license: apache-2.0 base_model: HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF base_model_relation: quantized library_name: ninfer pipeline_tag: image-text-to-text tags:

• ninfer
• qwen3.8
• groupwise-int
• dflash2
• speculative-decoding
• uncensored
• artifact
• rtx-5090

Qwen3.8-27B Uncensored (HauhauCS Aggressive) + DFlash2 — NInfer artifact

A single-file .ninfer artifact for the NInfer engine (single-GPU C++/CUDA inference) carrying the community uncensored tune HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF as the target model and the official z-lab/Qwen3.8-27B-DFlash2 draft model as the DFlash2 speculative companion (recipe qwen3_8_27b-v2, the first NInfer recipe with the DFlash2 payload). The native NextN/MTP head of the tune is preserved as well, so both --spec mtp and --spec dflash2 work.

Download & run

hf download WaveCut/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-DFlash2-NInfer qwen3_8_27b_hauhaucs_aggressive_dflash2.ninfer --local-dir models

# DFlash2 speculative decoding (7 drafts, optimized head), CLI:
./build/apps/ninfer models/qwen3_8_27b_hauhaucs_aggressive_dflash2.ninfer --prompt "Summarize the difference between prefill and decode." \
  --max-context 16384 --spec dflash2 --draft-tokens 7 --lm-head-draft

# OpenAI/Anthropic-compatible server:
./build/apps/ninfer-serve models/qwen3_8_27b_hauhaucs_aggressive_dflash2.ninfer --max-context 32768 --kv-capacity auto \
  --spec dflash2 --draft-tokens 7 --lm-head-draft

--spec mtp --draft-tokens 3 --lm-head-draft selects the tune's own NextN head instead. Vision (--vision) is the official tower: the release's BF16 projector reproduces it bit-for-bit through the FP16 export path, so the artifact carries the official BF16 vision words.

Hardware. Upstream NInfer targets one RTX 5090-class Blackwell card (sm_120, 32 GB); the smoke run above used an RTX PRO 4500 Blackwell. The RTX 3090 fork Don-Chad/ninfer-3090 loads the same qwen3.8-27b / groupwise-int identity once it carries the upstream DFlash2 runtime.

How it was built

The tune is published only as GGUF quants, so the target weights were rebuilt into the official Qwen/Qwen3.8-27B safetensors layout first, then converted with the unmodified stock converter:

1. GGUF → HF BF16 (gguf_to_hf.py, included). Every llama.cpp qwen35 export transform was inverted exactly: the Gated-DeltaNet V-head grouped→tiled reorder (qkv/z/a/b rows, A_log, dt_bias, conv1d channels, out_proj columns), -exp(A_log), the +1 on zero-centered RMSNorm weights, the conv1d squeeze, and the mtp.* → blk.64 NextN rename; the mmproj inverts the Conv3D temporal split and merger names. Q8_0 blocks are dequantized exactly like ggml (f16 scale × int8, RNE to BF16).
2. Exactness against the official checkpoint. This release was exported from an FP16 copy of the weights, so llama.cpp computed w+1 and -exp(A_log) in FP16 before storing F32 and the BF16/F32 words in the GGUF are lossy relative to the original BF16 checkpoint; where re-applying that forward transform to the official Qwen/Qwen3.8-27B tensor reproduces the GGUF words bit-for-bit, the official BF16 word was used. For Q8_0 matrices the official tensor was re-quantized with ggml's rule and compared byte-for-byte: identical codes ⇒ the tune did not touch that matrix ⇒ exact official BF16 words; any difference ⇒ the dequantized tune values. Result: 49 of 1199 tensors carry the tune (all in text layers 19–53); every other tensor reproduced the GGUF words bit-for-bit from the official checkpoint through the FP16 export path and therefore carries the exact official BF16 words (1150 tensors, 404 of them Q8_0 matrices whose only differences were ±1 rounding flips at quantizer ties with identical block scales). The modified matrices by kind:
   • model.language_model.layers.N.linear_attn.out_proj.weight × 13
   • model.language_model.layers.N.mlp.down_proj.weight × 16
   • model.language_model.layers.N.mlp.gate_proj.weight × 7
   • model.language_model.layers.N.mlp.up_proj.weight × 7
   • model.language_model.layers.N.self_attn.o_proj.weight × 6

3. Vision: the release's BF16 mmproj reproduces the official vision tower bit-for-bit through FP16 (333/333 tensors) — the tune did not touch vision.
4. Frontend resources (tokenizer, chat template, preprocessor configs, generation_config.json) are the official Qwen/Qwen3.8-27B files pinned by SHA-256 in the converter.
5. Converter: python3 -m tools.convert.qwen3_8_27b.convert --model <hf-bf16> --dflash2-model <z-lab DFlash2> --out qwen3_8_27b_hauhaucs_aggressive_dflash2.ninfer (92 s on the RTX PRO 4500). Its own report is qwen3_8_27b_hauhaucs_aggressive_dflash2.ninfer.conversion.json; the GGUF→HF decisions are in gguf_to_hf.report.json.

Caveats

• Double quantization on the modified tensors. Matrices the tune changed exist only as Q8_0 and are re-quantized into NInfer's groupwise profile (Q4/Q5/Q6/W8 groupwise + BF16). Unmodified matrices carry the exact official BF16 words.
• The engine binds this file to the registered qwen3.8-27b / groupwise-int profile: architecture, tokenizer and chat template are the official ones; only the weights carry the tune. The tune's own chat template in the GGUF is not embedded.
• DFlash2 lossless verification: the target model authorizes every emitted token, so the uncensored behaviour of the tune is unchanged by the draft model; DFlash2 only affects speed/acceptance.
• Set the sampler explicitly (temperature=1.0, top_p=0.95, top_k=20) if you rely on the tune's intended behaviour.
• Greedy decoding with --spec dflash2 is lossless in distribution but not bit-parity with plain greedy: the verify pass runs a batched floating-point route, so long greedy outputs can diverge by a token (in the smoke run: one word of the haiku after ~80 identical tokens).

Lineage

Qwen/Qwen3.8-27B (1d4bf0f2)
  ├─ HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF  (Q8_K_P + BF16 mmproj, Apache-2.0)
  │    └─ exact GGUF→HF inversion + official-consistency reconstruction (gguf_to_hf.py)
  └─ z-lab/Qwen3.8-27B-DFlash2 (50307d4c, MIT)  ── DFlash2 companion
       └─ qwen3_8_27b_hauhaucs_aggressive_dflash2.ninfer  (this repo, NInfer groupwise-int profile, recipe v2)

Licensed Apache-2.0 for the target weights (both upstreams), MIT for the DFlash2 draft weights.