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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-8B
+tags:
+  - speculative-decoding
+  - draft-model
+  - dflash
+  - domino
+  - sglang
+  - specforge
+---
+
+# Qwen3-8B Domino speculative-decoding draft (10-epoch)
+
+A **Domino** draft model for `Qwen/Qwen3-8B`, trained with the SpecForge
+**DataFlow online-disaggregated** pipeline (frozen Qwen3-8B target + live
+spec-capture through sglang/Mooncake -> DP domino trainer).
+
+## This checkpoint
+- `training_state.pt` — torch checkpoint; the trained draft weights are under
+  the key **`draft_state_dict`** (~1.1B params, bf16), plus training metadata
+  (`global_step`, `epoch`, `strategy`, `run_id`, ...).
+- `config.json` — the draft config (`DFlashDraftModel`, `projector_type=domino`,
+  5 layers, hidden 4096, `target_layer_ids=[1,9,17,25,33]`, `block_size=16`,
+  vocab 151936, `mask_token_id=151669`).
+- **Step 44,000** — the latest periodic checkpoint (save-interval 4000) of a
+  completed 10-epoch run (~epoch 9.3).
+
+## Training
+- Data: `perfectblend` (47,160 filtered prompts), 10 epochs, batch 2, DP=5,
+  LR 6e-4, warmup 0.04, grad-clip 1.0, num_anchors 256, loss-decay-gamma 7.0.
+- Target `Qwen/Qwen3-8B` frozen (no gradients); only the draft is trained.
+- Final training-time metrics: loss ~0.1, token-match acc ~0.95.
+
+> Note: the training-time `acc` is a proxy. Real serving accept-length (τ)
+> requires exporting to an sglang-loadable draft and running the spec-decode
+> benchmark.
+
+## Load the draft weights
+```python
+import torch
+sd = torch.load("training_state.pt", map_location="cpu", weights_only=False)
+draft_weights = sd["draft_state_dict"]  # load into a DFlashDraftModel(config)
+```