license: mit language:

• en base_model: dealignai/GLM-5.3-Flash-UNCENSORED-FP8 base_model_relation: quantized tags:
• glm
• moe
• uncensored
• abliterated
• multimodal
• image-text-to-text
• mtp
• 1m-context
• gguf

GLM-5.3-Flash — UNCENSORED — Maya-L GGUF

Quantized GGUF of an existing uncensored checkpoint, for llama.cpp and Project Maya. Nothing here was abliterated by us: the refusal removal is dealignai's work. We took their FP8 checkpoint and quantized it.

Files

Sharded with llama-gguf-split at ≤50 GB per file, which is already under the Hub's per-file limit — download every shard with the same prefix, llama-server -m accepts any one of them and loads the rest.

hf download Adolphsson/GLM-5.3-Flash-UNCENSORED-MTP-Maya-L-GGUF \
  --include "GLM-5.3-Flash-UNCENSORED-MTP-Maya-L*" --local-dir ./glm-l

Naming note

Maya-L is the recipe's tier name (Maya ships its quants as tiers S, M and L), not a uniform type code: ggml has no single type that fits a 156 GB build, so the tier is named after the recipe, and the table below says what each group actually holds. Read the composition before the name.

Per-group quantization (measured from the GGUF headers)

Every group sits at or above the sensitivity class its role warrants; nothing is below the base step of its family.

llama.cpp

llama.cpp spells the architecture glm5next, this GGUF is tagged glm5-next (the checkpoint's own name). Until the alias lands upstream, add one line to llm_arch_from_string in src/llama-arch.cpp:

llm_arch llm_arch_from_string(const std::string & name) {
    if (name == "glm5-next") { return LLM_ARCH_GLM5NEXT; }   // ← add this

Without it the loader rejects the file with unknown model architecture: 'glm5-next'. Nothing else is patched — the model runs on the upstream glm5next graph and its own mmproj path.

llama-server -m GLM-5.3-Flash-UNCENSORED-MTP-Maya-L-00001-of-00004.gguf \
  --mmproj mmproj-GLM-5.3-Flash-F16.gguf -c 32768 -ngl 99 --host 0.0.0.0 --port 9876

Project Maya

Maya reads the same GGUF and needs a derived pack (index + offsets + tokenizer, no weights copied):

python tools/iq_pack.py --gguf GLM-5.3-Flash-UNCENSORED-MTP-Maya-L-00001-of-00004.gguf \
  --out out/maya-glm --compat-bf16
maya --glm-pack out/maya-glm --max-context 1048576 --glm-ram-resident --spec 6

Maya-specific notes:

• The pack must sit next to the shards; the server takes the pack's parent as model dir.
• --glm-ram-resident is what makes it fast (all experts resident in VRAM + pinned RAM, zero disk reads per token). Without it every expert miss hits NVMe.
• --vision plus the maya-vision binary for images; the vision encoder borrows ~7.4 GB of the expert pool while it encodes.
• No engine patch is needed for the quant types: Maya serves Q4_0, Q5_0, Q8_0, Q3_K, Q4_K, Q5_K, Q6_K, IQ2_XXS/IQ2_XS/IQ2_S, IQ3_XXS/IQ3_S, IQ4_NL, IQ4_XS, IQ1_M and Q2_0 natively in its expert and dense paths, and this recipe stays inside that set. Types outside it drop off the fast path and become slow.
• MTP is on through STRATA_GLM_MTP=1. Measured trade-off on one RTX 5090: at 1M context MTP costs ~12 % (the draft block competes with the expert pool that the KV cache already squeezed), at 256K it gains ~16 %. Enable it when you run shorter contexts.

What was verified

Functional checks on the quantized build, RTX 5090, --glm-ram-resident, 1M context:

• arithmetic, factual recall, multilingual prose, Python — all correct
• 420-token narrative and a code-generation loop test: 0 % repeated 3-grams, no degeneration
• vision: image in, coherent description out (--mmproj, 1024 image tokens)
• 1M context loads and serves; KV 12.6 GB, expert pool 9.8 GB, 767 MiB VRAM spare
• MTP block loads and speculates (--spec 6)

No MMLU / GSM8K / perplexity / HarmBench ladder has been run on this build yet — the numbers above are functional and throughput only, and they sit on a single GPU. Treat the quant as functionally checked, not benchmarked. That is the honest state of it.

Throughput, for orientation only (same 260-token prompt, 8 runs, median of the last 6, temperature 0.7, single 5090): 28.5 tok/s at 1M context, 25.9 at 256K with MTP, 22.3 at 256K without. Your hardware will move these more than the flags do.

Provenance

• Original model: zai-org/GLM-5.3-Flash (MIT)
• Abliterated checkpoint: dealignai/GLM-5.3-Flash-UNCENSORED-FP8 (MIT) — the CRACK abliteration, downloaded and SHA256-verified
• Contribution of this repo: quantization only — IQ4-Maya recipe, per-group type map, the mmproj conversion from the FP8 vision tower, and the architecture alias note above. The uncensored behaviour comes from dealignai, not from this repo.
• Built & tested by: Adolphsson

License

MIT at every step of the lineage (Z.AI → dealignai → here). Quantization changes weights numerically, not their provenance. Full text: LICENSE, attribution: NOTICE.