---

license: mit

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

pipeline_tag: image-text-to-text

tags:

- gguf

- magicquant

- hybrid-quantization

- moe

- multimodal

---

Ornith-1.5-35B-A3B — MagicQuant hybrid GGUFs

Per-group hybrid quantizations of

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

(qwen3_5_moe, ~35B total / A3B active, vision-capable), produced by

MagicQuant's measured evolutionary search: tensors are grouped by role,

candidate per-group scheme assignments are rendered and perplexity-measured

rather than predicted, and a winner is selected per size band. imatrix-

calibrated (510 tensors), KL-blended (weight 0.1), 15 measured candidates over

3 rounds.

Pins: MagicQuant ed17991, Foundry 2f99202.

Files

Measured on one instrument: CPU llama.cpp perplexity, ctx 512, 100 chunks,

wikitext-2 wiki.test.raw. BF16 baseline: 66.19 GiB, PPL 7.9691.

| file | size | ratio vs BF16 | PPL | vs baseline |

|---|---|---|---|---|

| Ornith-1.5-35B-A3B-Q4_K_M.gguf | 19.58 GiB | 0.30× | 7.9530 | −0.20% |

| Ornith-1.5-35B-A3B-Q5_K_M.gguf | 22.88 GiB | 0.35× | 7.9951 | +0.33% |

| Ornith-1.5-35B-A3B-Q6_K.gguf | 28.84 GiB | 0.44× | 7.9792 | +0.13% |

| mmproj-Ornith-1.5-35B-A3B-f16.gguf | 0.86 GiB | — | — | vision projector |

Each tier mixes schemes per tensor group rather than applying one quant type

uniformly — the filenames name the size band, not a uniform type.

How to read these numbers honestly

Q4 measuring below the BF16 baseline does not mean it is better than BF16.

At this instrument's resolution (ctx 512 / 100 chunks) a ±0.3% spread is

noise, and the Q4 < Q6 < Q5 ordering among the three is not physically

meaningful. The defensible claim is narrower and more useful:

> Q4, Q5 and Q6 are indistinguishable from BF16 and from each other at

> this resolution. Separating them would need a sharper instrument

> (paired KL divergence, roughly 100× the resolution), which was not run.

The real signal is the cliff below these tiers. The same search also

measured winners it did not render: Q3 (15.58 GiB) costs +2.96%, and Q2

(10.33 GiB) collapses at +30.2%. That is where quality actually starts

being spent.

So: take Q4_K_M. It is 0.30× the BF16 size at a cost this instrument

cannot separate from zero. The larger tiers are published for anyone who wants

headroom, not because they are measurably better here.

Notes

• Vision requires the mmproj file alongside the model.
• Chat template embedded and verified present in every file before upload.
• Vocab metadata (tokenizer.ggml.token_type) is INT32 per spec — these files

load on current mainline llama.cpp builds.

• MoE with a shared-expert split; expect the usual llama.cpp requirements for

qwen3_5_moe. A build from mid-2026 or newer is recommended.

• Built and measured on an AMD Strix Halo (Ryzen AI MAX+ 395, gfx1151) host.