datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
RULER-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_20_refinement_math_chess_gemma3_12b_gemma4_31b_transition_feedback_tokRULER-262144-gemma3-base2026_07_29_collect_mathnet_gemma3_12b_gemma4_31b_flsft_tokwildchat-1m-gpt-4-1-regenerated-english-unused-gemma3gemma3n-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_29_collect_mathnet_gemma3_12b_gemma4_31b_tokRULER-32768-gemma3-instruct2026_07_20_collect_codeforces_gemma3_12b_gemma4_31b_flsft_tok2026_07_16_collect_omni_math_gemma3_12b_gemma4_31b_flsft_tokgemma3n-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_raw_student_tok2026_07_20_collect_codeforces_gemma3_12b_gemma4_31b_tok2026_08_11_refinement_5env_gemma3_12b_gemma4_31b_raw_student_tok2026_07_16_collect_omni_math_gemma3_12b_gemma4_31b_tokgemma3_12b_it_hc_ssss_n32_r1_dpogemma-3-12b-it-nla-wildchat-20k
Gemma-3-12B-IT NLA data, WildChat — blocks 24 / 32 / 40
Residual-stream activations from google/gemma-3-12b-it over 20,000 WildChat
conversations, at three depths, 200,000 activations per layer (10 token
positions per conversation).
This is the chat-domain counterpart to
achand45/gemma-3-12b-it-nla-data,
which is the same model and the same extraction contract over FineFineWeb web
text. Same injection marker (㈜, token 246566), same prompt template, same
d_model. Swap the corpus… See the full description on the dataset page: https://huggingface.co/datasets/Yooniel/gemma-3-12b-it-nla-wildchat-20k.dpo-yolo1-200k-gpt4.1-judge-2weak2strong-maxdelta_rejected-DECON-remove-gemma3gemma3-reasoning-dropin
Gemma3 Reasoning Drop-In Dataset
Drop-in replacement dataset for Gemma3-style notebooks expecting task, input, expected_output fields.
Usage
from datasets import load_dataset
dataset = load_dataset("Cyleux/gemma3-reasoning-dropin", split="train[:10000]")
Then your existing mapping code can stay the same:
task -> system
input -> user
expected_output -> assistant
Reasoning is preserved in expected_output using <think>...</think> blocks.
Stats
{
"input":… See the full description on the dataset page: https://huggingface.co/datasets/Cyleux/gemma3-reasoning-dropin.2026_07_20_collect_lichess_gemma3_12b_gemma4_31b_flsft_tok2026_07_20_collect_lichess_gemma3_12b_gemma4_31b_raw_student_no_feedback_tok