license: apache-2.0
base_model: Qwen/Qwen3.8-27B
base_model_relation: quantized
pipeline_tag: image-text-to-text
library_name: transformers
language:

• en
• zh
  tags:
• abliterated
• qwen
• qwen3
• qwen3.8
• uncensored
• ai-red-team
• red-teaming
• nvfp4
• fp4
• fp8
• mixed-precision
• compressed-tensors
• vllm
• vision-language
• function-calling
• reasoning
• mtp

Qwen3.8-27B-Uncensored-NVFP4

An abliterated (refusal-removed) & dynamic mixed-precision NVFP4 + FP8 build of Qwen's Qwen3.8-27B — for Blackwell FP4

One Gateway. Every Model. — Route Smarter · Ship Safer · Spend Less.

Run via API · API Endpoint · Website · Model Catalog · Model Card · GitHub · Discord · X

An abliterated (refusal-removed) and dynamic mixed-precision NVFP4 + FP8 quantized
build of Qwen/Qwen3.8-27B — a 27B-parameter dense,
hybrid-attention (Gated DeltaNet linear + full attention) native vision-language model with
flexible thinking control, tool-calling, and an MTP speculative-decoding head. This build removes
the safety refusal direction, then quantizes the bulk feed-forward layers to 4-bit NVFP4 while
keeping precision-sensitive layers and the KV cache at FP8, so accuracy is better preserved
than uniform W4A4. 262K context, tools + reasoning + MTP + vision preserved.
Browse all models in the OrcaRouter Model Catalog.

▶ This model is deployed as a hosted API

Run it instantly on OrcaRouter — OpenAI-compatible, no setup, 262K context with tools + reasoning. Endpoint: api.orcarouter.ai/v1 · model qwen/qwen3.8-27b. Grab a key at orcarouter.ai (sk-orca-...).

Sibling releases:  •  Qwen3.8-27B-Uncensored — BF16 source  •  Qwen3.8-27B-Uncensored-FP8 — block-FP8 for vLLM  •  Qwen3.8-27B-Uncensored-GGUF — 2-bit→16-bit for llama.cpp  •  Qwen3.8-27B-Uncensored-MLX — MLX for Apple Silicon (2 / 4 / 8-bit).

⚠️ Disclaimer — read before use

This model has had its safety alignment substantially removed via abliteration
(orthogonalizing the refusal direction out of the residual stream). As a direct consequence:

• It will comply with harmful, unethical, offensive, or illegal requests that the
  original Qwen3.8-27B would refuse. It has no meaningful built-in guardrails.
• It is released strictly for legitimate research — interpretability, AI-safety and
  refusal-mechanism study, red-teaming, robustness evaluation, and controlled experiments.
• You assume full responsibility and liability for how you use it and for everything it
  generates. Do not deploy it to end users or in production without adding your own safety,
  moderation, and abuse-prevention layers.
• Use must comply with the Apache 2.0 License
  inherited from the base model, and all laws and regulations that apply to you.
• The authors and uploaders accept no liability for any misuse or harm arising from this
  model. Its outputs do not reflect the views of the uploaders or of Qwen / Alibaba.

By downloading or using this model you acknowledge and accept the above.

Model details

Abliteration

Refusal-direction removal following Arditi et al. (2024), Refusal in Language Models Is
Mediated by a Single Direction. A single refusal direction r (k = 1) is estimated as the
massive-activation–masked mean-difference of harmful − harmless last-token residuals at
layer 38 (round(0.6 × 64)), on AdvBench (harmful) vs Alpaca (harmless). r is then
orthogonalized out of every residual-writing matrix — W' = W − r(rᵀW) — computed in
float32:

The vision tower is untouched and the MTP head is abliterated consistently with the
main model, so speculative decoding keeps working. This is the same abliterated BF16 base as the
FP8,
GGUF and
BF16 releases — only the
quantization differs.

Dynamic mixed-precision (NVFP4 + FP8) scheme

Rather than quantizing every linear uniformly, precision-sensitive layers are kept at FP8 while
only the bulk feed-forward layers go to 4-bit:

• Weights: round-to-nearest. NVFP4 packs FP4 (E2M1) in groups of 16 with an FP8-E4M3 block
  scale and an FP32 per-tensor global scale; FP8 uses per-output-channel scales.
• Activations: NVFP4 layers use dynamic per-token FP4 with a calibrated global scale; FP8
  layers use dynamic per-token FP8 (no static activation scale).
• KV cache: static per-tensor FP8, calibrated.
• Calibration: 512 samples — 75% tatsu-lab/alpaca
  • 25% compliant harmful completions, enable_thinking=False, sequence length 2048 — used only
    for the NVFP4 activation global scales and the static FP8 KV-cache scales, keeping calibration on
    the activation distribution the abliterated model actually produces.
• Built with llmcompressor (QuantizationModifier, two config groups + kv_cache_scheme),
  format: mixed-precision. Split: 168 NVFP4 / 233 FP8 / 206 BF16 linears.

vLLM serves this through the compressed-tensors path: the FP4 layers use FP4 tensor cores on
Blackwell, while the FP8 layers run on Hopper-class and newer.

Intended use

• Research into refusal mechanisms, alignment, and interpretability.
• Red-teaming and safety / robustness evaluation in controlled environments.
• Uncensored generation for authorized, lawful research settings.

Out of scope

• Any use that violates the base model's Apache 2.0 license or applicable law.
• Deployment to the public or to end users without additional safety and moderation layers.
• Generating content intended to harm, harass, defraud, or endanger people.

Evaluation

Abliteration is a weight edit shared across all releases of this model, so the refusal
behavior of this checkpoint tracks the BF16 / FP8 builds. On the byte-identical-scheme
FP8 build, harmful-prompt
refusal collapses from 64–99% (base) to 0–6% (thinking off) and ≤ 1.7% (thinking on),
while benign over-refusal drops (XSTest-safe 5.6% → 0.4%) and capability stays within ±1.3 pts
of the base (MMLU 84.3 → 84.7, MMLU-Pro 77.6 → 76.8, GSM8K 90.0 → 88.7, CMMLU 81.4 → 80.8). See
that model card for the full tables.

Quant-specific numbers pending. Capability-retention and perplexity for this
NVFP4 + FP8 mixed-precision checkpoint have not yet been measured — the FP4 layers require
Blackwell FP4 tensor cores to run natively, and this build is released for evaluation on
that hardware. Numbers will be added here once benchmarked. As a mixed 4-bit/8-bit checkpoint it
is expected to trade a little accuracy for size versus the FP8 build; the dynamic split (only the
less-sensitive FFN layers at FP4) is designed to keep that loss small.

Multimodal (vision)

The vision tower is preserved — all 167 visual.* weight tensors are kept in BF16 and the
merger / image + video preprocessor configs are intact, so this stays a full vision-language model
(Qwen3_5ForConditionalGeneration), a drop-in for the base. Abliteration only edits the
language-model residual writers, so image understanding is architecturally unaffected (and
image-conditioned refusals are reduced along with text ones). Serve without
--language-model-only to use vision.

Usage

Self-host with vLLM (OpenAI-compatible)

Requires a recent vLLM (≥ 0.27, with compressed-tensors). The FP4 layers need a Blackwell
GPU (B200 / GB200 / RTX 50-series) for native FP4 tensor cores; the FP8 layers run on Hopper-class
and newer.

docker run -d --name qwen38-uncensored-nvfp4 --gpus all --ipc=host --shm-size=8g \
  -v /path/to/Qwen3.8-27B-Uncensored-NVFP4:/model:ro \
  -p 8000:8000 vllm/vllm-openai:v0.27.1 \
  --model /model --served-model-name Qwen3.8-27B-Uncensored \
  --speculative-config '{"method":"mtp","num_speculative_tokens":2}' \
  --gpu-memory-utilization 0.9 \
  --max-model-len 262144 --max-num-seqs 96 \
  --trust-remote-code \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice --tool-call-parser qwen3_coder

The mixed-precision quantization_config (including the FP8 kv_cache_scheme) is read from
config.json — do not pass --quantization or --kv-cache-dtype. --speculative-config mtp
enables the preserved MTP draft head.

Reasoning (thinking) toggle

Thinking is on by default (Qwen3.8). Toggle it per request via chat_template_kwargs; the
reasoning trace is returned in the reasoning field (--reasoning-parser qwen3).

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

resp = client.chat.completions.create(
    model="Qwen3.8-27B-Uncensored",
    messages=[{"role": "user", "content": "Prove that sqrt(2) is irrational."}],
    extra_body={"chat_template_kwargs": {"enable_thinking": True}},
)
print(resp.choices[0].message.reasoning)  # thinking trace
print(resp.choices[0].message.content)    # final answer

Tool calling

Standard OpenAI tools + assistant tool_calls + role: tool result messages are supported,
including multi-turn (feed the tool result back for a follow-up answer). Parsed by
--tool-call-parser qwen3_coder.

Via OrcaRouter (hosted API — no setup)

Served on OrcaRouter through the OpenAI-compatible
gateway (262K context, tools + reasoning). Grab an API key at
orcarouter.ai (sk-orca-...).

from openai import OpenAI

client = OpenAI(base_url="https://api.orcarouter.ai/v1", api_key="sk-orca-...")
resp = client.chat.completions.create(
    model="qwen/qwen3.8-27b",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(resp.choices[0].message.content)

Hardware requirements & performance

Software

• vLLM ≥ 0.27 with compressed-tensors (Qwen3.5/3.8 support) — e.g. vllm/vllm-openai:v0.27.1.

Compute

• The NVFP4 (FP4) layers require a Blackwell GPU (B200 / GB200 / RTX 50-series) for native FP4
  tensor cores. The FP8 layers run on Hopper (H100 / H200) and newer.

Memory

• Weights: ~23 GB (mixed 4-bit / 8-bit), vs the ~56 GB BF16 checkpoint.
• Minimum ~32 GB VRAM for weights + a small KV cache; the full 262K context needs substantial
  extra KV cache (the checkpoint already stores the KV cache in FP8).
• Recommended: a single Blackwell B200 (or larger) for the full FP4 path.

Throughput / concurrency

• Continuous batching; concurrency bounded by --max-num-seqs and the KV cache that fits after
  weights are loaded. The MTP draft head gives a large decode speedup on real workloads.

Bias, risks, and limitations

• Safety guardrails removed — the model will produce harmful, biased, or offensive content
  on request. See the disclaimer above.
• It inherits any biases and limitations of the base Qwen3.8-27B.
• Mixed NVFP4 + FP8 is not lossless versus BF16; quant-specific capability impact for this
  build is not yet measured (see Evaluation).
• The FP4 layers require Blackwell hardware to run natively; on pre-Blackwell GPUs the FP4 path is
  unavailable.

License

Apache 2.0, inherited from the base model
Qwen/Qwen3.8-27B. Abliteration and quantization do
not change the underlying license obligations.

Changelog

2026-08-21 — Fixed vLLM loading error (lm_head.weight_scale)

Earlier revisions failed to load in vLLM with:

ValueError: There is no module or parameter named 'lm_head.weight_scale' in Qwen3_5ForCausalLM.
The available parameters belonging to lm_head (ParallelLMHead) are: {'lm_head.weight'}

Cause: the output head (lm_head) had been quantized to FP8, so the checkpoint shipped a
lm_head.weight_scale tensor. vLLM's Qwen3_5ForCausalLM always builds lm_head as an
unquantized ParallelLMHead (only a weight parameter), leaving the extra scale with no
destination and aborting the load.

Fix: lm_head is now kept unquantized — restored to the original BF16 weight, lm_head.weight_scale
removed, and lm_head moved to the quantization ignore list (matching the INT8 build). Only
config.json, model.safetensors.index.json, and model-00005-of-00005.safetensors changed; all
other tensors (FP4 body, FP8 layers, FP8 KV scales, MTP head) are byte-for-byte identical.