FINAL-Bench / Darwin-28B-Coder-GGUF

Darwin-28B-Coder — GGUF (MTP-enabled)

GGUF builds of FINAL-Bench/Darwin-28B-Coder with the native Multi-Token Prediction (MTP) head preserved, for self-speculative decoding in llama.cpp .

Requested in the base model discussion .

Files

All files include the MTP layer — verified in metadata: general.architecture = qwen35 , qwen35.nextn_predict_layers = 1 , tensors blk.64.nextn.* .

Multi-Token Prediction (MTP)

This model ships with a trained MTP head (1 prediction layer). With a recent llama.cpp build that includes MTP support (merged in PR #22673 ), the nextn layer is used for self-speculative decoding — typically ~1.5–2× faster generation with identical output (the main model verifies every drafted token, so quality is unchanged).

A standard (non-MTP) GGUF does not contain the prediction head — you need these MTP-enabled files to benefit from the speedup.

Usage

# 1) Build a recent llama.cpp (MTP support is in mainline since PR #22673)
git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
cmake -B build -DGGML_CUDA=ON && cmake --build build -j --config Release

# 2) Run — the nextn (MTP) layer enables self-speculative decoding
./build/bin/llama-cli \
  -m Darwin-28B-Coder-Q4_K_M.gguf \
  -ngl 99 -c 8192 \
  -p "Write a quicksort in Python."

For the exact MTP/speculative flags and the latest behaviour, see the llama.cpp MTP documentation / PR #22673. Works with llama-cli and llama-server .

Model spec (public)

License & attribution

License and usage follow the base model FINAL-Bench/Darwin-28B-Coder . These are GGUF conversions only; refer to the base model card for model details, intended use, and limitations.

GGUF conversion + quantization by the FINAL-Bench team using llama.cpp/convert_hf_to_gguf.py .