Laguna-S-2.1-DFlash

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Laguna-S-2.1-DFlash

DFlash speculator (draft model) for Laguna S 2.1, bf16.

• Architecture: DFlashLagunaForCausalLM (6 sliding-attention layers, block_size 16).
• Shares token embedding + lm_head with the target; draft_vocab_size == vocab_size (no d2t/t2d).
• Loads under vLLM (native laguna_dflash) and TRT-LLM (pytorch DFlash backend) as the draft model in a speculative config.

Usage

vLLM

vllm serve --model poolside/Laguna-S-2.1 \
    --speculative-config '{"model":"poolside/Laguna-S-2.1-DFlash","num_speculative_tokens":7,"method":"dflash"}' \
    ...

SGLang

python -m sglang.launch_server --model-path poolside/Laguna-S-2.1 \
  --speculative-algorithm DFLASH \
  --speculative-draft-model-path poolside/Laguna-S-2.1-DFlash \
  ...

llama.cpp (GGUF)

A llama.cpp conversion of this draft model (laguna-s-2.1-DFlash-BF16.gguf) is published in poolside/Laguna-S-2.1-GGUF alongside the target GGUFs. It embeds the target tokenizer and the DFlash metadata (dflash.decoder_arch = laguna, capture layers, block size), so it works directly as the -md draft model:

git clone --branch laguna https://github.com/poolsideai/llama.cpp
cd llama.cpp && cmake -B build && cmake --build build -j

./build/bin/llama-server \
  -m laguna-s-2.1-Q4_K_M.gguf \
  -md laguna-s-2.1-DFlash-BF16.gguf \
  --spec-type draft-dflash --spec-draft-n-max 7 \
  -fa on --jinja --port 8000

--spec-draft-n-max is clamped to the trained block size (15 draft tokens + 1).

[!NOTE] Requires Poolside's llama.cpp fork, branch laguna. Upstream llama.cpp ships the generic DFlash framework but not the Laguna decoder contract this draft model needs, and upstream PR ggml-org/llama.cpp#25165 covers the target architecture only.