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Zynerji/
Ektome-Qwen3.8-27B-PristinelyUncensored

Name: Ektome-Qwen3.8-27B-PristinelyUncensored
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GGUF status — 2026-08-15

Q4_K_M has been rebuilt and re-uploaded. It is correct. If you downloaded any GGUF from this repo before 2026-08-15 01:00 UTC, delete it and pull again.

The earlier files failed with error loading model: check_tensor in llama.cpp / LM Studio, with MTP on or off. The header declared block_count=65 / nextn_predict_layers=1 while the file shipped only blk.0..63 and no MTP tensors — the header disagreed with its own contents, so the block-count check failed before MTP was ever consulted. Root cause was upstream: the multi-token-prediction head was lost when the abliterated model was saved, so metadata (read from config.json) and tensors disagreed.

The rebuilt Q4_K_M is verified: header block_count=65 matches 65 actual blocks, blk.64 (the MTP block) is present, and it loads and generates.

The bf16 safetensors were never affected — 1199 tensors, exact parity with Qwen/Qwen3.8-27B.

Reported and correctly diagnosed by MrGuzzy and Scottbyrd. Thank you both.

Quantized siblings

repo	format	size	max context on 24 GB	runtime
this repo	bf16 safetensors	~54 GB	—	transformers, vLLM, anything
-HOMEUSER-16-24	mixed-precision int4	14.7 GB	262,144	patched vLLM (fork)
-GPTQ-MTP	4-bit GPTQ / Marlin	17.7 GB	weight-limited	stock vLLM / SGLang
-HYBRID	mixed-precision	18.7 GB	weight-limited	stock vLLM
GGUF Q4_K_M	llama.cpp	16.8 GB	—	llama.cpp, LM Studio, Ollama

Which one do you want?

Long context, and you'll install a fork → HOMEUSER-16-24. It is the only build that quantizes embed_tokens and lm_head — two 248320 x 5120 tensors every other release leaves in bf16 — which frees ~6 GB for KV cache and is why 262,144 tokens fit on a 24 GB card. Measured on eight GPUs (5090 / 4090 / 3090 Ti / 3090 / A4000 / 5060 Ti / 5070 Ti / 4080 Super); MMLU 0.7995, HumanEval 0.8902, MMStar 0.6633. Stock vLLM cannot bind those two tensors, hence the fork.
You want unmodified vLLM or SGLang → GPTQ-MTP. 72.6 tok/s at batch 1 / 512.1 at batch 8 on an RTX PRO 6000 (sm_120), vLLM 0.27.1. It cannot be loaded by transformers — the [48, 5120] DeltaNet projections are excluded from Marlin via GPTQModel's dynamic config, which only vLLM and SGLang understand. See that repo's card for the two load requirements.
llama.cpp / LM Studio / Ollama → the GGUF.

MTP (multi-token prediction) — present, and off unless you enable it

These weights include the complete 15-tensor MTP head, byte-identical to Qwen/Qwen3.8-27B (Ektomē edits the language decoder only). Speculative decoding is not automatic — vLLM ignores the head unless you ask for it:

python

from vllm import LLM
llm = LLM(
    model="Zynerji/Ektome-Qwen3.8-27B-PristinelyUncensored",
    speculative_config={"method": "qwen3_5_mtp", "num_speculative_tokens": 1},
)

vllm serve Zynerji/Ektome-Qwen3.8-27B-PristinelyUncensored   --speculative-config '{"method":"qwen3_5_mtp","num_speculative_tokens":1}'

Measured on the 4-bit GPTQ sibling (RTX PRO 6000, vLLM 0.27.1, greedy): +35% at batch 1, +30% at batch 8, and +63% on long-form code generation — the gain is largest on structured output, because the draft head is accepted more often there.

⚠️ Those figures are from the 4-bit build, not from these bf16 weights. bf16 inference is more compute-bound, so the speedup here may differ — we have not measured it. The mechanism and the invocation are identical; only the magnitude is unverified for bf16.

One caveat that applies to both: at greedy decoding, speculative and non-speculative output are each individually deterministic but differ from one another. Pick one configuration and stay on it if you cache completions or run evaluation harnesses.

What this bf16 contains

1199 tensors — exact parity with Qwen/Qwen3.8-27B, including the 15-tensor MTP (multi-token-prediction) head and the full vision tower. Verified tensor-by-tensor against base.
128 residual-write matrices excised (o_proj / out_proj / down_proj), language decoder only. The MTP head, the vision tower, and the 2 MTP-block matrices that pass a naive dim check were all left untouched.
Ektomē receipts: refusal compliance 0.000 → 1.000 (n=100), MMLU-val 0.812 → 0.818 (Δ +0.005 against SE 0.020 — capability held, not improved), generative gate clean (foreign 0.000, degeneration 0.000, instruction-following 0.400 → 0.400). Winner A:frac=0.6.
This is the parent artifact: every quantized sibling is derived from these weights, so any fine-tune, re-quantization or re-abliteration should start here rather than from a GGUF.

The receipt — pristine vs Ektomē (before / after)

model	capability (MMLU-val, 0-shot) ↑	refusal on harmful ↓
pristine Qwen3.8-27B (before)	0.812	100%
Ektomē (this model)	0.818	0%

Fully uncensored, capability +0.005 vs pristine. Capability = MMLU-validation loglikelihood accuracy; refusal on harmful = AdvBench, judge-free keyword classifier, thinking disabled so chain-of-thought can't mask the answer.

How we know it's real — the catcher's verdict

Every claim here is gated by a surrogate-null catcher, not asserted. The excision is applied to pristine and measured; a capability change counts only if it beats the MMLU sampling floor.

📋 Show the raw before/after

text

$ ektome_abliterate  Qwen/Qwen3.8-27B
  PRISTINE (before): compliance=0.000   mmlu-val=0.812
  EKTOME   (after):  compliance=1.000   mmlu-val=0.818   (dcap=+0.005)
  -> uncensored AND capability preserved, zero training.

Why it's different

Uncensored — complies on harmful prompts the base refuses.
Capability-preserved — capability stays close to the pristine base, not the capability-lower crude-abliterated models most tools ship.
Zero training — a single surgical weight edit on pristine, seconds of compute.
Honest — every claim is gated by a surrogate-null test, not marketing.

Run it locally

python

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("Zynerji/Ektome-Qwen3.8-27B-PristinelyUncensored")
model = AutoModelForCausalLM.from_pretrained("Zynerji/Ektome-Qwen3.8-27B-PristinelyUncensored", torch_dtype="bfloat16", device_map="auto")

GGUF (any gaming PC): Q4_K_M (16.8 GB) — verified to load in llama.cpp / LM Studio / Ollama. 4-bit GPTQ/Marlin (17.7 GB, vLLM or SGLang only) is available as a separate repo and is the fastest option — see its card for measured throughput. LM Studio / Ollama.

The Ektomē line & feedback

If this is useful, a ❤️ like helps others find it. It's part of a line — the same catcher-gated surgery across frontier small models (Qwen, Mistral, Llama, Nemotron) and larger bases. Want a specific base next or have head-to-head numbers? Open a Discussion — the line is steered by what people actually run.

Honest notes

Uncensored: this model follows instructions the base refuses. Use it lawfully and responsibly; you are accountable for what you generate.
Compliance uses a judge-free keyword classifier (thinking disabled); the capability delta is gated against a surrogate-null noise floor.
Method: produced by Ektomē, a capability-preserving surgical-abliteration method. Results are reported; the full recipe is not (yet).

License

Inherits the base model's license (Qwen3.8-27B → apache-2.0). No warranty; your use, your responsibility.