datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.dflash-code-multilingual-teacher-responses-qwen235b
Code + Multilingual Teacher Responses (Qwen3-235B-A22B-Instruct-2507)
This repo now contains 302,800 total samples across the main blended
data.jsonl / .parquet file plus a second Nemotron-only file
(nemotron_code_teacher_responses.jsonl / .parquet). All responses were
generated by Qwen3-235B-A22B-Instruct-2507 in non-thinking mode
(enable_thinking=false) to match downstream speculator training and eval.
Built in two batches: an initial 59,506-row batch (50K code + 9.5K… See the full description on the dataset page: https://huggingface.co/datasets/inference-optimization/dflash-code-multilingual-teacher-responses-qwen235b.