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GGUF builds of `deepreinforce-ai/Ornith-1.0-9B` with the KL-distilled MTP draft head from `protoLabsAI/Ornith-1.0-9B-MTP` **baked into the trunk** — llama.cpp does lossless multi-token self-speculative decoding out of the box, no separate draft model to wire up. Every file here carries the `nextn` head, so `--spec-type draft-mtp` just works. "Bundled" = trunk + `nextn` head in one file. The **IQ** rungs are i-quants (importance-matrix calibrated) with the **MTP head pinned to Q8_0** so acceptance holds on the low-bit trunk (measured ~0.81–0.84 on IQ2_M–IQ4_XS, on par with the k-quants). Serve them exactly like the k-quants. The standalone head is **not a model** — loading `mtp-head/…` directly will crash. It exists only to pair with a base Ornith-9B GGUF via `--model-draft`.

Text generation
Input Text→ Output Text
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GGUF builds of `ornith-ai/Ornith-1.5-9B` with a **distilled MTP draft head baked into the trunk** — llama.cpp does lossless multi-token self-speculative decoding out of the box, no separate draft model to wire up. Every file here carries the `nextn` head, so `--spec-type draft-mtp` just works. Ornith ships 1.5-9B with `mtp_num_hidden_layers: 1` in `config.json` but **none of the `mtp.*` weights** — 0 of 760 tensors. So the stock GGUFs (official and third-party) have no MTP head and can't speculate. These do. > **`Q4_K_M` is no longer the low-VRAM recommendation.** An earlier version of this card said it > was. `IQ4_XS` beats it on size, speed and MTP gain — measured, table below.

Visual QA
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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

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