datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ablation_nemotron_thinking_32k_with_reasoning_effort
Dataset: ablation_nemotron_thinking_32k_with_reasoning_effort
This dataset was uploaded from /mnt/yulan_pretrain/mount/data_final_train/ablation_nemotron_thinking_32k_with_reasoning_effort/stage_1/tmp/.
imabari_wiki_qa_v4_validated_w_reasoning_effort_qwen38
Imabari Wiki QA v4 Validated with Reasoning Effort — Qwen3.8
概要 / Overview
日本語・今治弁のQAを用いて、reasoning effort に応じた思考文の生成を学習するための教師ありファインチューニング（SFT）用データセットです。Imabari Wiki QA v4 Validated の質問と回答を保持し、元記事の文脈を参照して思考文を再生成しています。
This dataset supports supervised fine-tuning (SFT) of reasoning-effort-conditioned explanations using Japanese QA with Imabari dialect expressions. Questions and answers from Imabari Wiki QA v4 Validated are preserved, while reasoning text is… See the full description on the dataset page: https://huggingface.co/datasets/ikedachin/imabari_wiki_qa_v4_validated_w_reasoning_effort_qwen38.imabari_wiki_qa_v4_reasoning_effort_llmjp4
Imabari Wiki QA v4 with Reasoning Effort — LLM-jp 4
概要 / Overview
日本語・今治弁のQAを用いて、reasoning effort に応じた思考文の生成を学習するための教師ありファインチューニング（SFT）用データセットです。Imabari Wiki QA v4 Validated の質問と回答を保持し、元記事の文脈を参照して思考文を再生成しています。
This dataset supports supervised fine-tuning (SFT) of reasoning-effort-conditioned explanations using Japanese QA with Imabari dialect expressions. Questions and answers from Imabari Wiki QA v4 Validated are preserved, while reasoning text is regenerated… See the full description on the dataset page: https://huggingface.co/datasets/ikedachin/imabari_wiki_qa_v4_reasoning_effort_llmjp4.gpt-ru-reasoning_effort-sftimabari_wiki_qa_v4_reasoning_effort_qwen38
Imabari Wiki QA v4 with Reasoning Effort — Qwen3.8
概要 / Overview
日本語・今治弁のQAを用いて、reasoning effort に応じた思考文の生成を学習するための教師ありファインチューニング（SFT）用データセットです。Imabari Wiki QA v4 Validated の質問と回答を保持し、元記事の文脈を参照して思考文を再生成しています。
This dataset supports supervised fine-tuning (SFT) of reasoning-effort-conditioned explanations using Japanese QA with Imabari dialect expressions. Questions and answers from Imabari Wiki QA v4 Validated are preserved, while reasoning text is regenerated with… See the full description on the dataset page: https://huggingface.co/datasets/ikedachin/imabari_wiki_qa_v4_reasoning_effort_qwen38.ru-reasoning_effort-sft_dpo_think_gpt
NotEvilAI/ru-reasoning_effort-sft_dpo_think_gpt
NotEvilAI/ru-reasoning_effort-sft_dpo_think_gpt -
синтетический датасет для поддержки генерации ризонинга на русском языке с вариативным объёмом thinking(reasoning_effort).
Reasoning_effort представлен в виде системного промта Reasoning: [effort], где effort - одно из следующих значений:
low, medium, high - стандартные значения минимального, среднего и большого ризонинга для gpt-oss-20b/gpt-oss-120b
none - отключить ризонинг, в… See the full description on the dataset page: https://huggingface.co/datasets/NotEvilAI/ru-reasoning_effort-sft_dpo_think_gpt.std_lang_wiki_qa_v4_validated_w_reasoning_effort_llmjp4
Standard Japanese Wiki QA v4 with Reasoning Effort — LLM-jp 4
概要
Imabari Wiki QA v4 with Reasoning Effort — LLM-jp 4 の thinking（思考文）と answer（最終回答）の言葉遣いを標準語（です・ます調）へ変換した派生データセットです。日本語QAの教師ありファインチューニング（SFT）や、reasoning effort に応じた説明文・回答の生成に利用できます。
質問に新たに回答したり、推論内容を追加したりすることは目的としていません。元の意味・結論・根拠を保持し、今治弁などの方言表現を標準語へ置き換えるよう指示しています。質問は変換対象に含めていません。
LLM-jp 4 のチャットテンプレート用に、assistant の content に最終回答、thinking に思考文を格納しています。「LLM-jp 4」は対象のデータ形式を示します。元の思考文生成モデルと標準語化の設定モデルは… See the full description on the dataset page: https://huggingface.co/datasets/ikedachin/std_lang_wiki_qa_v4_validated_w_reasoning_effort_llmjp4.ablation_openmathreasoning_thinking_with_reasoning_effort
Dataset: ablation_openmathreasoning_thinking_with_reasoning_effort
This dataset was uploaded from /mnt/yulan_pretrain/mount/data_final_train/ablation_openmathreasoning_thinking_with_reasoning_effort/stage_1/tmp/.
sherry-reasoning-effort-0.1-dataset
Sherry Reasoning Effort Dataset (0.1)
Math reasoning traces at 3 effort levels (low, medium, high) generated by Qwen3.6-35B-A3B (NVFP4) via API.
Description
Each problem was sampled from NuminaMath-CoT (numeric-answer problems only, proofs filtered out).
For every problem, 3 candidates were sampled from the generator model with thinking enabled,
verified against the ground truth, and the 3 verified traces with different reasoning depths were kept:
the shortest… See the full description on the dataset page: https://huggingface.co/datasets/valendra/sherry-reasoning-effort-0.1-dataset.ru-reasoning_effort-sft_dpo_think_gpt-dpo_gptgpt-ru-reasoning_effort-sftru-reasoning_effort-sft_dpo_think_gpt-gpt-fixed-none-reasoningru-reasoning_effort-sft_dpo_think_gpt-gpt-fixed-bad-answersui-reasoning-effort-rollouts-full
UI Reasoning Effort Rollouts
Completed rollout records with reasoning traces, UIClip scores, token counts, latency, and source metadata.
Hub repo: https://huggingface.co/datasets/ciderlab/ui-reasoning-effort-rollouts-full
Rows: 667
train: 667
The dataset is stored with Hugging Face's native save_to_disk() format and embeds raw responses, HTML, screenshots, and prompt templates.
Columns
model_name: model or deployment name used for the rollout.
served_model:… See the full description on the dataset page: https://huggingface.co/datasets/ciderlab/ui-reasoning-effort-rollouts-full.test_reasoning_effort