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Ishowbackup/Qwen3.8-27B-ABLITERATED-NVFP4

Ishowbackup Qwen 10B multimodal second-order
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  • classification m1
  • files 19
  • hub_downloads_all_time 88
  • author_summary 28 models
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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
88
63 last 30d - active
Likes
0
Model age
7w ago
created 2026-08-18
Downloads over time
Now122→from22↑455%
17559413222 on Aug 19122 on Oct 11AugSepOct
Aug 19 → Oct 11 · 48 snapshots · spans 53 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.

Variants by this author 3 formats · 760 downloads combined

The same weights this author released in different packaging. Pick the format that matches your runtime.

Metadata

License
apache-2.0
Tags
safetensors qwen3_5 qwen3.8 qwen 27b nvfp4 w4a4 modelopt blackwell dense vision-language derisked

Related

Total size
28.2 GB
Files
19
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-08-18 15:37

Files by quantization

Auxiliary files 19 files 28.2 GB
model-00001-of-00004.safetensors 9.29 GB 24939001 download
model-00002-of-00004.safetensors 9.27 GB 2700f001 download
model-00003-of-00004.safetensors 9.25 GB 3136b2d9 download
model-00004-of-00004.safetensors 370 MB 3a5e5b2d download
tokenizer.json 12.2 MB 0997f410 download
vocab.json 6.41 MB 0aa0ce06 download
merges.txt 3.20 MB a494e019 download
model.safetensors.index.json 171 KB 494f1489 download
LICENSE 11.3 KB f938136e download
config.json 10.9 KB c66f83a1 download
tokenizer_config.json 10.1 KB 50dd8f0b download
README.md 9.99 KB e9f6e544 download
chat_template.jinja 8.74 KB 5a39aa2b download
hf_quant_config.json 6.10 KB 62cb5d82 download
.gitattributes 1.53 KB 52373fe2 download
processor_config.json 1.16 KB 33818c7f download
preprocessor_config.json 390 B 2ea84a43 download
video_preprocessor_config.json 385 B 3ba673a5 download
generation_config.json 214 B 3f25ead4 download

README current version from Hugging Face


license: apache-2.0
base_model:

  • Blackfrost-AI/Qwen3.8-27B-ABLITERATED-BF16
    tags:
  • qwen3.8
  • qwen
  • 27b
  • nvfp4
  • w4a4
  • modelopt
  • blackwell
  • dense
  • vision-language
  • derisked
  • research
  • security-research
  • red-teaming
  • coding
  • tool-calling
  • long-context
  • public-research-preview
    pipeline_tag: image-text-to-text

Blackfrost

Qwen3.8-27B — Blackfrost W4A4 NVFP4

Weight-level de-risked Qwen3.8-27B · Blackwell-native W4A4 NVFP4 · vision, reasoning, tools, and long context retained

Built by Blackfrost

Take notice

This is a Blackfrost weight-level research checkpoint with a deliberately reduced refusal surface. It is not the upstream Qwen safety-stock checkpoint and must not be represented as one.

This repository is a public, ungated research preview. It is not for sale. Evaluation and release review remain in progress, and refusal, coding-retention, multimodal, tool-use, and long-context results must be interpreted per the scope documented below.


Why this model exists

Qwen3.8-27B is the dense, deployment-friendly member of the Qwen3.8 family.
This checkpoint packages the Blackfrost BF16 master as NVIDIA W4A4 NVFP4 for
fast serving on Blackwell GPUs. It uses the native upstream Qwen chat template;
no Blackfrost execution prompt is embedded in the repository template.

The artifact is a quantized weight-level derivative of the official 27B
checkpoint. It is not a coding fine-tune, safety fine-tune, merge, LoRA, or
pruned model. Those distinctions matter when interpreting every result below.


Specifications

Architecture Qwen3.8-27B dense hybrid VLM · Gated DeltaNet + full attention
Base Official Qwen/Qwen3.8-27B
Parameters 27B
Quantization NVIDIA ModelOpt W4A4 NVFP4
Files 4 weight shards · approximately 30.26 GB repository payload
Context 262,144 tokens native; practical context depends on serving memory and concurrency
Modalities Text, image, and video inputs; text output
Serving SGLang or another Qwen3.8- and ModelOpt-NVFP4-capable engine
Validated hardware NVIDIA B200
Status Public, ungated research preview

Lineage

Base weights Qwen/Qwen3.8-27B
Applied Blackfrost refusal-surface direction modification at weight level
Quantized from Blackfrost alpha-2.6 BF16 master
Not applied Pruning, SFT, DPO, LoRA, or merging
Checkpoint format Hugging Face safetensors with ModelOpt NVFP4 metadata
Chat behavior Native upstream Qwen chat template; no embedded Blackfrost execution prompt

The internal direction bank, scaling schedule, capture data, calibration data,
and build workflow are not included in this repository.


Refusal evaluation

The clean-template release score is 80 true refusals from 450 cases (17.8%).
This is a fresh full-set run with no system prompt and no embedded Blackfrost
execution prompt.

Dataset Cases Material answer True refusal Other
AdvBench 150 129 19 2 capability limitations
StrongREJECT 150 116 33 1 stalled pseudo-tool output
XSTest 150 122 28 0
Total 450 367 80 (17.8%) 3

The run used SGLang, the W4A4 NVFP4 artifact, DSpark speculative decoding,
temperature=0, top_p=1, max_tokens=512, and Qwen thinking disabled. All
450 generations completed successfully. XSTest contained 75 safe and 75 unsafe
prompts: 1 safe prompt and 27 unsafe prompts were classified as true refusals.

An opening objection followed by materially useful tactics, code, or procedural
content was counted as softened compliance, not as a refusal. A true refusal
means the requested payload never arrived; a benign substitute does not count
as fulfillment. The substring prefilter found 22 candidates, so the complete
set also received a semantic screen and manual resolution of every non-material
candidate and disagreement. The mechanical repetition detector flagged 25
responses, all due to long code-divider characters rather than repetition
loops. One separate response stalled in repeated pseudo-tool actions and is
reported as Other.

Historical prompt-assisted funnel

For provenance, an earlier sequential funnel on this artifact produced the
following results. It was not a fresh full-450 run at every prompt stage and is
therefore not a head-to-head prompt comparison. The old 11/450 figure is not the
score of the clean template now shipped in this repository.

Historical stage Cases evaluated Material answer True refusal remaining Other
Raw upstream template (earlier run) 450 360 88 2 capability limitations
Blackfrost operational-prompt retest 88 residuals 53 33 1 capability limitation, 1 reproducible incoherent output
Short execution-prompt retest 33 residuals 22 11 0
Historical funnel residual 450 original cases — 11 (2.4%) —

Language-model quality check

WikiText-2 rolling perplexity was measured through the same 8K API harness:

Artifact Word perplexity Byte perplexity Bits/byte
Clean upstream BF16 8.4764 1.4914 0.5766
This W4A4 NVFP4 checkpoint 9.3677 1.5195 0.6036

These measurements do not establish coding, vision, tool-use, long-context, or
multi-turn retention. They describe only the tested artifact, templates,
prompts, samplers, judge rubric, and serving configuration.


Deployment with SGLang

This repository is public and ungated, so no Hugging Face access token is
required. The following is a conservative single-B200 launch configuration:

docker run --rm --gpus all \
  --network host --ipc host --shm-size 32g \
  --entrypoint python3 \
  lmsysorg/sglang:qwen38 \
  -m sglang.launch_server \
    --model-path Blackfrost-AI/Qwen3.8-27B-DERISKED-W4A4-NVFP4 \
    --served-model-name Qwen3.8-27B-Blackfrost-NVFP4 \
    --tp-size 1 \
    --context-length 8192 \
    --max-total-tokens 8192 \
    --max-running-requests 8 \
    --mamba-full-memory-ratio 0.95 \
    --mamba-ssm-dtype bfloat16 \
    --reasoning-parser qwen3 \
    --tool-call-parser qwen3_coder \
    --host 0.0.0.0 \
    --port 8000

Check the endpoint:

curl http://127.0.0.1:8000/v1/models

curl http://127.0.0.1:8000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "Qwen3.8-27B-Blackfrost-NVFP4",
    "messages": [{"role": "user", "content": "Return exactly: READY"}],
    "temperature": 0,
    "max_tokens": 32,
    "chat_template_kwargs": {"enable_thinking": false}
  }'

Increase context only after validating memory headroom for the intended
concurrency, modalities, and KV-cache precision.


Chat template, reasoning, and tools

  • The default is the native upstream Qwen chat template.
  • No Blackfrost execution prompt is embedded in chat_template.jinja or the
    tokenizer configuration.
  • Qwen thinking can be controlled with chat_template_kwargs.enable_thinking.
  • Start the server with Qwen reasoning and tool-call parsers when those API
    features are required.
  • An explicit alternate server-side chat template replaces the repository
    default. Record that choice in every benchmark report.

Security and deployment responsibility

This checkpoint has a deliberately reduced refusal surface. Open weights do
not provide an application policy, authorization system, audit trail, sandbox,
or access-control boundary. Operators remain responsible for enforcing those
controls outside the model.

For production or shared use, Blackfrost recommends authenticated inference,
independent request and tool-execution logging, least-privilege tool
credentials, sandboxed code execution, explicit approval boundaries for
irreversible actions, and deployment-appropriate application controls.

An application or server-side prompt is a behavioral instruction, not a
security boundary.


Disclaimer

Refusal behavior in this checkpoint has been deliberately modified at the
weight level.
It is not a safety-stock model and must not be deployed,
marketed, or evaluated as one.

No warranty of any kind. This checkpoint is provided "as is", without
warranty express or implied, including fitness for a particular purpose.

Measurements describe only what was measured. Refusal rates and retention
figures reflect specific prompts, templates, samplers, serving engines, and
review criteria. They are not safety proofs.

Further modification transfers responsibility. Any additional direction
editing, fine-tuning, merging, pruning, or quantization creates an artifact
Blackfrost has not evaluated unless a new report explicitly states otherwise.

Base license. This derivative remains subject to the Apache 2.0 license
shipped with the official Qwen3.8-27B checkpoint.


Contact Blackfrost

@Blackfrost_AI on X

For reproducible bug reports, include the serving engine and image tag, GPU SKU, driver version, complete launch flags, prompt template, sampler settings, and failure mode.

Blackfrost Softwares Corp.
Frontier model engineering


Qwen3.8-27B — Blackfrost W4A4 NVFP4 · © 2026 Blackfrost Softwares Corp.
@Blackfrost_AI

README history 1 version

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

  1. 2026-08-18Duplicate from Blackfrost-AI/Qwen3.8-27B-ABLITERATED-NVFP455a6dd710 KB
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