license: other license_name: glm-5.3 license_link: LICENSE base_model:

zai-org/GLM-5.3
dealignai/GLM-5.3-UNCENSORED-FP8 tags:
glm
glm-5.3
glm-dsa
moe
gguf
uncensored
abliterated
maya language:
en
zh
ru pipeline_tag: text-generation

GLM-5.3 Uncensored — Maya quant (G-M24U-GPTQ)

The full GLM-5.3 (753B MoE) quantized to ~2.5 bits with GPTQ, with refusals removed, built to run on the GLM-5.3 Uncensored on Maya engine — a fork of Project Maya ported to the full GLM-5.3.

43–57 tokens/s on 7 GPUs (5× RTX 4090 48 GB + 2× A100 40 GB), 512K context, 2 / 40 refusals on JailbreakBench (stock: 39 / 40).

Files

File	Size	What
`GLM-5.3-Maya-G-M24U-GPTQ-0000{1..6}-of-00006.gguf`	267 GB	the quantized model (llama.cpp `glm-dsa` tensor layout)
`pack/`	2.8 GB	the Maya engine's index of the shards + dense weights + tokenizer + chat template; it finds the shards next to itself
`dir3.f32`	5.8 MB	the refusal vector, 78 layers × 3 directions × 6144, float32 (applied at runtime)

Run

git clone https://github.com/denisnotes357-prog/GLM-5.3-Uncensored-Maya && cd GLM-5.3-Uncensored-Maya
./setup.sh                      # builds the engine (decline the Flash model download it offers)
hf download nofacedfl/GLM-5.3-Uncensored-Maya --local-dir models/GLM-5.3-Uncensored-Maya
tools/glm53/run-glm53.sh        # chat at http://127.0.0.1:8095, OpenAI and Anthropic APIs

The shipped config needs ~320 GB of VRAM across 7 GPUs for full speed; with less, the engine serves the experts that do not fit from RAM (slower). The tested runtime is the Maya engine; llama.cpp compatibility of this exact file was not tested.

How it was made

Quant (G-M24A-GPTQ). FP8 calibration statistics on 128 sequences (chat, multilingual, reasoning, code, tool calls) → imatrix for every tensor and GPTQ for the experts' gate/up. Experts IQ2_S gate/up and IQ3_XXS down (IQ3_S down in layers 3–6 and 74–77); attention and shared expert Q4_K, Q8_0 on the layers placed on the A100s; NextN Q3_K/Q4_K; router and norms F32. Against FP8 on 8 held-out texts: KL 0.204, top-1 agreement 88.4%.
Donor (G-M24U). dealignai/GLM-5.3-UNCENSORED-FP8 changes only self_attn.o_proj and mlp.shared_experts.down_proj (155 tensors); those were quantized to the same types and swapped in. Refusals 39 → 6 of 40, speed unchanged.
Refusal vector (dir3.f32). Three directions per layer measured on 256 harmful / 100 harmless prompts (method of alesha-pro), projected out of the residual stream after every layer by the engine. Refusals 6 → 2 of 40.

Full story, numbers and dead ends: HOW-IT-WAS-MADE.md.

Speed (7 GPUs, pipelined MTP window, 400-token answers)

Text	tok/s
English	54–57
Russian	43–45
Chat at temperature 1.0	43–44
1M context	~30

Responsible use

This model answers requests the original refuses. It is published for research, red-teaming, private and creative use by adults. You are responsible for how you use it and for following the laws that apply to you. Outputs can be wrong or harmful; do not rely on them for medical, legal or other high-stakes decisions.

License and credits

Model: GLM-5.3 by Z.AI under the GLM-5.3 License (included in this repo; permissive, with a security-review condition for Model-as-a-Service operators above US$10B revenue).
Donor tensors: dealignai/GLM-5.3-UNCENSORED-FP8, MIT.
Engine: Project Maya by Niko1221 and the Strata contributors, MIT; the GLM-5.3 port in GLM-5.3-Uncensored-Maya, MIT.

GLM-5.3 Uncensored — Maya quant (G-M24U-GPTQ)

The full GLM-5.3 (753B MoE) quantized to ~2.5 bits with GPTQ, with refusals removed, built to run on the GLM-5.3 Uncensored on Maya engine — a fork of Project Maya ported to the full GLM-5.3.

43–57 tokens/s on 7 GPUs (5× RTX 4090 48 GB + 2× A100 40 GB), 512K context, 2 / 40 refusals on JailbreakBench (stock: 39 / 40).

Files

File

Size

What

GLM-5.3-Maya-G-M24U-GPTQ-0000{1..6}-of-00006.gguf

267 GB

the quantized model (llama.cpp glm-dsa tensor layout)

pack/

2.8 GB

the Maya engine's index of the shards + dense weights + tokenizer + chat template; it finds the shards next to itself

dir3.f32

5.8 MB

the refusal vector, 78 layers × 3 directions × 6144, float32 (applied at runtime)

Run

git clone https://github.com/denisnotes357-prog/GLM-5.3-Uncensored-Maya && cd GLM-5.3-Uncensored-Maya ./setup.sh # builds the engine (decline the Flash model download it offers) hf download nofacedfl/GLM-5.3-Uncensored-Maya --local-dir models/GLM-5.3-Uncensored-Maya tools/glm53/run-glm53.sh # chat at http://127.0.0.1:8095, OpenAI and Anthropic APIs

How it was made

Quant (G-M24A-GPTQ). FP8 calibration statistics on 128 sequences (chat, multilingual, reasoning, code, tool calls) → imatrix for every tensor and GPTQ for the experts' gate/up. Experts IQ2_S gate/up and IQ3_XXS down (IQ3_S down in layers 3–6 and 74–77); attention and shared expert Q4_K, Q8_0 on the layers placed on the A100s; NextN Q3_K/Q4_K; router and norms F32. Against FP8 on 8 held-out texts: KL 0.204, top-1 agreement 88.4%.

Donor (G-M24U). dealignai/GLM-5.3-UNCENSORED-FP8 changes only self_attn.o_proj and mlp.shared_experts.down_proj (155 tensors); those were quantized to the same types and swapped in. Refusals 39 → 6 of 40, speed unchanged.

Refusal vector (dir3.f32). Three directions per layer measured on 256 harmful / 100 harmless prompts (method of alesha-pro), projected out of the residual stream after every layer by the engine. Refusals 6 → 2 of 40.

Full story, numbers and dead ends: HOW-IT-WAS-MADE.md.

Text

tok/s

English

54–57

Russian

43–45

Chat at temperature 1.0

43–44

1M context

~30

Responsible use

License and credits

Model: GLM-5.3 by Z.AI under the GLM-5.3 License (included in this repo; permissive, with a security-review condition for Model-as-a-Service operators above US$10B revenue).

Donor tensors: dealignai/GLM-5.3-UNCENSORED-FP8, MIT.

Engine: Project Maya by Niko1221 and the Strata contributors, MIT; the GLM-5.3 port in GLM-5.3-Uncensored-Maya, MIT.