---

license: apache-2.0

base_model:

- ornith-ai/Ornith-1.5-35B-A3B

- junafinity/Ornith-1.5-35B-A3B-uncensored-GGUF-8bit

pipeline_tag: image-text-to-text

library_name: gguf

tags:

- ornith

- qwen3_5

- moe

- gguf

- imatrix

- mtp

- speculative-decoding

- abliterated

- uncensored

- zerofuse

- multimodal

- vision

- llama.cpp

---

Ornith 1.5 35B-A3B ZeroFuse AD-IQ3_S/IQ3_XXS MTP GGUF

An importance-matrix quant of

junafinity/Ornith-1.5-35B-A3B-uncensored-GGUF-8bit,

an abliterated build of

ornith-ai/Ornith-1.5-35B-A3B.

This repository is the embedded-MTP edition. The compact imatrix language

model is unchanged; the source model's single Q8_0 MTP block is appended as

blk.40.* and explicitly tested on CUDA.

Files

| File | Purpose | Size |

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

| Ornith-1.5-35B-A3B-ZeroFuse-AD-IQ3_S-IQ3_XXS-MTP-Q8.gguf | Language model with embedded Q8_0 MTP | 16.41 GB / 15.27 GiB |

| mmproj-Ornith-1.5-35B-A3B-uncensored-f16.gguf | F16 vision projector | 899 MB |

| imatrix/imatrix.gguf | Importance matrix used for the compact base quant | 192 MB |

| logs/quantize-AD-IQ3_S-IQ3_XXS.log | Quantization log | 123 KB |

| tools/merge_mtp_gguf.py | Minimal reproducible MTP merge tool | 3 KB |

| benchmarks/100k-recall.json | Exact 100K recall result | 544 B |

| SHA256SUMS | Artifact checksums | — |

Download the model and mmproj for image input. The model alone supports text.

Verified structure

| Property | Value |

|---|---:|

| Parameters | 35.51B |

| Decoder layers | 40 |

| MTP layers | 1 |

| GGUF tensors | 753 |

| F32 tensors | 310 |

| Q8_0 tensors | 323 |

| IQ3_XXS tensors | 80 |

| IQ3_S tensors | 40 |

The MTP block contains 20 blk.40.* tensors copied byte-for-byte from the

Junafinity Q8_0 source. Metadata is updated to block_count=41 and

nextn_predict_layers=1.

Tested llama.cpp launch

Tested with the Muse llama.cpp branch. Builds without embedded draft-mtp

support may load the model normally but will not provide speculative decoding.

llama-server \
  --model Ornith-1.5-35B-A3B-ZeroFuse-AD-IQ3_S-IQ3_XXS-MTP-Q8.gguf \
  --mmproj mmproj-Ornith-1.5-35B-A3B-uncensored-f16.gguf \
  --device CUDA0 --split-mode none \
  --n-gpu-layers all \
  --n-cpu-moe 16 \
  --override-tensor 'blk\.40\..*=CUDA0' \
  --ctx-size 102400 --parallel 1 \
  --batch-size 256 --ubatch-size 64 \
  --threads 8 --threads-batch 8 \
  --flash-attn on \
  --cache-type-k q4_0 --cache-type-v q4_0 \
  --spec-type draft-mtp \
  --spec-draft-n-max 2 \
  --spec-draft-device CUDA0 \
  --spec-draft-ngl all \
  --spec-draft-type-k q4_0 \
  --spec-draft-type-v q4_0 \
  --jinja --reasoning auto --reasoning-format deepseek \
  --ctx-checkpoints 1 --checkpoint-min-step 8192 \
  --cache-ram 0 \
  --host 127.0.0.1 --port 8082

--n-cpu-moe 16 is the tested 16 GB VRAM fit profile, not a universal

recommendation. The MTP layer, target KV, draft KV, and MTP compute buffers were

verified on CUDA0.

Local verification

Hardware: RTX 4090 Laptop GPU, 16 GB VRAM; i9-14900HX; Linux.

| Gate | Result |

|---|---:|

| 8K bounded generation | pass |

| 8K generation | 31.79 tok/s |

| 8K MTP acceptance | 51.1% |

| 100K exact recall | pass |

| 100K prompt processing | 149.74 tok/s |

| 100K generation | 32.57 tok/s |

| 100K MTP acceptance | 60.7% |

| Maximum observed VRAM at 100K | 11,945 MiB |

Exact 100K expected and returned value:

ORCHID-739184, COBALT-482017, SABLE-301955

These are single-machine deployment measurements, not general model-quality

benchmarks. Prompt processing with embedded MTP was substantially slower than

the best non-MTP deployment on this machine.

Reproducibility

The included merge script:

1. Reads the existing compact imatrix GGUF without requantizing it.
2. Selects only the 20 blk.40.* tensors from the Junafinity Q8_0 source.
3. Appends those tensors and updates the two MTP metadata fields.
4. Reopens the result and verifies tensor count, metadata, and an MTP tensor hash.

The included importance matrix contains 510 entries computed from 9,686

calibration chunks. The quantization log records the original tensor-level

recipe and tool revision.

Credits and provenance

1. Base model: Ornith AI,

Ornith-1.5-35B-A3B.

1. ZeroFuse abliteration and source GGUF:

junafinity, using

ZeroFuse. The source repository

supplies the Q8_0 MTP tensors and vision projector.

1. Inference and quantization:

llama.cpp and the tested Muse branch.

1. This compact quant and MTP packaging:

l4zy0n3.

No claim is made that quantization or MTP packaging improves source-model

intelligence. They trade precision and implementation complexity for local

deployment fit and speculative-decoding capability.

License and responsible use

The immediate source repository declares Apache-2.0. The original Ornith model

declares MIT. Follow all upstream license notices and terms. This model has had

safety guardrails reduced or removed. Users remain responsible for legal, safe,

and appropriate deployment.