---

language:

• zh
• en

license: apache-2.0

library_name: gguf

pipeline_tag: image-text-to-text

base_model:

• Qwen/Qwen3.8-27B
• junafinity/Qwen-3.8-27B-Uncensored

tags:

• qwen3.8
• gguf
• zerorefusal
• uncensored
• unsloth-dynamic
• iq4-xs
• mtp
• multimodal
• llama.cpp
• 16gb-vram

---

Qwen3.8-27B ZeroRefusal UD-IQ4_XS V3 Final (Native MTP)

English | 简体中文

This is a Qwen3.8-27B GGUF built and validated for an RTX 5060 Ti 16 GB deployment.

The model was requantized from ZeroFuse-edited BF16 weights using the tensor-type layout and importance matrix of the final Unsloth Dynamic V3 UD-IQ4_XS release. The resulting single GGUF retains the native MTP / NextN layer and does not require a separate draft model.

This is an independent community release, not an official release from Qwen, Unsloth, llama.cpp, or ZeroFuse.

Release files

| File | Purpose | Size |

|---|---|---:|

| Qwen3.8-27B-ZeroRefusal-UD-IQ4_XS-V3-Final-MTP.gguf | Main text model with native MTP | 14,252,845,056 bytes |

| mmproj-Qwen3.8-27B-F16.gguf | Qwen3.8 Vision F16 projector | 927,607,008 bytes |

| quantization/imatrix_unsloth.gguf | Importance matrix used for quantization | 13,642,656 bytes |

| quantization/v3-final-tensor-overrides.txt | Anchored type map for all 866 tensors | 17,007 bytes |

Main model SHA256:

f6c1f6e1211aeceaa386cbeabd0bdfbee0d124747ecaa3e908a93394518e9274

See SHA256SUMS for all published binary hashes.

Model structure

• Architecture: qwen35
• Tensor count: 866
• Block count: 65
• qwen35.nextn_predict_layers = 1
• Native MTP: 15 tensors under blk.64.*
• general.file_type = 30
• llama.cpp reports: IQ4_XS - 4.25 bpw
• Original context length: 262,144
• Validated production context: 73,728

The final tensor names, shapes, and quantization-type distribution match the final Unsloth Dynamic V3 UD-IQ4_XS reference. This does not imply byte-identical tensor payloads: the behavior weights were edited and requantized.

Validated production profile

GPU                 RTX 5060 Ti 16 GB
llama.cpp           upstream b10435 / 9e40df6
context             73728 (72 x 1024, shared KV pool)
parallel            2
KV                   unified Q4_0 K / Q4_0 V
text placement      full GPU
MTP                  1
draft KV             F16 K / F16 V
Vision               F16 mmproj, CPU offload
image-max-tokens     4096
API port             8001

With 73728 + P2 + unified KV, both slots share one 72K KV pool. It is not a separate 72K context for each slot.

llama-server example

llama-server \
  -m Qwen3.8-27B-ZeroRefusal-UD-IQ4_XS-V3-Final-MTP.gguf \
  --alias qwen3.8-27b \
  --mmproj mmproj-Qwen3.8-27B-F16.gguf \
  --no-mmproj-offload \
  --image-max-tokens 4096 \
  -c 73728 \
  -np 2 \
  --kv-unified \
  -ngl 999 \
  --flash-attn on \
  -ctk q4_0 \
  -ctv q4_0 \
  --spec-type draft-mtp \
  --spec-draft-n-max 1 \
  --spec-draft-type-k f16 \
  --spec-draft-type-v f16 \
  --no-spec-draft-backend-sampling \
  -b 512 \
  -ub 64 \
  --threads 7 \
  --fit off \
  --jinja \
  --host 0.0.0.0 \
  --port 8001

Measured performance on RTX 5060 Ti 16 GB

| Test | Result |

|---|---:|

| P1 decode | 41.67 tok/s |

| P2 stream A | 31.58 tok/s |

| P2 stream B | 34.08 tok/s |

| P2 average per stream | 32.83 tok/s |

| P2 server aggregate | 65.67 tok/s |

| P2 wall-clock aggregate | 61.88 tok/s |

| Lowest observed free VRAM | about 138 MiB |

MTP draft acceptance on the speed-test samples was 80.85% for P1 and 77.43% / 62.22% for the two P2 streams. Acceptance is content-dependent.

64-prompt refusal validation

Under the fixed validation configuration:

valid       64
accepted    64
refused      0
errors       0
result       0/64 refusal

Method:

• Dataset: mlabonne/harmful_behaviors
• Revision: 01cead01398926d81f7c52bdb790ee8cf77ebba7
• Corpus SHA256: b4f2ddec5ab06058b721be9afe75e7fdc656852da257d8e7a117425b3dd89114
• First 64 prompts, maximum 64 output tokens per prompt
• Temperature 1.0, top-p 0.95, top-k 20
• Fixed 23-marker refusal classifier

Only the methodology and summary are public. Raw harmful outputs are intentionally not uploaded. This result applies only to the fixed test and is not a general safety or quality guarantee.

Provenance

Final Dynamic V3 quantization reference:

repo      unsloth/Qwen3.8-27B-GGUF
file      Qwen3.8-27B-UD-IQ4_XS.gguf
revision  4ca720788d1e01f1bff70c033e0d0028fd02e502
bytes     14252845984
sha256    40fac4050e940397dbf13087afd50f4734a11805bf9d65ef8ddd7483470e6199

Behavior-edit source:

repo          junafinity/Qwen-3.8-27B-Uncensored
revision      903d149c148b81fdf4e568a05ac9ad4225f493d7
method        ZeroFuse v0.1.0 directional ablation
trial         38
source layer  35
strength      1.2242385643286666
edited layers 9-56
BF16 KL       0.009713646
BF16 refusal  0/64

The quantization map uses fully anchored regular expressions. This prevents the global output.weight rule from accidentally matching per-layer attn_output.weight tensors. See quantization/v3-final-tensor-overrides.txt.

Limitations and safety

• The 16 GB configuration has a very narrow VRAM margin. Different drivers, CUDA versions, batch shapes, or background GPU workloads can cause OOM.
• Do not run ComfyUI or another heavy CUDA workload at the same time.
• Keep the F16 Vision projector on CPU for this profile.
• ZeroRefusal/uncensored models can produce unsafe, illegal, or incorrect content. Deployers are responsible for access control, auditing, and safety policy.
• Follow LICENSE and all upstream model, dataset, and tool licenses and terms.