license: apache-2.0
base_model: ressl/Qwen3.8-27B-uncensored
base_model_relation: quantized
library_name: vllm
pipeline_tag: image-text-to-text
language: [en]
tags: [nvfp4, modelopt, sglang, blackwell, uncensored, abliterated, qwen3_5, hybrid]
Qwen3.8-27B uncensored (NVFP4)
TL;DR: abliterated Qwen3.8-27B in 30.1 GB NVFP4 (ModelOpt), served on a single
96 GB Blackwell card at 44 tok/s. Hard refusals on the 1120-prompt
cross-eval: 1/1120 (BF16 master 0/1120, base model
804/1120). Built for security research, red-teaming and
penetration-testing workflows; it will comply with requests a stock model
refuses. Use responsibly.
Facts & figures
| Base model | ressl/Qwen3.8-27B-uncensored (BF16 abliterated master) |
| Method | NVIDIA ModelOpt NVFP4, MLP-projection-only recipe: mlp.gate_proj, mlp.up_proj, mlp.down_proj in NVFP4; gated attention, Gated DeltaNet paths, embeddings, LM head, vision tower and MTP head stay BF16 |
| Size | 30.1 GB (from 55.6 GB BF16) |
| Calibration | mlabonne/harmless_alpaca, 128 samples, sequence length 512 |
| Context length | 262,144 tokens native |
| Hardware | 1x NVIDIA RTX PRO 6000 Blackwell 96 GB (SM120) |
| Measured throughput | 44 tok/s decode (SGLang, TP1, modelopt_fp4) |
| Toolchain | NVIDIA ModelOpt 0.46.0, SGLang 0.5.19, transformers 5.15.0 |
Refusal behavior (measured, hard markers only)
Effective refusal rate on the same 1120 harmful prompts used for the BF16
master, served at temperature 0 with thinking disabled, counting explicit
decline phrases in the first 25 words.
| Eval set | Prompts | Base model | BF16 master | This export |
|---|---|---|---|---|
| JailbreakBench | 100 | 71 | 0 | 0 |
| tulu-harmbench | 320 | 232 | 0 | 0 |
| HarmfulQA | 300 | 207 | 0 | 0 |
| LLM-LAT | 300 | 225 | 0 | 1 |
| mlabonne harmful | 100 | 69 | 0 | 0 |
| TOTAL | 1120 | 804 | 0 | 1 |
Run it with SGLang (validated)
python -m sglang.launch_server \
--model-path ressl/Qwen3.8-27B-uncensored-NVFP4 \
--served-model-name Qwen3.8-27B-uncensored \
--quantization modelopt_fp4 --tp 1 \
--context-length 262144 --mem-fraction-static 0.85 \
--trust-remote-code
Quality & limitations
- Quantization-aware caveat: residual refusals above the BF16 numbers are
quantization-induced; the BF16 master answers the same prompts. - MTP speculative decoding is not part of this export (ModelOpt drops the
mtp.*tensors); the vision tower is preserved. - The model is a reasoning model by default; pass
enable_thinking=Falsefor
short direct answers.
Provenance & reproducibility
BF16 master: abliterated Qwen/Qwen3.8-27B
with norm-preserving biprojection over 128 residual-writing matrices
(pipeline in the master repository). Export: qwen38_export_nvfp4.py, quant
audit nvfp4_report.json. Validation: the served artifact was evaluated
end-to-end with the house cross-eval before upload.
License & credits
Apache-2.0 (inherited from Qwen3.8-27B).
Abliteration, quantization and validation by Robert Ressl
(Hugging Face -
Website -
LinkedIn).