TB-KD draft (ToolBench contexts)

A draft model for speculative decoding against the frozen target Qwen/Qwen2.5-Coder-14B-Instruct (vLLM speculative decoding with method=draft_model), produced by Stage-1 KD on ToolBench contexts (ablation A). Full project, training scripts, and the complete acceptance / wall-clock / exactness evaluation: Speculative_Decoding.

Qwen2.5-Coder-0.5B-Instruct distilled with the same KD recipe on 5k ToolBench multi-turn assistant-turn boundary contexts (incl. post-observation states) — ablation A of the Speculative_Decoding project; the off-policy checkpoint that matched on-policy GKD on transfer evals.

Measured (k=5, greedy, 100 frozen prompts, instrumented HF loop)

eval	tau	alpha	exactness vs AR
xLAM-500	4.07	0.948	50/50 token-identical
TB-500 (held-out tools)	3.04	0.852	50/50 token-identical

Outputs are token-identical to plain greedy decoding of the target in the HF instrumented loop (exactness gates; the vLLM bf16 near-tie caveat is documented in the repo README). The vocabulary is zero-padded to 152,064 rows to satisfy vLLM s SpeculativeConfig same-vocab-size requirement; no real token id lives in the padding band, and the padding is gated on greedy parity (see src/serving/prepare_draft.py).

Benchmark context: vLLM 0.30.0, target bf16, k=5, batch 1. This model is a research artifact from a 1-day H100 study; no safety fine-tuning was performed (the base model s policies apply).