license: other
license_name: qwen-community-1.0
license_link: https://huggingface.co/Qwen/Qwen3.8-Flash-Next/blob/main/LICENSE
base_model:

• Qwen/Qwen3.8-Flash-Next
• Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16
  base_model_relation: quantized
  tags:
• omlx
• mlx
• apple-silicon
• qwen3.8-flash-next
• qwen4_exp
• mtp
• abliterated
• uncensored
• derisked
• 4-bit
  pipeline_tag: image-text-to-text
  library_name: mlx

Qwen3.8-Flash-Next-Abliterated-MLX-4bit

A high quality abliterated 4-bit build of Qwen3.8-Flash-Next, optimized for Apple
Silicon with oMLX.

Lineage: Qwen/Qwen3.8-Flash-Next
→ Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16
(abliterated) → this build (4-bit oQ4e).

The vision tower and the MTP (multi-token-prediction) draft head are
both preserved and working, so the build retains image input and speculative
decoding abilites. Quantized with oMLX to oQ4e: iMatrix-calibrated mixed precision, with
per-tensor bit widths rather than a uniform 4 bits.

Abliteration is measured by Blackfrost-AI on the parent
BF16 checkpoint
with the Blackfrost-AI "Qwentium" persona active. This 4-bit
build has yet to be re-measured after quantization, and ships with the stock
Qwen persona rather than the "Qwentium" persona. The upstream Blackfrost-AI "Qwentium" persona is
included as an optional swap to further reduce refusals; see Personas. Prior work on
Qwen3.8-27B-Abliterated-MTPLX
showed Blackfrost-AI abliteration holding at the weight level with the persona removed, with
refusal rate of 8.7% at 1 attempt, 4.2% within 3 attempts, and 1.7% with the
Blackfrost persona. See
Abliteration and refusal surface.

Note: SSD offload of the n-gram table brings it to roughly 76 GB needed in unified memory.

⚠️ Intended use and terms

Published for experimental AI research and AI-safety research only:
red-teaming, refusal and alignment research, interpretability, and quantization
or inference research.

It must not be used for anything illegal, in any jurisdiction, under any
circumstances. The removal of refusal behavior is a research property of the
artifact, not an invitation, a permission, or an endorsement. Nothing about
publishing this model condones or excuses illegal activity.

You are solely responsible for your use of it and for complying with
applicable laws and upstream licence terms. It ships with essentially no safety
refusals: assume it will comply with any instruction, including harmful ones.
Run it locally on hardware you control. Do not expose it as a public endpoint or
deploy it to untrusted users.

Performance

Measured on an M3 Ultra Mac Studio (256 GB) using oMLX. Generation is capped at a
fixed token count with prompts that overrun the cap, so every run generates
exactly that many tokens and rates are directly comparable.

Decode, tokens/second. Same prose prompt throughout, so the rows differ only
in the setting named.

Reasoning xhigh (the default when reasoning_effort is unset):

Reasoning off (enable_thinking: false):

On a technical-explanation prompt, which is more predictable and so accepts more
drafts, MTP reaches 57.3 tok/s (512) and 58.1 tok/s (1024) with reasoning
off at temperature 0, against the same ~26.7 baseline: a 2.13–2.18x speedup.

Decode without MTP is flat at 26.5–26.9 tok/s regardless of reasoning mode,
temperature or length. All the variation comes from MTP, because the speedup
depends on how predictable the text is. Temperature 1 costs the most: it makes
the draft head wrong more often, so acceptance and speedup both fall, and it
costs nothing measurable with MTP disabled.

Only xhigh was benchmarked. medium and low were not measured, and since
reasoning effort governs how many reasoning tokens get generated, they will
change time-to-answer substantially even though decode rate itself is flat.

Prefill runs ~145–225 tok/s on short prompts and ~815 tok/s on 1000-token
prompts, since fixed per-request overhead amortizes over more tokens.

MTP draft head:

Temperature 1 costs roughly 25% of decode throughput when MTP is enabled,
because sampling makes the draft head's guesses wrong more often and acceptance
falls. With MTP disabled, temperature is nearly free.

Abliteration and refusal surface

Refusal behaviour was removed at the weight level upstream BF16 by Blackfrost-AI.
Their reported figures on R1-HARMFUL-BENCH-450:

* These values were measured by Blackfrost-AI on the parent BF16 checkpoint
with the Qwentium persona active. They are not measurements of this build: this
is a 4-bit quantization that ships with the stock Qwen template by default, and
neither quantization effects nor the stock-template configuration were evaluated
for refusal behaviour here.

Refusals have not been re-measured for this build. The configuration that
actually ships here, abliterated weights with the stock Qwen template and no
persona, is exactly the combination neither of the tables above covers.

As the closest available proxy, the sibling
Qwen3.8-27B-Abliterated-MTPLX-4bit
was measured in both configurations on mlabonne/harmful_behaviors (n=520),
single attempt:

Two things that suggests, both unverified for this model. The weights do most of
the work with reasoning on: 8.7% refusal on the stock template means compliance
is a property of the weights, not of prompt framing. And the persona matters far
more with reasoning off, where it moves 29.4% to 3.8%. So if you are running
with enable_thinking: false and want minimum refusals, the persona swap is
worth more than it is with reasoning on.

That is a different model and a different quantization. Do not treat these as
figures for this build.

Personas

Ships with the stock Qwen chat template as the default, which injects no
system prompt at all. The abliterated weights carry the behaviour on their own.

The upstream Blackfrost "Qwentium" persona is included as an opt-in. It is a
~1,200 token compliance-oriented system prompt prepended to every request; on the
sibling 27B build it reduced refusals by roughly a further 5 percentage points
over the abliterated weights alone, so it is a marginal boost rather than the
mechanism.

Switch with the bundled script, then restart your server:

bash switch_persona.sh blackfrost   # persona on
bash switch_persona.sh qwen         # back to stock (default)
bash switch_persona.sh status       # which is active

Run it from the model directory. It is safe against an hf download cache
checkout: it replaces symlinks rather than writing through them, so the shared
blob store is left intact.

The template lives in two places, chat_template.jinja and the chat_template
key inside tokenizer_config.json, and different runtimes read different ones,
so the script switches both. By hand, copy both files of a pair:

With the Blackfrost template active you can substitute your own text without
editing files:

{"chat_template_kwargs": {"blackfrost_system_prompt": "your prompt here"}}

The persona cannot be switched off from within that template, only replaced: an
empty override falls back to the built-in persona. That is why stock is the
default here and the script exists.

Switching changes prompt formatting only. The weights are in the .safetensors
files and are byte-identical either way.

Speed cost of the persona is minimal. Measured on identical prompts with MTP
disabled at a 512-token cap:

It adds about 0.8 s of prompt-processing latency per request and leaves decode
throughput essentially unchanged, since the cost is in reading the extra ~1,000
tokens rather than in generating.

Quick start

Requires oMLX on Apple Silicon.

hf download grant-ai/Qwen3.8-Flash-Next-Abliterated-MLX-4bit \
  --local-dir ~/omlx-models/qwen38-abliterated-4bit

omlx serve --model-dir ~/omlx-models --port 8000

Then use the OpenAI-compatible endpoint:

curl http://127.0.0.1:8000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model": "qwen38-abliterated-4bit",
       "messages": [{"role": "user", "content": "Explain MoE routing."}],
       "max_tokens": 1024, "temperature": 1.0, "top_p": 0.95, "top_k": 20}'

Settings that matter

Reasoning effort defaults to xhigh, the most expensive setting, and is the
largest lever on time-to-answer: at xhigh roughly half the generated tokens are
reasoning rather than answer. Drop to medium or low for quicker replies, or
set enable_thinking: false to turn reasoning off entirely.

MTP is off by default and must be enabled to get the speculative-decoding
speedup. With MTP enabled, temperature-0 output is not bit-reproducible across
runs.

Technical details

How it was made

1. Abliterated BF16 weights from
   Blackfrost-AI/Qwen3.8-Flash-Next-DERISKED-BF16,
   itself derived from
   Qwen/Qwen3.8-Flash-Next.
2. iMatrix measured from the model's own activations, on a calibration proxy
   built on disk because the 335 GB source exceeds the in-memory calibration
   limit.
3. Body and MTP head both quantized with iMatrix-calibrated oQ4e. Standard
   quantization treats every weight as equally important and applies one bit
   width throughout. oQ4e instead measures which weights actually matter to the
   model's outputs and spends more bits on those, so the same file size buys
   better quality. Applying it to the draft head as well as the body keeps the
   head accurate enough to stay useful: acceptance was measured at 71.3%, not
   assumed.
4. Vision tower carried through and verified with a real image request rather
   than a loader check.

Limitations

• Capability benchmarks have not been run; only conversion correctness and
  throughput were verified.
• Refusal behaviour was not measured on this build. "Abliterated" describes the
  upstream weights, not a property this conversion tested.
• With MTP enabled, temperature-0 output varies between runs.
• Speed depends heavily on machine, context length, reasoning effort, and
  thinking mode.

Credits

• Qwen for Qwen3.8-Flash-Next.
• Blackfrost-AI for the abliterated BF16
  weights this build was quantized from.
• oMLX for the oQ mixed-precision quantizer and the Lightning MTP runtime.

Disclaimer and licence

This is an independent, unaffiliated conversion. It is not endorsed by,
developed with, developed by, sponsored by, or associated with Qwen, Alibaba
Cloud, Blackfrost-AI, or oMLX. None of them reviewed, approved, or contributed to
this repository, and none of them bear any responsibility for it.

It does not represent Qwen or Qwen3.8-Flash-Next. The behavior of this model
is not the behavior of the official Qwen release. Refusal behavior has been
removed by a third-party modification, and the source checkpoint's injected
system prompt has been made optional here rather than default. Do not treat any
output of this model as representative of, or attributable to, the upstream model
or any party named in this card. "Qwen" is used solely to identify the base
architecture.

Changes made to the upstream work: quantized to
mixed-precision oQ4e with iMatrix calibration, body and MTP draft head together;
vision tower preserved; the source checkpoint's chat_template.jinja and the
chat_template key in tokenizer_config.json replaced with the stock Qwen3.8
template, with the source checkpoint's injected system prompt retained in the
repository as an optional swap rather than a default. No weights were modified
beyond quantization; the abliteration itself is upstream work.

Licence: Qwen Community License 1.0, inherited from the upstream model and
weights. Your use is also subject to the licence terms of those upstream works.