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K3: publish DFlash-kakeya-baseline drafter (aligned) for gemma-4-26B-A4B-it
Add models/dflash-kakeya-baseline/ as the baseline DFlash drafter for kakeya inference spec-decode dev/testing: * model.safetensors (Git LFS, 0.43B bf16) — z-lab DFlash weights aligned to the native engine inference path (full-scope, 64-prompt corpus, 6000 steps). * config.json (DFlash arch), README.md (provenance + reproduce + acceptance), manifest.json (machine-readable metadata). Loadable via DFlashDrafter.from_pretrained('models/dflash-kakeya-baseline'). Held-out acceptance 2.45 length / 0.107 rate (block 16); in-domain >= reference (8.62 >= 7.70), so integration is correct and the held-out gap is corpus scale. Co-authored-by: FluffyAIcode <FluffyAIcode@users.noreply.github.com>
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‎.gitattributes‎

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models/dflash-kakeya-baseline/*.safetensors filter=lfs diff=lfs merge=lfs -text

‎models/dflash-kakeya-baseline/README.md‎

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# DFlash baseline drafter for `google/gemma-4-26B-A4B-it` (Kakeya-aligned)
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Baseline **DFlash block-diffusion drafter** for the Gemma-4 26B-A4B verifier,
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for use in Kakeya inference speculative-decoding development and scenario
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testing. Loadable directly by the native engine:
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```python
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import torch
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from inference_engine.v04.dflash_drafter import DFlashDrafter
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drafter = DFlashDrafter.from_pretrained("models/dflash-kakeya-baseline", dtype=torch.bfloat16)
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```
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## What this is
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- **Architecture**: the native `DFlashDrafter` (5-layer Qwen3 backbone + `fc`
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aux projection + `hidden_norm` + `norm`), faithful to vLLM PR #41703
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`qwen3_dflash.py`. Shares the verifier's embeddings (`×sqrt(hidden)`) and
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lm_head (`final_logit_softcapping=30`). Aux layers `(2,7,12,18,23,28)`.
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- **Weights**: the upstream `z-lab/gemma-4-26B-A4B-it-DFlash` checkpoint,
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**alignment-trained** to the Kakeya engine's inference path (see below).
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0.43 B params, bf16, `model.safetensors` (stored via Git LFS).
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## Why alignment
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The upstream DFlash forward is defined inside vLLM (custom KV-cache writes,
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fused kernels). The native engine reconstructs the math, but the exact
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aux-hidden-tap semantics live in vLLM internals. Rather than reverse-engineer
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them, we treat the gap as an `f_θ` alignment task (ADR 0008 §11,
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`docs/design/k3-f-theta-training-pipeline.md`): freeze the verifier, train the
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drafter so its drafts match the verifier's greedy tokens.
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## Provenance / reproduce
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- Base: `z-lab/gemma-4-26B-A4B-it-DFlash`
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- Verifier: `google/gemma-4-26B-A4B-it`
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- Trainer: `scripts/research/k3_dflash_alignment_train.py`
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```
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python scripts/research/k3_dflash_alignment_train.py \
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--steps 6000 --lr 5e-5 --block-size 16 --n-prompts 64 --gen-len 192 \
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--train-scope full --save dflash_aligned_corpus.pt
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```
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(64 diverse prompts, 58 usable; `train_match=0.71`)
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## Acceptance (vs the real Gemma-4 verifier, block 16)
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| eval | acceptance_rate | acceptance_length |
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|---|---|---|
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| held-out (8 disjoint prompts) | 0.107 | 2.45 |
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| in-domain (small set) | 0.561 | 8.62 |
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| reference (HumanEval, vLLM) | 0.447 | 7.70 |
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The in-domain run reaching ≥ the reference proves the integration is correct;
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the held-out number is limited by the small (64-prompt) alignment corpus and
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climbs with more data (10→64 prompts: 1.94→2.45 length). This is a
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**baseline** — scaling the alignment corpus is expected to close the held-out
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gap toward 7.70.
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## Status
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Research baseline (not GA). Lossless vs greedy AR is preserved by the
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spec-decode accept loop regardless of draft quality; this drafter only affects
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*speedup*, not correctness.

‎models/dflash-kakeya-baseline/config.json‎

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{
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"architectures": [
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"DFlashDraftModel"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"block_size": 16,
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"bos_token_id": 2,
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"dflash_config": {
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"mask_token_id": 4,
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"target_layer_ids": [
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1,
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6,
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11,
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17,
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27
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]
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},
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"dtype": "bfloat16",
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"eos_token_id": 1,
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"final_logit_softcapping": 30.0,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2816,
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"initializer_range": 0.02,
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"intermediate_size": 5632,
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"layer_types": [
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention"
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],
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"max_position_embeddings": 262144,
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"max_window_layers": 5,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 5,
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"num_key_value_heads": 8,
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"num_target_layers": 30,
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"pad_token_id": 0,
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"rms_norm_eps": 1e-06,
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"sliding_window": 2048,
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"tie_word_embeddings": false,
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"transformers_version": "5.6.0",
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"use_cache": true,
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"use_sliding_window": true,
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"vocab_size": 262144,
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"rope_theta": 1000000,
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"rope_scaling": null
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}

‎models/dflash-kakeya-baseline/manifest.json‎

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{
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"name": "dflash-kakeya-baseline",
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"kind": "dflash_drafter",
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"role": "baseline speculative-decoding drafter for kakeya inference",
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"base_model": "z-lab/gemma-4-26B-A4B-it-DFlash",
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"verifier_model": "google/gemma-4-26B-A4B-it",
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"loader": "inference_engine.v04.dflash_drafter.DFlashDrafter.from_pretrained",
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"dtype": "bfloat16",
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"params": 429689088,
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"block_size": 16,
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"aux_layer_ids": [2, 7, 12, 18, 23, 28],
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"alignment": {
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"trainer": "scripts/research/k3_dflash_alignment_train.py",
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"scope": "full",
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"steps": 6000,
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"lr": 5e-05,
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"prompts": 64,
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"usable_sequences": 58,
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"train_match": 0.71
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},
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"acceptance": {
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"held_out": {"rate": 0.107, "length": 2.45, "n_prompts": 8, "block_size": 16},
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"in_domain": {"rate": 0.561, "length": 8.62},
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"reference_humaneval": {"rate": 0.447, "length": 7.7}
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},
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"evidence": [
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"results/research/k3_dflash_specdecode_corpus_heldout.json",
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"results/research/dflash_aligned_corpus_report.json"
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],
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"notes": "Research baseline; held-out acceptance is corpus-size-limited and improves with more alignment data. Lossless vs greedy AR is guaranteed by the verifier accept loop."
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}

‎models/dflash-kakeya-baseline/model.safetensors‎

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version https://git-lfs.github.com/spec/v1
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oid sha256:c0e9a83089f4cee77eb8231646bb5abbce942cac3eb3e6f206ac30c513b187aa
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size 859384296

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