inference-optimization/speculators-ci-datasets
speculator-tutorial Raw vs. on-policy regenerated conversation data for training speculative-decoding drafters (EAGLE-3 / DFlash / DSpark style), with the original source data kept alongside so you can see exactly what regeneration changes and why it matters. Prompts come from UltraChat-200k. The verifier / teacher model is Qwen/Qwen3-8B. Why regenerate at all? A speculative-decoding drafter is trained to predict what the verifier would say next. If you train it… See the full description on the dataset page: https://huggingface.co/datasets/inference-optimization/speculators-ci-datasets.
| Author / organization | inference-optimization |
|---|---|
| License (as declared) | mit |
| Downloads | 1.1K (as reported by Hugging Face) |
| Likes | 0 |
| Created | 10 August 2026 |
| Last updated at source | 10 August 2026 |
Popularity, last 2 days
1,104 now (+54 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
More from inference-optimization
Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.