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lfitoto/Qwen3.8-27B-Uncensored-NVFP4

sourceHugging Faceapache-2.0updated 17d agoView on Hugging Face

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Model Card

<h1>Qwen3.8-27B-Uncensored-NVFP4</h1>

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

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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](https://www.orcarouter.ai/models/qwen/qwen3.8-27b) — OpenAI-compatible, no setup, 262K context with tools + reasoning. Endpoint: [`api.orcarouter.ai/v1`](https://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](https://www.apache.org/licenses/LICENSE-2.0) 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


Base model	`Qwen/Qwen3.8-27B`
Architecture	`Qwen3_5ForConditionalGeneration` — 64 layers, hidden 5120, hybrid Gated DeltaNet (48 linear-attention + 16 full-attention, interval 4), native VL tower + MTP head
Modification	Abliteration (refusal-direction removal) then dynamic mixed-precision NVFP4 + FP8 quantization
Quantization	Mixed-precision (compressed-tensors) — NVFP4 (W4A4) on the FFN of layers 0–55, FP8 (W8A8 dynamic) on attention / GDN projections / the last 8 layers' FFN / `lm_head`, and a static FP8 KV cache
Format	safetensors, resharded to ≤ 5 GB shards (5 + 1 shards, 23.4 GB, 1968 tensors)
Precision	FP4 E2M1 group-16 (+ FP8-E4M3 block scale + FP32 global scale) and FP8 E4M3; vision tower / norms / GDN `in_proj_a/b` / embeddings / MTP head kept in BF16
Preserved	Full vision-language tower and MTP speculative-decoding head (drop-in for the base)
Context	262,144 tokens

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:

Component	matrices edited
`self_attn.o_proj` (16 full-attention layers + MTP)	17
`linear_attn.out_proj` (48 linear-attention / GDN layers)	48
`mlp.down_proj` (64 layers + MTP)	65
`embed_tokens` (row space)	1
Total	131

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:

Component	Precision	# linears
MLP `gate/up/down`, layers 0–55	NVFP4 — FP4 E2M1, group 16, FP8-E4M3 block scale + FP32 global scale (W4A4)	168
`self_attn.{q,k,v,o}_proj`, GDN `in_proj_qkv/in_proj_z/out_proj`	FP8 — W8A8, per-channel weight, dynamic per-token activation	208
MLP `gate/up/down` of the last 8 layers (56–63), `lm_head`	FP8 (W8A8 dynamic)	25
KV cache	FP8 — static, per-tensor	—
Vision tower, GDN `in_proj_a/in_proj_b`, all norms, embeddings, MTP head	BF16 (unquantized)	206

—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.

bash

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).

python

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](https://www.orcarouter.ai/models) through the OpenAI-compatible gateway (262K context, tools + reasoning). Grab an API key at orcarouter.ai (sk-orca-...).

python

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.