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README.md

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+---
+license: apache-2.0
+base_model: ornith-ai/Ornith-1.5-35B-A3B
+tags:
+  - gguf
+  - quantized
+  - apex
+  - apex-mtp
+  - moe
+  - mixture-of-experts
+  - qwen3
+  - vlm
+  - vision
+  - speculative-decoding
+  - self-speculative
+  - mtp
+---
+
+<!-- apex-banner-v2 -->
+<div style="background-color: #f59e0b; color: white; padding: 20px; border-radius: 10px; text-align: center; margin: 20px 0;">
+<h2 style="color: white; margin: 0 0 10px 0;">⚡ Each donation = another big MoE quantized</h2>
+<p style="font-size: 18px; margin: 0 0 15px 0;">I host <b>30+ free APEX MoE quantizations</b> as independent research. My only local hardware is an <b>NVIDIA DGX Spark</b> (122 GB unified memory), enough for ~30-50B-class MoEs, but <b>bigger ones (200B+) require rented compute</b> on H100/H200/Blackwell, typically $20-100 per quant.<br>If APEX quants are useful to you, your support directly funds those bigger runs.</p>
+<p style="font-size: 20px; margin: 0;">
+<a href="https://www.patreon.com/cw/mudler" style="color: white; text-decoration: underline;">🎉 Patreon (Monthly)</a> &nbsp;|&nbsp;
+<a href="https://www.buymeacoffee.com/mudler" style="color: white; text-decoration: underline;">☕ Buy Me a Coffee</a> &nbsp;|&nbsp;
+<a href="https://github.com/sponsors/mudler" style="color: white; text-decoration: underline;">⭐ GitHub Sponsors</a>
+</p>
+</div>
+
+# Ornith-1.5-35B-A3B APEX MTP GGUF
+
+APEX quantizations of [ornith-ai/Ornith-1.5-35B-A3B](https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B).
+
+**Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team** | [APEX Project](https://github.com/localai-org/apex-quant)
+
+These files bundle the model's **MTP / NextN draft head** as `blk.40`, so speculative decoding runs against the file itself with `--spec-type draft-mtp`. For the same quants without the head, see [Ornith-1.5-35B-A3B-APEX-GGUF](https://huggingface.co/mudler/Ornith-1.5-35B-A3B-APEX-GGUF).
+
+## Files
+
+| File | Size | For |
+|---|---|---|
+| Ornith-1.5-35B-A3B-APEX-MTP-Quality.gguf | 23.72 GB | highest quality |
+| Ornith-1.5-35B-A3B-APEX-MTP-Balanced.gguf | 26.17 GB | general purpose |
+| Ornith-1.5-35B-A3B-APEX-MTP-Compact.gguf | 17.44 GB | consumer GPUs |
+| Ornith-1.5-35B-A3B-APEX-MTP-I-Mini.gguf | 14.37 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.
+
+The MTP head is a full MoE block in its own right, about 2.4% of the weights. On Quality, Balanced and Compact it is pinned to Q8_0, since a drafter that mispredicts the target wastes the speculation it was added for. I-Mini keeps it at tier precision to stay small.
+
+## Usage
+
+```bash
+# text
+llama-cli -m Ornith-1.5-35B-A3B-APEX-MTP-Balanced.gguf -p "Your prompt" -ngl 99
+
+# vision
+llama-mtmd-cli -m Ornith-1.5-35B-A3B-APEX-MTP-Balanced.gguf --mmproj mmproj.gguf -ngl 99
+
+# speculative decoding against the bundled MTP head
+llama-cli -m Ornith-1.5-35B-A3B-APEX-MTP-Balanced.gguf --spec-type draft-mtp -ngl 99
+```
+
+Needs a recent llama.cpp with `qwen3_5_moe` support.
+
+## Notes
+
+Sizes and quantization recipes are published in the [APEX repository](https://github.com/localai-org/apex-quant). No throughput benchmarks were run on these files.