datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
2026_08_20_refinement_math_chess_gemma3_12b_gemma4_31b_transition_feedback_tok2026_08_11_refinement_5env_gemma3_12b_gemma4_31b_raw_student_tokRULER-262144-gemma3-instruct2026_08_05_refinement_5env_gemma3_12b_gemma4_31b_tokgemma-3-taide-12b-chat-eval-logs-and-scoresgemma-3-4b-it-eval-logs-and-scoresgemma-3-4B-T1-it-eval-logs-and-scoresGemma-3-12b-it-eval-logs-and-scores2026_08_09_refinement_5env_gemma3_12b_gemma4_31b_flsft_tokgemma-3-27b-it-eval-logs-and-scores2026_07_19_collect_leandojo_gemma3_12b_gemma4_31b_flsft_tok2026_08_12_refinement_math_chess_gemma3_12b_gemma4_31b_raw_student_tokgemma3n-conversational-reasoning
Gemma3N Conversational Reasoning
This dataset is prepared for Unsloth Gemma3/Gemma3N conversational notebooks that use:
from datasets import load_dataset
from unsloth.chat_templates import standardize_data_formats
dataset = load_dataset("Cyleux/gemma3n-conversational-reasoning", split="train[:3000]")
dataset = standardize_data_formats(dataset)
Schema:
conversations: ShareGPT-style list of turns with from and value
metadata columns are included for analysis and filtering
Notes:… See the full description on the dataset page: https://huggingface.co/datasets/Cyleux/gemma3n-conversational-reasoning.2026_07_19_collect_leandojo_gemma3_12b_gemma4_31b_raw_student_tokRULER-262144-gemma3-base2026_07_29_collect_mathnet_gemma3_12b_gemma4_31b_flsft_tok2026_07_20_collect_lichess_gemma3_12b_gemma4_31b_raw_student_no_feedback_tok2026_07_29_collect_mathnet_gemma3_12b_gemma4_31b_transition_feedback_tokwildchat-1m-gpt-4-1-regenerated-english-unused-gemma32026_07_20_collect_codeforces_gemma3_12b_gemma4_31b_raw_student_tok2026_07_29_collect_mathnet_gemma3_12b_gemma4_31b_raw_student_no_feedback_tokRULER-32768-gemma3-instruct2026_07_29_collect_mathnet_gemma3_12b_gemma4_31b_tok2026_07_20_collect_codeforces_gemma3_12b_gemma4_31b_flsft_tok2026_07_20_collect_lichess_gemma3_12b_gemma4_31b_transition_feedback_tok2026_07_16_collect_omni_math_gemma3_12b_gemma4_31b_flsft_tok2026_08_26_omni_math_train_feedback_adherence_gemma3_12b_gemma4_31b_candidates
Omni-MATH train feedback-adherence candidates
Production candidate data for studying whether a student follows teacher feedback.
Student: google/gemma-3-12b-it
Teacher and adherence judge: google/gemma-4-31B-it
Source problems: LLParallax/Omni-MATH-filtered, train partition after a fixed 512-problem test split
Source trajectories: LLParallax/2026_07_16_collect_omni_math_gemma3_12b_gemma4_31b
Collection config:… See the full description on the dataset page: https://huggingface.co/datasets/1337xyz1337xyz/2026_08_26_omni_math_train_feedback_adherence_gemma3_12b_gemma4_31b_candidates.gemma3n-slicing-configsThis repository contains configurations to slice Gemma 3n E4B, which is enabled thanks to it being a MatFormer.
The E4B model can be sliced into small models, trading off quality and latency/compute requirements.
We recommend exploring the [MatFormer Lab](TODO: add link) to getting started with slicing Gemma 3n E4B yourself.
For each configuration, we calculate the MMLU accuracy.
Although these are not the only configurations possible, they are optimal configurations
identified by calculating… See the full description on the dataset page: https://huggingface.co/datasets/google/gemma3n-slicing-configs.Selective-Context-Gemma3-12B-results2026_07_16_collect_omni_math_gemma3_12b_gemma4_31b_tok