Ternary-Bonsai-2-27B-MTP-GGUF
PrismML's Ternary Bonsai 2 27B (a 27B dense model with end-to-end ternary weights, Qwen3.8-27B hybrid backbone, 262K context, Apache-2.0) **with a NextN / MTP draft head bundled in**, so the model self-speculates — no second file, no external drafter. The published PrismML GGUFs carry no NextN layer. These bundles add the upstream Qwen/Qwen3.8-27B MTP head (its `mtp.*` tensors, exported by upstream llama.cpp `convert_hf_to_gguf.py --mtp --outtype q8_0`) as one extra layer; every other key and tensor is the PrismML file, byte for byte. The head stays in the normal basis (it is not among the Hadamard-rotated tensors); its token-embedding lookup on the rotated table gets the inverse transform in the engine (PrismML's `518ad108`). The vision projector is **not** duplicated here: use PrismML's `Ternary-Bonsai-2-27B-mmproj-Q8_0.gguf` (600 MiB) from the base repo with `--mmproj`.
翻訳準備中のため原文を表示しています
PrismML's Ternary Bonsai 2 27B (a 27B dense model with end-to-end ternary weights, Qwen3.8-27B hybrid backbone, 262K context, Apache-2.0) **with a NextN / MTP draft head bundled in**, so the model self-speculates — no second file, no external drafter. The published PrismML GGUFs carry no NextN layer. These bundles add the upstream Qwen/Qwen3.8-27B MTP head (its `mtp.*` tensors, exported by upstream llama.cpp `convert_hf_to_gguf.py --mtp --outtype q8_0`) as one extra layer; every other key and tensor is the PrismML file, byte for byte. The head stays in the normal basis (it is not among the Hadamard-rotated tensors); its token-embedding lookup on the rotated table gets the inverse transform in the engine (PrismML's `518ad108`). The vision projector is **not** duplicated here: use PrismML's `Ternary-Bonsai-2-27B-mmproj-Q8_0.gguf` (600 MiB) from the base repo with `--mmproj`.
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