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VIDRAFT's on-device family: a 35B model that runs on iPhone and on CPU with no GPU — stock llama.cpp, no fork.

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

File	Quant	Size	Notes
`Darwin-28B-Coder-Q4_K_M.gguf`	Q4_K_M	16.8 GB	recommended for most GPUs
`Darwin-28B-Coder-Q8_0.gguf`	Q8_0	29.0 GB	near-lossless
`Darwin-28B-Coder-F16.gguf`	F16	54.7 GB	full precision

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)


Architecture	`qwen35` (hybrid attention)
Layers	64 + 1 MTP
Hidden size	5120
Attention heads	24 (KV 4)
Context length	262,144
Vocab	248,320
Precision (source)	bfloat16

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.

Learn more

Running large coding models on-device without a GPU: Can you run a large LLM without a GPU?
What model quantization is: What is model quantization?

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

📱 Run it on your phone or a GPU-less PC → POCKET · 🚀 Try it live (CPU chat)