license: apache-2.0 base_model: JonathanColetti/Qwen3.8-27B-Uncensored base_model_relation: quantized language:

• en
• zh pipeline_tag: text-generation tags:
• gptq
• int4
• uncensored
• abliterated
• mtp
• speculative-decoding
• intel
• arc
• intel-arc
• xpu
• vllm
• qwen3.8

Qwen3.8-27B-Uncensored-GPTQ-Int4-sym-G128-MTP-BF16

GPTQ-Int4 quantization of JonathanColetti/Qwen3.8-27B-Uncensored, built for Intel Arc / vLLM XPU with the MTP head preserved in BF16 so native speculative decoding still works.

At the time of upload this is the only GPTQ build of an uncensored Qwen3.8-27B — existing variants are GGUF (llama.cpp), AWQ, or unquantized safetensors. GPTQ-Int4 is the format Intel's integer-first XMX hardware actually wants.

Why this exists

Quantizing this model naively breaks speculative decoding. The 15 mtp.* tensors are the draft head; if they get quantized along with everything else, draft acceptance collapses and you lose roughly half your decode speed. The fix is one line of quantize config:

dynamic={"-:.*mtp.*": {}}   # exclude mtp.* from quantization -> stays BF16

The result has 400 quantized weight tensors (I32) + 15 preserved BF16 MTP tensors.

Provenance

Qwen/Qwen3.8-27B                        (Apache-2.0, base)
  └─ JonathanColetti/Qwen3.8-27B-Uncensored   (abliterated, BF16, 55.6 GB)
       └─ this repo                            (GPTQ-Int4, 19 GB)

Quantization

Measured performance

Intel Arc Pro B70, 230 W, vLLM 0.27.2rc1.dev77+gac7509e2b.xpu, vllm-xpu-kernels 0.1.12.3, MTP4, fp8 KV, 131,072 context, prefix caching off, client post-first tok/s, median of n=3–5.

Cold input (prompt tokens / TTFT): 1808 @ p2k · 1810 @ p4k · 1777 @ p6k · 1738 @ p8k. Long context verified to 120,532 tokens.

Throughput tracks MTP acceptance, which depends on how predictable the output is — code drafts well, freeform prose less so. Treat 68.5 as the good case, not a floor.

Serving (vLLM XPU)

Requires the two MTP patches from the Intel Arc Pro B70 cookbook (patch_mtp_nightly.py, then patch_mtp_boundary.py), applied to vllm/vllm-openai-xpu@sha256:f01e24f6c7ff01f1e0662234255a1372297d1dbd89d003cf13c8fad3eab1ba4f.

vllm serve /model \
  --quantization gptq --dtype float16 --max-model-len 131072 \
  --gpu-memory-utilization 0.88 --kv-cache-dtype fp8 \
  --max-num-seqs 1 --max-num-batched-tokens 8192 \
  --no-enable-prefix-caching --served-model-name qwen38 --language-model-only \
  --speculative-config '{"method":"mtp","num_speculative_tokens":4}' \
  --enable-auto-tool-choice --tool-call-parser qwen3_xml

-e B70_MTP_BF16_DRAFT=1 is required for the BF16 draft gate.

Three things that will bite you

1. --max-num-seqs 1 is mandatory with any MTP mode. With more, a batch can mix a prefill with a spec-decode step and the GDN kernel aborts the entire engine: causal_conv1d does not support spec-decode and non-spec (prefill + decode) tokens in the same invocation. Reproducible with 2 concurrent requests. For real parallel batching you must drop speculation entirely.
2. Prefix caching does nothing. Qwen3.8 is a hybrid GDN/Mamba architecture and vLLM's supports_mamba_prefix_caching flag is not declared for it, so enabling it yields 0 cache hits while costing KV space and ~4% decode. Measured: 3,036 queries → 0 hits. Not hardware-specific; the same applies on CUDA.
3. --kv-cache-dtype fp8 is required at 128K — fp16 KV does not fit in 32 GB.

Limitations — please read

• No quality evaluations were run. No perplexity comparison against the BF16 source, no coding or reasoning benchmarks, no quantitative refusal-rate testing. What was verified: tensor structure, quantization config, inference speed, MTP acceptance, tool calling, 120K context, and light generation smoke tests. If you need quality guarantees, measure before relying on it.
• Calibration used general web text with no code. If coding quality matters to you, a code-inclusive calibration set would likely be better.
• Abliteration behaviour is inherited, not verified. In informal testing it complies without refusal or moralizing preamble, but stays fairly mild in register — it removes refusals more than it changes tone. All decensoring properties come from the upstream abliteration; this repo only changes numeric precision.
• This is an uncensored model. It will attempt requests an aligned model declines. You are responsible for how you use it.

Credits

• Qwen — base model (Apache-2.0)
• JonathanColetti — abliteration
• Intel Arc Pro B70 inference cookbook — MTP patches and serving recipe
• gptqmodel — quantization