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.Qwen3-8B-Regenerated-Collectionspeculators_benchmarks_tool_calldflash-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.Qwen3.5-0.8B-responsesQwen3-30B-A3B-responsesSWE-bench_MultilingualQwen3.5-4B-responsesgpt-oss-120b-responsesLongbench_Samples_SpecdecQwen3-32B-responsesQwen3-8b-sharegpt-5kdflash-qwen3-8b-qwen235b-instruct-bs16-prepared-datalaguna-xs-ultrachat-responsesevery-eval-ever-demoQwen3.5-9B-responsesDeepSeek-V4-Flash-responsesspeculators-qwen3-30b-a3b-instruct-2507SWE-bench_Lite
Dataset Summary
SWE-bench Lite is subset of SWE-bench, a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 300 test Issue-Pull Request pairs from 11 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
The dataset was released as part of SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
Want to run inference now?
This dataset only contains the… See the full description on the dataset page: https://huggingface.co/datasets/inference-optimization/SWE-bench_Lite.Dataset-Qwen3-235B-InstructSWE-bench_VerifiedDataset Summary
SWE-bench Verified is a subset of 500 samples from the SWE-bench test set, which have been human-validated for quality. SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. See this post for more details on the human-validation process.
The dataset collects 500 test Issue-Pull Request pairs from popular Python repositories. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
The original… See the full description on the dataset page: https://huggingface.co/datasets/inference-optimization/SWE-bench_Verified.gpt-oss-20b-nan-hidden-states-reprolaguna-xs-ultrachat-conversationsctest-Qwen3.6-27B-speculator-datasetlaguna-xs-magpie-300k-responsesGemma4-Responses-Nemotronctest-subset-Qwen3.5-397B-A17B-FP8-dynamic-speculator-datasetqwen3-test-modellaguna-xs-magpie-300k-conversationsupdated-ctest-Qwen3-8B-speculator-dataset