TL;DR: the official DSpark speculative drafter for DeepSeek-V4-Flash-0731, converted from the ds4-fork GGUF format (deepseek4-dspark arch) to mainline llama.cpp format (dflash arch + DSpark markov/confidence heads). As far as I know this is the first publicly available DSpark drafter that loads on upstream llama.cpp (--spec-type draft-dspark). Numerical content is byte-identical to the source checkpoint (bleysg's 0731 drafter, Q2_K experts / Q8_0 attention): pure rename of arch string, KV metadata keys and tensor names, plus the tokenizer block copied from the target model (the fork format carries none) and tokenizer.ggml.mask_token_id set to the DSpark noise token (128799).

--spec-draft-n-max 5 matters: the drafter was trained with block_size=5 and the default (3) wastes 2 draft positions per block.

Target model: Unsloth UD-IQ3_XXS (97 GB), full GPU offload, temperature 0.

Two practical notes from the same session: (1) speculative decoding composes badly with heavy CPU expert offload — the k-token verify batch touches ~k× distinct experts on the CPU side, so keep offload to 1-2 layers if you need it at all; (2) llama.cpp CUDA copies weights into device buffers (it is not an mmap view), so leave real headroom on unified-memory boxes.

Unsloth's published KLD vs Q8 (lower = closer to full quality), mapped onto what actually runs on a 121 GB unified-memory box together with this 6.5 GB drafter:

Extra datapoint: splicing Q3_K_XL expert tensors into the IQ3_XXS base (same Unsloth imatrix, bytes directly transplantable) recovers only ~25% of the quality gap when swapping 21/43 expert layers — first or last, nearly the same — i.e. the quantization divergence is spread almost uniformly across layers. There is no free intermediate quant on this hardware.

dspark-drafter-convert.py (included) does the whole thing in ~1 minute with gguf-py only — no requantization. Per-layer tensors map 1:1 (dspark.N.* → blk.N.*, names identical); globals: main_proj→fc, main_norm→enc.output_norm, norm→output_norm, hc_head_*→output_hc_* (the only non-obvious rename); KV: dflash.block_size=5, dflash.target_layers=[40,41,42], hparams copied from the target model's metadata (the drafter stages are full DSV4 blocks with the same dimensions). The script asserts tensor count, shapes and swap coverage, and is trivially adaptable to future DSpark checkpoints in the fork format.

Source checkpoint: bleysg's DSpark-drafter-Q2K-Q8-0731 (ds4 fork format). All credit for the drafter training goes to the DeepSeek team; conversion only here. MIT, same as the base model.