AI is happening. The future is updating.Ornith-1.0-9B-MTP-GGUF
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`.
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`.