library_name: gguf
license: other
pipeline_tag: text-generation
tags:
- qwen3.5
- uncensored
- hauhauCS
- nvfp4
- mtp
- vision
- llama.cpp
- blackwell
- ollama
- Image-to-Text
base_model: HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive
Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-MTP-GGUF-NVFP4
Uncensored Qwen3.5 9B (HauhauCS Aggressive) · NVFP4 quantized · MTP head grafted · vision-capable
A hand-built GGUF of HauhauCS's uncensored Qwen3.5-9B fine-tune, quantized with a custom NVFP4 recipe for Blackwell GPUs, with the Multi-Token-Prediction (MTP) head grafted in from the upstream base model, plus the original vision projector (mmproj) for image input.
Credits
This model is built on the work of two teams:
- The Qwen team (QwenLM) — built the base Qwen3.5-9B model, including its MTP head.
- The HauhauCS team (HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive) — tuned the base model into this uncensored "Aggressive" variant (0/465 refusals, fully unlocked with no capability loss) and provided the vision projector.
This repository (AIconjured) is the NVFP4 quantization, MTP graft, and packaging of that model.
Files
| File | Size | Description |
|---|---|---|
Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-MTP-GGUF-NVFP4.gguf |
5.9 GB | LLM: 442 tensors (427 main + 15 MTP), block_count=33, nextn_predict_layers=1 |
mmproj-Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-BF16.gguf |
0.9 GB | Vision projector, clip arch, 334 tensors, BF16 (unchanged from upstream) |
Size: ~5.9 GB (LLM) + ~0.9 GB (mmproj) — down from 18.8 GB in BF16.
Model facts
- Architecture:
qwen35— 32 trunk layers (hybrid DeltaNet SSM + full attention, interval 4), 9.2B params, 262K native context, 4096-dim embeddings, 16 heads / 4 KV heads - Base model: HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive (BF16, 17.9 GB)
- MTP source: unsloth
Qwen3.5-9B-MTP-GGUF(Q8_0 carrier)
Quantization recipe
The quantization is a mixed-precision recipe, not a single uniform type. It was built with llama.cpp's --tensor-type-file (regex → type) plus an imatrix generated from ~144 KB of mixed calibration text (PPL ≈ 5.18):
| Type | Tensors | What | Why |
|---|---|---|---|
| NVFP4 | 192 | ffn_gate/up/down, attn_qkv, attn_gate, ssm_out, full-attn attn_q/k/v/output |
All the big GEMMs — where the bits are. NVFP4 (FP4 weights + FP8/UE4M3 scales) is the native Blackwell path (BLACKWELL_NATIVE_FP4), fastest on RTX 50-series. |
| Q8_0 | 66 | token_embd, output (LM head), attn_k, ssm_alpha, ssm_beta, ssm_conv1d |
Small but accuracy-sensitive tensors; Q8_0 is near-lossless. |
| F32 | 184 | All norm weights, ssm_a, ssm_dt |
Norms/decay constants are tiny but quantizing them causes noticeable quality loss; kept at full precision. |
Recipe (llama.cpp --tensor-type-file format):
token_embd\.weight=q8_0
^output\.weight=q8_0
blk\.\d+\.attn_k\.weight=q8_0
blk\.\d+\.ssm_beta\.weight=q8_0
blk\.\d+\.ssm_alpha\.weight=q8_0
blk\.\d+\.ssm_conv1d\.weight=q8_0
blk\.\d+\.attn_output\.weight=nvfp4
blk\.\d+\.ffn_gate\.weight=nvfp4
blk\.\d+\.ffn_up\.weight=nvfp4
blk\.\d+\.ffn_down\.weight=nvfp4
blk\.\d+\.attn_qkv\.weight=nvfp4
blk\.\d+\.attn_gate\.weight=nvfp4
blk\.\d+\.ssm_out\.weight=nvfp4
blk\.\d+\.attn_q\.weight=nvfp4
blk\.\d+\.attn_v\.weight=nvfp4
MTP (Multi-Token Prediction)
The HauhauCS fine-tune ships without MTP tensors. The MTP head (15 tensors, blk.32.nextn.*) was grafted from unsloth's Q8_0 base-model carrier, and the metadata was set to block_count=33 + nextn_predict_layers=1 so the loader sees 32 trunk layers + 1 MTP layer at index 32.
Important: the MTP head comes from the base model, not the fine-tune. Speculative decoding is self-verifying (the main model accepts/rejects every draft token), so quality is guaranteed by the uncensored fine-tune — but the accept rate will be lower than a fine-tune-native MTP head, since the base head doesn't perfectly match the fine-tuned distribution.
To actually get the speedup you need an engine that implements MTP spec decode:
llama-server -m Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-MTP-GGUF-NVFP4.gguf \
--mmproj mmproj-Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-BF16.gguf \
--spec-type draft-mtp -c 8192 -ngl 99
Ollama note: Ollama (tested 0.33.3) loads the MTP tensors but does not implement MTP speculative decoding — the tensors are ignored and the model runs as a plain 32-layer model. Text and vision both work fine; you just don't get the draft speedup.
Vision
Vision is a separate clip-arch mmproj GGUF (334 tensors, BF16, unmodified from upstream). The LLM file intentionally contains no vision tensors — the qwen35 arch loader rejects them, and this is the structure Ollama expects. Load both files:
FROM ./Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-MTP-GGUF-NVFP4.gguf
FROM ./mmproj-Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-BF16.gguf
The projector uses the qwen3vl_merger with a vision encoder; verified working end-to-end in Ollama (text + image).
Hardware / performance notes
- Built and verified on 2× RTX 5060 Ti (16 GB each, Blackwell, sm_120a) with a CUDA 12.8 llama.cpp build.
- All 32 layers + output offload to GPU; the model fits entirely in VRAM on a single 16 GB card at moderate context.
- NVFP4 kernels use the native Blackwell FP4 path; on non-Blackwell GPUs the tensors still load and run (emulated/dequantized), with less of a speed advantage.
- 262K native context is declared; practical usable context is limited by VRAM.
Reproduction
- llama.cpp:
378aa2ebc(2026-08-27), CUDA build - Quantize:
llama-quantize --imatrix imatrix-9b.gguf --tensor-type-file recipe.txt ... - Imatrix: 56 chunks × 2048 tokens, mixed wiki/prose calibration, final PPL 5.1784 ± 0.096
- MTP graft: tensor copy of the 15
blk.32.nextn.*tensors +qwen35.nextn_predict_layers=1,qwen35.block_count=33
License
This quantization is a derivative of HauhauCS's uncensored fine-tune of the Qwen3.5-9B base model. See the base model's license at HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive and the Qwen base model's license at QwenLM.