Dragoy/Swift-Qwen3.8-27B-abliterated-NVFP4

Swift-Qwen3.8-27B · huihui-style abliterated · NVFP4 (standard HF format)

NVFP4 + FP8 quantized checkpoint of the abliterated Swift model, published in the standard Hugging Face safetensors format. It loads in vLLM and transformers, so standard features such as JSON-schema structured output (vLLM guided decoding) work out of the box.

Specs

Usage (vLLM)

pip install vllm==0.27.1
export VLLM_USE_FLASHINFER_SAMPLER=0   # no nvcc needed in the container
vllm serve dragoy/Swift-Qwen3.8-27B-abliterated-NVFP4 \
  --max-model-len 32768 --gpu-memory-utilization 0.9

JSON-schema output:

from vllm import SamplingParams
params = SamplingParams(temperature=1.0, top_p=0.95, top_k=20,
                        guided_json={"type": "object",
                                     "properties": {"answer": {"type": "string"}},
                                     "required": ["answer"]})

Provenance

1. 1.Source: ukisai/Swift-Qwen3.8-27b @ 1b30aaaf753fe5c1cb51ada2ea0367a53445359c
2. 2.Abliteration: huihui-style (refusal-direction projection, Arditi et al. 2024 family), transferred by per-tensor weight difference from the Qwen/Qwen3.8-27B ↔ huihui-ai/Huihui-Qwen3.8-27B-abliterated pair — format/weight change, no fine-tuning
3. 3.Quantization: llm-compressor oneshot, NVFP4 + FP8 allocation copied verbatim from unsloth/Qwen3.8-27B-NVFP4 (quantization_config), 32 calibration samples
4. 4.Config repair: nested multimodal config restored + quantization_config grafted (llm-compressor flattens it)
5. 5.Verification: shape preflight + vLLM 0.27.1 smoke on RTX PRO 6000

Also available

The same checkpoint, packaged as a single-file engine artifact for Blackwell (sm_120a) instead of standard safetensors: Dragoy/Swift-Qwen3.8-27B-abliterated-NVFP4-NInfer.

License

This repository is a derivative of the Swift checkpoint, whose license is the Swift Open License v1.0 — not Apache. The chain:

What that means in practice:

• —Free use, including commercial, while your gross revenue (counting all controlled entities) is below the $1,000,000 per fiscal year threshold; qualified non-profits have no threshold for non-commercial or research use.
• —Above the threshold: obtain a separate written licence from UkisAI (Swift Enterprise License).
• —Redistribution: ship both licence files, keep the copyright and attribution notices, and mark files you modified (Swift licence §4–§5).

This is a description of what the licences say, not legal advice.

Intended use and limitations

This is an uncensored model: the upstream abliteration removes the refusal direction, so it will attempt requests a stock instruction-tuned model declines. It is published for research, evaluation and local deployment where that behaviour is understood and wanted.

Use at your own responsibility. Anyone deploying it is responsible for their own safeguards, output handling, and compliance with the licences above and applicable law. The publisher publishes this checkpoint as-is, without warranty of any kind, and is not liable for how it is used or for its outputs. The refusal behaviour — and any capability cost of the abliteration — originates entirely upstream; this repository changes precision and format, not behaviour.

Credit for the base model to Qwen (Alibaba Cloud); for the Swift training to UkisAI; for the abliteration style to huihui-ai; and for the published NVFP4 recipe to unsloth.