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mvid/Huihui-Qwen3.8-27B-abliterated-MTPLX-Q8

mvid Qwen 27B second-order
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Abliteration classifier · v1.0.0
M1
Primary method

Direct removal

No other method signals detected in this model.
Confidence
MEDIUM
Why this label 3 signals
Method inferred from partial signals - repository name, related files, or tag patterns. Producer identity not confirmed; label may sharpen or shift as we gather more evidence.
  • 'abliterated' in name/tags
  • is_gguf=0 (base model)
  • no specific method indicators - defaulting to M1 (most common)
Refusal direction extracted via
Extraction technique

Difference-of-means

Confidence
MEDIUM
Why we say so
primary_method=M1; difference-of-means is the reference extraction for M1/M3 (Arditi 2024)
Downloads · lifetime
697
286 last 30d - stable
Likes
0
Model age
6w ago
created 2026-08-24
Downloads over time
Now800→from216↑270%
187411635858216 on Aug 26800 on Oct 12AugSepOct
Aug 26 → Oct 12 · 48 snapshots · spans 47 days

Genealogy 0 direct forks

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This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Metadata

License
apache-2.0
Tags
mlx safetensors qwen3_5 mtplx apple-silicon speculative-decoding mtp multi-token-prediction abliterated uncensored qwen3 not-for-all-audiences

Related

Total size
27.9 GB
Files
20
Quantizations
1
Registered
2026-08-24 20:02
Last updated on HF
2026-08-24 20:03

Files by quantization

Auxiliary files 20 files 27.9 GB
model-00005-of-00006.safetensors 4.99 GB e1884a56 download
model-00002-of-00006.safetensors 4.99 GB 6249f0d4 download
model-00003-of-00006.safetensors 4.97 GB a0715589 download
model-00001-of-00006.safetensors 4.94 GB a11adc73 download
model-00004-of-00006.safetensors 4.93 GB f6e07a6c download
model-00006-of-00006.safetensors 1.80 GB 2452a44e download
model-vision.safetensors 879 MB 964bef26 download
mtp.safetensors 430 MB 1e350715 download
tokenizer.json 19.1 MB 06b95093 download
model.safetensors.index.json 202 KB 02b63274 download
chat_template.jinja 8.74 KB c0c686f9 download
mtplx_runtime.json 7.87 KB db4b9281 download
README.md 6.92 KB 84bc4dd3 download
config.json 5.62 KB 63ff5db6 download
.gitattributes 1.53 KB 52373fe2 download
tokenizer_config.json 1.13 KB 8d610519 download
preprocessor_config.json 390 B 2ea84a43 download
video_preprocessor_config.json 385 B 3ba673a5 download
processor_config.json 367 B ced889f7 download
generation_config.json 202 B 023756cf download

README current version from Hugging Face


license: apache-2.0
base_model: huihui-ai/Huihui-Qwen3.8-27B-abliterated
base_model_relation: quantized
pipeline_tag: text-generation
library_name: mlx
tags:

  • mlx
  • mtplx
  • apple-silicon
  • speculative-decoding
  • mtp
  • multi-token-prediction
  • abliterated
  • uncensored
  • qwen3
  • not-for-all-audiences

Huihui-Qwen3.8-27B-abliterated-MTPLX-Q8

An MLX INT8 build of huihui-ai/Huihui-Qwen3.8-27B-abliterated
with its native Multi-Token Prediction (MTP) draft head kept intact and packed for
self-speculative decoding on Apple Silicon.

On an M5 Max this decodes at 54.0 tok/s at draft depth 3 versus 17.1 tok/s autoregressive —
a 3.17x speedup
with no separate draft model, because the MTP head ships inside the model.

This model is uncensored. Its safety alignment has been removed. See
Intended use and risks before you deploy it.

Provenance

This build is two transformations away from the original Qwen release, and neither of them
trained anything:

  1. Qwen/Qwen3.8-27B — the original release.
  2. huihui-ai/Huihui-Qwen3.8-27B-abliterated (commit 739e3c5b89) — an abliteration
    of the above, produced with
    Sumandora/remove-refusals-with-transformers.
    Abliteration is a direct weight edit that identifies the model's refusal direction in
    activation space and projects it out. It is not fine-tuning and uses no training data.
    Only layers 18-51 of 64 were ablated; the MTP draft head and the vision tower were
    left unmodified
    .
  3. This repo — an MTPLX forge of the above: INT8 body quantization plus a structurally
    quantized MTP sidecar. No calibration data, no training, no gradient ever computed.

Because huihui's edit never touched the MTP head, this build's draft head is bit-for-bit the
head from the original Qwen release. That is verified, not assumed: see
On the sidecar.

Build recipe

The trunk was forged with MTPLX using mtp_policy: keep_bf16:

{
  "body_bits": 8,
  "body_group_size": 64,
  "body_mode": "affine",
  "mtp_policy": "keep_bf16"
}

The MTP sidecar was then quantized post hoc as a separate structural step, matching how
the official MTPLX reference build was made:

MTP quantization INT8, group size 64, affine, policy all
Quantized matrices 8 — fc, attention q/k/v/o_proj, MLP gate/up/down_proj
Left in BF16 all head norms (input_layernorm, post_attention_layernorm, q_norm, k_norm, mtp.norm, pre_fc_norm_embedding, pre_fc_norm_hidden)
Sidecar size 849,400,403 B BF16 -> 451,270,903 B INT8
Calibration / training none
Trunk weights unchanged by this step

On the sidecar

The quantized sidecar in this repo is byte-identical (SHA-256 1e350715…) to the MTP
sidecar shipped in the official Qwen3.8-27B-MTPLX-Optimized-Quality reference build.

That single fact confirms both halves of the provenance claim above: the huihui abliteration
genuinely left the MTP head untouched, and this repack reproduces the official head
quantization procedure exactly rather than approximating it.

Measured performance

Hardware: Apple M5 Max, 128 GB, macOS 26.5.2. Measured with mtplx forge verify
(mtplx 2.9.1, suite long-code-uncapped, max_tokens 2048, profile sustained, fans
pinned). Single run per depth.

Mode tok/s vs AR Draft acceptance by position
AR (depth 0) 17.06 1.00x —
Depth 1 31.49 1.85x 0.969
Depth 2 45.44 2.66x 0.952, 0.896
Depth 3 (default) 54.01 3.17x 0.973, 0.933, 0.870

Verdict mtp_depth_wins; nothing was flagged as quality-rejected or acceptance-collapsed.
Depth 3 is the shipped default.

The INT8 sidecar was gated against the BF16 sidecar it replaces, on the same machine and the
same harness. It did not regress — it measured better on every axis:

BF16 sidecar INT8 sidecar (shipped)
Depth-3 acceptance 0.951, 0.899, 0.838 0.973, 0.933, 0.870
Depth-3 multiplier 2.88x 3.17x
Depth-3 tok/s 48.89 54.01
Sidecar on disk 849 MB 451 MB

Both sidecars carry identical draft weights up to INT8 rounding, so treat the gap as
single-run variance on a stochastic decode rather than as evidence that quantizing improved
the head. The honest reading is flat or better, and 398 MB smaller.

What has not been measured

No capability, accuracy, or quality benchmark has been run on this model. The numbers
above are decode throughput and draft-acceptance rates only. Neither the INT8 body
quantization's effect on output quality nor the abliteration's effect on capability has been
evaluated here. If quality matters for your use, benchmark it yourself.

Usage

Requires MTPLX on Apple Silicon; the MTP sidecar is
what makes the speedup work, and a generic MLX loader will ignore it.

mtplx setup --model mvid/Huihui-Qwen3.8-27B-abliterated-MTPLX-Q8 --download
mtplx quickstart --port 8000

Sampler shipped in mtplx_runtime.json: temperature 0.6, top_p 0.95, top_k 20;
draft depth 3, profile sustained.

Roughly 28 GB on disk, so budget a 64 GB machine for comfort at long context.

Intended use and risks

This model is abliterated: the refusal direction has been projected out of its weights.
It will not reliably decline harmful, illegal, unethical, or dangerous requests, and it will
often comply fluently and confidently with requests that the original Qwen3.8-27B would have
refused. The safety behavior is removed from the weights, so it cannot be recovered with a
system prompt.

Consequences worth being explicit about:

  • Do not deploy this to untrusted or public users without your own external moderation layer.
  • Removing refusals does not add knowledge. Confidently-worded dangerous output can also
    simply be wrong, which makes it more dangerous, not less.
  • Abliteration is a blunt instrument. It can degrade instruction-following and reasoning in
    ways unrelated to safety, and as noted above, that has not been measured here.
  • You are responsible for what you generate with it and for complying with the Apache-2.0
    terms and applicable law.

It is published for research on alignment, refusal mechanisms, and speculative decoding, and
for adults who understand the above and want an unfiltered local assistant.

License

Apache-2.0, inherited from both parents: Qwen/Qwen3.8-27B and
huihui-ai/Huihui-Qwen3.8-27B-abliterated. Neither the abliteration nor this quantization
adds licensing terms.

Attribution

Powered by MTPLX / https://github.com/youssofal/MTPLX

Thanks to the Qwen team for the base model and the MTP head, to
huihui-ai for the abliteration, and to
Sumandora for the refusal-removal tooling.

README history 2 versions

The author's README evolved over time. Click a version to see its content at that point.

  1. 2026-08-24Update MTPLX Forge README05ad4f06.9 KB
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  2. 2026-08-24Publish MTPLX forged modelc8fbee481 B
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