lobbying the media + congress to boogeyman open source models, I release:

Imatrix GGUF quantizations of zandenAI/GLM-5.2-FP8-Uncensored, an abliterated (refusal-removed) build of zai-org/GLM-5.2-FP8 - a 754B-parameter Mixture-of-Experts model.

All credit for the abliteration methodology and the source weights goes to Zanden Kane (@zandenkane). This repository only provides GGUF conversions for local inference.

Method

• Source: FP8 -> dequantized to BF16 -> GGUF (the BF16 source is included here).
• Quants: built with an importance matrix (imatrix) and a dynamic, MoE-aware recipe - experts at the target bit-width, with token-embeddings / output / attention kept higher (Q8_0 / Q6_K).
• Calibration: standard public imatrix calibration corpus.

Files

File	Type	~Size	Notes
glm52-BF16-*.gguf	BF16	~1.5 TB	full-precision source; re-quant from this
glm52-IQ1_M.gguf	IQ1_M	~180 GB	smallest; big-RAM / Mac
glm52-IQ2_M.gguf	IQ2_M	~250 GB
glm52-IQ3_XXS.gguf	IQ3_XXS	~290 GB
glm52-IQ4_XS.gguf	IQ4_XS	~400 GB	best quality-per-byte at 4-bit
glm52-Q4_K_M.gguf	Q4_K_M	~450 GB	robust 4-bit default
glm52-Q6_K.gguf	Q6_K	~617 GB	near-lossless
glm52.imatrix	imatrix	small	roll your own quant levels

(Quants upload as they finish - some may still be in progress.)

Run with llama.cpp

./llama-server -m glm52-IQ4_XS.gguf -ngl 99 -c 16384 --host 0.0.0.0 --port 8080

For the sharded BF16, download all parts and point llama.cpp at the first shard; it auto-loads the rest.

Hardware guide

Quant	Fits on
IQ1_M / IQ2_M	256 GB RAM box, 192-256 GB Mac, or 3-4x A100
IQ3_XXS / IQ4_XS	384-512 GB RAM, 512 GB Mac, or 6-8x A100
Q4_K_M / Q6_K	8x A100 / H100, or large-RAM CPU/offload

Safety

The source model removes general refusals but preserves categorical refusal for CSAM / minor-exploitation content. Use responsibly and in accordance with your local laws. The original license and disclaimer apply in full.

License

MIT (inherited from the base model).

Credits

• Base model: Z.ai / zai-org - GLM-5.2-FP8
• Abliteration + source weights: Zanden Kane - zandenAI/GLM-5.2-FP8-Uncensored
• GGUF conversion (imatrix + dynamic recipe): this repository