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wang-yang/Ornith-1.0-35B-MTPLX
Ornith-1.0-35B-MTPLX is a text generation model from wang-yang. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as mit.
A multi-token-prediction (MTP) graft onto deepreinforce-ai/Ornith-1.0-35B, packaged for MTPLX native speculative decoding on Apple Silicon.
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From the Hugging Face model README
A multi-token-prediction (MTP) graft onto deepreinforce-ai/Ornith-1.0-35B, packaged for MTPLX native speculative decoding on Apple Silicon.
Ornith-1.0-35B (qwen35moe, 35B-A3B, Qwen3.5 base) is a strong agentic-coding MoE but ships without MTP heads. This build grafts the official 1-layer MoE-MTP from Qwen/Qwen3.5-35B-A3B (the upstream base of Ornith — dimensions match exactly) and quantizes the MTP to 4-bit, which is what makes the speedup practical (see below).
| Mode | tok/s | Speedup | MTP acceptance |
|---|---|---|---|
| AR (no MTP) | 76.3 | 1.00× | — |
| MTP depth 1 | 103.6 | 1.36× | 89.6% |
| MTP depth 2 | 114.9 | 1.50× | 93.1% / 78.2% |
| MTP depth 3 | 116.4 | 1.53× | 91.5% / 80.3% / 65.6% |
verdict: mtp_depth_wins · MTPLX inspect tier: verified
A bf16 graft of this same MTP layer was a net ~20× slowdown despite 92% acceptance — the MTP is a full 256-expert MoE layer, and at bf16 its draft forward costs ~67–143 ms/token (it doesn't hit the fast MoE kernel). Quantizing the MTP experts to 4-bit affine drops the draft cost to ~2.5 ms/token (~27–51×) with negligible acceptance loss, flipping the result to a real 1.53× speedup. The bottleneck was draft cost, not acceptance.
mtplx_mtp_quantization in config.json)Qwen3_5MoeForConditionalGeneration / MTPLX arch_id qwen3-next-mtpmtplx tune --model <path-to-this-model> # confirms best MTP depth (D3, ~1.53×)
mtplx start --model <path-to-this-model> # serve with MTP speculative decoding
This derivative is released under MIT, preserving the base model's license. The grafted MTP tensors originate from the Apache-2.0 licensed Qwen3.5-35B-A3B; that license and its NOTICE apply to those tensors. No weights were retrained — this is a graft + quantization repackaging.
mtp.* tensors lifted from Qwen3.5-35B-A3B (785 tensors: mtp.fc + mtp.layers.0.* with 256 experts).mtplx_mtp_norm_encoding="delta".forge (mtp_policy=requantize, --allow-degraded-mtp); contract calibrated exact_agreement.