4.45 TB
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Updated 19 days ago
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glm52.imatrix1.13 GB
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glm52-Q8_0-00020-of-00020.gguf3.53 GB
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glm52-Q8_0-00001-of-00020.gguf41.8 GB
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glm52-Q6_K-00002-of-00015.gguf43.2 GB
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glm52-Q6_K-00001-of-00015.gguf43.3 GB
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glm52-Q4_K_M-00011-of-00011.gguf16.5 GB
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glm52-Q4_K_M-00006-of-00011.gguf44.3 GB
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glm52-Q4_K_M-00005-of-00011.gguf43.2 GB
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glm52-Q4_K_M-00004-of-00011.gguf44.3 GB
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glm52-Q4_K_M-00003-of-00011.gguf43.2 GB
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glm52-Q4_K_M-00002-of-00011.gguf42.5 GB
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glm52-Q4_K_M-00001-of-00011.gguf44.2 GB
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glm52-IQ4_XS-00010-of-00010.gguf7.01 GB
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glm52-IQ4_XS-00009-of-00010.gguf43.7 GB
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glm52-IQ3_XXS-00009-of-00009.gguf19.9 GB
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glm52-IQ3_XXS-00001-of-00009.gguf44.7 GB
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glm52-IQ2_M-00007-of-00007.gguf30.9 GB
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README.md

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
Total size
4.45 TB
Files
114
Last updated
Jul 22
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Contributors