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Domain-Aware Speculative Decoding — Synthetic Dataset
Synthetic distillation data for training domain-specific draft models in speculative decoding.
Overview
Generated by running TurboSparse-Mistral-Instruct (7B) in teacher-forcing mode on Flan tasks and capturing top-10 token distributions at each position.
Structure
train/v3/ — 66 NPZ files (2.2 GB), one per Flan cluster
validation/v3/ — 66 JSONL files (93 MB)
test/v3/ — 66 NPZ files (598 MB)
Record Schema (JSONL)
{
"cluster": "aeslc_10templates",
"prompt": "<flan input>",
"reference": "<flan target>",
"trunk": [1234, 567, ...],
"top10_ids": [[1234, 99, 12, ...], ...],
"top10_probs": [[0.45, 0.20, 0.05, ...], ...]
}
NPZ Format
Same fields stored as numpy arrays. See DATASET_FORMAT.md.
Target Model
- TurboSparse-Mistral-Instruct (7B, BambooForCausalLM)
- Vocab: 32064 tokens (Mistral base + 64 special)
Usage
from huggingface_hub import snapshot_download
snapshot_download("MikhailRudenko/domain-aware-sd-synthetic", repo_type="dataset", local_dir="data/synthetic")
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