Update DFlash to 1M-verifier continuation step 15000

.gitattributes

@@ -33,4 +33,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-Laguna-S-2.1-DFlash-BF16.gguf filter=lfs diff=lfs merge=lfs -text

README.md

@@ -8,72 +8,11 @@ base_model:
 - poolside/Laguna-S-2.1
 ---
 
-<p align="center">
-  <img alt="poolside-banner" src="https://poolside.ai/assets/laguna/laguna-s-2-1-banner.svg" width="800px">
-</p>
+# Laguna-S-2.1-DFlash-polishing
 
-<p align="center">
-  <a href="https://openrouter.ai/poolside/laguna-s-2.1"><strong>Use on OpenRouter</strong></a> ·
-  <a href="https://vercel.com/ai-gateway/models/laguna-s-2.1"><strong>Use on Vercel AI Gateway</strong></a> ·
-  <a href="https://poolside.ai/blog/introducing-laguna-s-2-1"><strong>Release blog post</strong></a>
-</p>
-
-<br>
-
-# Laguna-S-2.1-DFlash
-
-DFlash speculator (draft model) for
-[Laguna S 2.1](https://huggingface.co/poolside/Laguna-S-2.1), bf16.
+DFlash speculator (drafter) 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).
+- Source checkpoint: `s3://poolside.titan.checkpoints.us-east-2/adam/checkpoints/ft_sft_e0630_rhiemann_baseline_titan_sft_training/0008400/DFlash_Stage2/25000`
 - Loads under vLLM (native `laguna_dflash`) and TRT-LLM (pytorch DFlash backend) as the draft model in a speculative config.
-
-## Usage
-
-### vLLM
-
-```shell
-vllm serve --model poolside/Laguna-S-2.1 \
-    --speculative-config '{"model":"poolside/Laguna-S-2.1-DFlash","num_speculative_tokens":7,"method":"dflash"}' \
-    ...
-```
-
-### SGLang
-
-```shell
-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](https://huggingface.co/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:
-
-```shell
-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`](https://github.com/poolsideai/llama.cpp/tree/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](https://github.com/ggml-org/llama.cpp/pull/25165)
-> covers the target architecture only.

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