datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
gemma4-german-tutor-data
German Tutor — grammar correction, conversation & flashcard data
The training set, evaluation suites, source lexicons and eval results behind
kessenma/gemma4-e4b-german-tutor-4bit
— a Gemma 4 E4B fine-tune that runs fully on-device (MLX, 4-bit) as the tutor in a German
learning app.
The fine-tune lifted the core grammar suite from 72% → 85%, halved missed errors
(17% → 9%), and cut false corrections (34% → 22%). Everything needed to reproduce those
numbers is in this repo.… See the full description on the dataset page: https://huggingface.co/datasets/kessenma/gemma4-german-tutor-data.gemma4-german-sft-corpus
Gemma-4-E4B German SFT Corpus — 4 controlled variants
Curated, native-heavy German supervised-fine-tuning (SFT) corpus, built to improve the
general German skill of unsloth/gemma-4-E4B-it via LoRA — NOT to target any single
benchmark. The EuroEval-ported German benchmarks (scala_de, sb10k_de, include_de,
mmlu_prox_de, germeval_de, germanquad_de, …) are used only as honest thermometers, never
as training signal — no benchmark train/test split is mixed in, deliberately, to avoid… See the full description on the dataset page: https://huggingface.co/datasets/peerbench/gemma4-german-sft-corpus.gemma4-31b-tool-selector-sft-v1.1
Gemma 4 31B Tool Selector SFT v1.1
Balanced supervision for a strict single-call selector that either emits one
supported deterministic tool invocation or explicitly defers to a fixed neural
verifier. This is the training lineage for the selected Gemma 4 31B selector
adapter.
Contents
Split
Rows
Tool
Defer
Purpose
train
1,408
704
704
Optimization
validation
384
192
192
Training-time validation
audit
256
—
—
Final audit only
Both canonical… See the full description on the dataset page: https://huggingface.co/datasets/Kobarac/gemma4-31b-tool-selector-sft-v1.1.nemotron-cc-atomic-simplification-gemma4-31b
nemotron-cc atomic-statement simplification (Gemma 4 31B-it)
2,000,000 records: source text from
nvidia/nemotron-cc-v2.1 (High-Quality-Synthetic
split) rewritten by google/gemma-4-31B-it into a sequence of atomic, Subject-Verb-Object
statements.
Generated with vLLM 0.22.1 in-process batch inference (see src/generate/run.py in the
producing repo), TP=4, max_model_len=16384, max_tokens=8192, prompts filtered to
<=8192 templated tokens.
Fields
id: original… See the full description on the dataset page: https://huggingface.co/datasets/rpisano/nemotron-cc-atomic-simplification-gemma4-31b.gemma4-onpolicy-student-corrections
Gemma 4 12B FrontierDistill - On-Policy Student Failure Corrections
Attribution Requirement: This dataset was created and curated by True2456. Any use, redistribution, derivative dataset, model fine-tune, or paper using this dataset MUST cite and reference True2456 and the Gemma 4 12B FrontierDistill Project.
This dataset contains 2,000 on-policy student failure corrections collected live from Gemma 4 12B (gemma-4-12b-it-qat-frontierdistill).
Every example in this dataset… See the full description on the dataset page: https://huggingface.co/datasets/True2456/gemma4-onpolicy-student-corrections.caliber-extension-gemma4-e2b-grpo-rollouts
CALIBER Extension — Gemma4-E2B GRPO Rollouts
Training rollouts from matched GRPO arms on google/gemma-4-E2B-it
(new-prompt template, non-thinking, full bf16, max completion 1500, 150 steps).
Subsets
subset
arm
τ
prior
rows
mean reward_total
accuracy
full schema
caliber
vanilla CALIBER
0.0
—
1600
2.298
0.514
0.664
mink
Min-K% prior
1.0
mink_0.2
4800
2.506
0.520
0.680
minkpp
Min-K++% prior
1.0
minkpp_0.2
4800
2.637
0.541
0.726
Load:
from datasets… See the full description on the dataset page: https://huggingface.co/datasets/dmnsh/caliber-extension-gemma4-e2b-grpo-rollouts.cyberforge-teacher-traj-gemma4-31b
CyberForge Teacher Trajectories (Gemma-4-31B)
880 agentic security-patch trajectories from the Gemma-4-31B self-distillation teacher, cleansed to the
final versions used to train the student models in the CyberForge paper.
Each line is one trajectory (JSONL): messages (system / user / assistant turns of the
mini-swe-agent loop) and metadata.
Teacher: Gemma-4-31B self-distillation teacher
Records: 880
Format: JSONL, one trajectory per line
Related
Companion… See the full description on the dataset page: https://huggingface.co/datasets/AmL-hug/cyberforge-teacher-traj-gemma4-31b.gemma4-onpolicy-50topics-2000-corrections
Gemma 4 12B FrontierDistill - 2,000 Authentic 50-Topics On-Policy Student Failure Corrections
Attribution Requirement: This dataset was created and curated by True2456. Any use, redistribution, derivative dataset, model fine-tune, or paper using this dataset MUST cite and reference True2456 and the Gemma 4 12B FrontierDistill Project.
This dataset contains 2,000 authentic on-policy student failure corrections collected live from Gemma 4 12B (gemma-4-12b-it-qat-frontierdistill)… See the full description on the dataset page: https://huggingface.co/datasets/True2456/gemma4-onpolicy-50topics-2000-corrections.personahub-teacher-scale-9k-gemma4-sft-20260514
PersonaHub Teacher Scale 9k Gemma4 SFT
Trainer-ready JSONL for TRL/Gemma SFT. Each row has messages, and the final message is the assistant target.
This is an emergency synthetic PersonaHub-seeded SFT pilot artifact generated on 2026-05-14. Treat as research/training pilot data; run qualitative audits before production training decisions.
Files:
train.jsonl: trainer-ready messages format
manifest.json: counts and provenance summary
Schema per row:
{"case_id":"..."… See the full description on the dataset page: https://huggingface.co/datasets/Pranavz/personahub-teacher-scale-9k-gemma4-sft-20260514.gemma-4-31B-korean-on-policy-150k
Korean On-Policy QA (Gemma 4) — EAGLE-3 training data
Korean instruction/response pairs where the responses were regenerated on-policy by
a Gemma 4 verifier. Originally built to retrain an EAGLE-3 speculator for Korean, but
also usable for general Korean instruction-tuning / distillation.
Structure
Rows: ~150,000
Language: Korean
Columns:
instruction (string) — the question / instruction
output (string) — the verifier-generated response
messages (list) — chat… See the full description on the dataset page: https://huggingface.co/datasets/BCCard/gemma-4-31B-korean-on-policy-150k.gemma4-onpolicy-50topics-corrections
Gemma 4 FrontierDistill - Authentic 50-Topics On-Policy Student Failure Corrections
Attribution Requirement: This dataset was created and curated by True2456. Any use, redistribution, derivative dataset, model fine-tune, or paper using this dataset MUST cite and reference True2456 and the Gemma 4 FrontierDistill Project.
This dataset contains 1,000 authentic on-policy student failure corrections collected live from gemma-4-12b-it-qat-frontierdistill across 50 distinct… See the full description on the dataset page: https://huggingface.co/datasets/True2456/gemma4-onpolicy-50topics-corrections.gemma-4-31b-it-304xTrace of Gemma 4 31B LLM.
Data count (Total: 304):
English - 194
Russian - 110
Data is presented in {"messages":[{"role":"user", "content":"Prompt"}, {"role":"assistant", "content": "Response"}]} format and each conversation split by newline.
gemma-4-26B-A4B-korean-on-policy-150k
Korean On-Policy QA (Gemma 4 26B-A4B) — EAGLE-3 training data
Instruction/response pairs whose responses were regenerated on-policy by
BCCard/gemma-4-26B-A4B-it-FP8-Dynamic. Built to retrain an EAGLE-3 speculator for
Korean, but also usable for general instruction-tuning / distillation.
Structure
Rows: ~150,000
Columns: instruction (str), output (str, verifier-generated), messages (chat list)
Split: train
How it was made
Prompt source:… See the full description on the dataset page: https://huggingface.co/datasets/BCCard/gemma-4-26B-A4B-korean-on-policy-150k.gemma4-coder-tool-eval
Gemma-4 Coder — tool-calling gate suite
The 8-case eval behind the tool-call pass rate in our model cards' model-index:
positive prompts where the model must emit a structured tool call, plus a no-tool
abstain case (it must answer directly, not hallucinate a call). Use it to measure
any local tool-calling model the same way we do — or to reproduce our numbers.
from datasets import load_dataset
cases = load_dataset("tpls/gemma4-coder-tool-eval", split="train") # each: {name… See the full description on the dataset page: https://huggingface.co/datasets/tpls/gemma4-coder-tool-eval.arabic-daily-thinking-gemma4-500
Arabic Daily Conversations with Thinking — Gemma 4 (492 samples)
Overview
492 multi-turn Arabic conversations (target was 500, 8 failed in pipeline)
6 daily-life domains: family_parenting, food_cooking, health_wellness, relationships_social, personal_growth, home_lifestyle
Each assistant turn has both thinking and text fields
Generated April 2026
Generation Pipeline
User side: claude-sonnet-4-6 via openai-next proxy (with randomized inline… See the full description on the dataset page: https://huggingface.co/datasets/Jianshu001/arabic-daily-thinking-gemma4-500.Gemma4NPC-Quest-Dataset
Dataset Card for Gemma4NPC Preference Dataset
Dataset Description
The Gemma4NPC Preference Dataset is a specialized text-generation and reinforcement learning dataset designed to train Large Language Models (LLMs) for use as Non-Playable Characters (NPCs) in video games.
Integrating LLMs into game engines requires models that can seamlessly blend creative roleplay with strict formatting requirements. This dataset addresses two primary training objectives:… See the full description on the dataset page: https://huggingface.co/datasets/spy5er/Gemma4NPC-Quest-Dataset.Gemma4-NPC-Dataset
Dataset Card for Gemma4NPC Preference Dataset
Dataset Description
The Gemma4NPC Preference Dataset is a specialized text-generation and reinforcement learning dataset designed to train Large Language Models (LLMs) for use as Non-Playable Characters (NPCs) in video games.
Integrating LLMs into game engines requires models that can seamlessly blend creative roleplay with strict formatting requirements. This dataset addresses two primary training objectives:… See the full description on the dataset page: https://huggingface.co/datasets/spy5er/Gemma4-NPC-Dataset.