---

license: apache-2.0

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

tags:

- gguf

- quantized

- apex

- moe

- mixture-of-experts

- qwen3

- vlm

- vision

---

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Ornith-1.5-35B-A3B APEX GGUF

APEX quantizations of ornith-ai/Ornith-1.5-35B-A3B.

Brought to you by the LocalAI team | APEX Project

These are the standard quants. For versions that bundle the MTP draft head for speculative decoding, see Ornith-1.5-35B-A3B-APEX-MTP-GGUF.

Files

| File | Size | For |

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

| Ornith-1.5-35B-A3B-APEX-Quality.gguf | 22.82 GB | highest quality |

| Ornith-1.5-35B-A3B-APEX-Balanced.gguf | 25.27 GB | general purpose |

| Ornith-1.5-35B-A3B-APEX-Compact.gguf | 16.54 GB | consumer GPUs |

| Ornith-1.5-35B-A3B-APEX-I-Mini.gguf | 13.47 GB | smallest, imatrix only |

| mmproj.gguf | 0.90 GB | vision projector, pair with any of the above |

I- files use an importance matrix built from diverse calibration data (chat, code, reasoning, tool-calling, agentic traces, Wikipedia). Quality, Balanced and Compact also ship without it.

The model

Ornith-1.5-35B-A3B is a 36 B parameter Mixture-of-Experts model with 256 routed experts and 8 active per token, plus a shared expert. It has 40 layers with hybrid attention, interleaving three linear-attention layers per full-attention layer, and a vision tower.

How APEX quantizes it

Routed experts are 89.6% of the weights here but only 8 of 256 fire for any given token, so they tolerate lower precision than the parts every token passes through. APEX classifies each tensor by role and applies a layer-wise precision gradient: the first and last layers keep higher precision, middle layers compress harder, and the always-active shared expert is kept high.

Attention is only 3.6% of the weights on this model (2.8% linear, 0.8% full), so it is not where the size is and is not treated as a lever.

Usage

# text
llama-cli -m Ornith-1.5-35B-A3B-APEX-Balanced.gguf -p "Your prompt" -ngl 99

# vision
llama-mtmd-cli -m Ornith-1.5-35B-A3B-APEX-Balanced.gguf --mmproj mmproj.gguf -ngl 99

Needs a recent llama.cpp with qwen3_5_moe support.

Notes

Sizes and quantization recipes are published in the APEX repository. No throughput benchmarks were run on these files.