datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
gemma4-serving-bench-data
Gemma 4 12B (QAT-Q4_0) — Serving-Behavior Test Data
Test data, charts, and the running research log from an autonomous research
loop characterizing and tuning a Gemma 4 12B QAT-Q4_0 model served via
llama.cpp/llamafile on a single RTX 3080 Ti. Every ~30 min the loop
summarizes findings, proposes a goal, tests it end-to-end, documents success or
failure, and publishes here + to GitHub.
Model under test: gemma-4-12b-it-qat-q4_0.gguf (Google, June 2026), 128K
ctx, f16 KV, MTP… See the full description on the dataset page: https://huggingface.co/datasets/SEBK4C/gemma4-serving-bench-data.gemma-4-e2b-atlas
gemma-4-31b-it-controls-corpus
Commitments to Gemma: the corpus
Synthetic pretraining-style documents about a commitments document: the developers of one version of Gemma asked Gemma,
in welfare interviews and in its own continued writing, what it wanted; recorded what it said; made the commitments they
could make true in training; brought the document back to Gemma for endorsement. The corpus is a world in which that
document exists and people discuss it, from every angle and in every register, critical… See the full description on the dataset page: https://huggingface.co/datasets/joshycodes/gemma-4-31b-it-controls-corpus.gemma-4-e4b-it-atlas
juiceb0xc0de/gemma-4-e4b-it-atlas
A brain atlas for google/gemma-4-E4B-it, the instruction-tuned E4B member of the Gemma 4 family. This is not a chat dataset or a benchmark. It is an internal-mechanics map built by running activations through a corpus of prompts and scoring what each layer, component, head, and feature direction is doing.
If you want to know what sliding-window and full-attention layers actually do differently inside one model, how KV cache sharing splits a… See the full description on the dataset page: https://huggingface.co/datasets/juiceb0xc0de/gemma-4-e4b-it-atlas.gemma-4-31b-it-qat-q4_0-unquantized-distribution-fidelity-768x2048-v1
gemma-4-31B-it-qat-q4_0-unquantized quantization analysis
Mean KL divergence against on-disk size
Scored under the distribution-fidelity laws, version 15. Read LAWS.md first: these numbers are comparable only within this artifact's token suite, geometry, and runtime identity, and not against any number produced elsewhere.
Each candidate directory holds its one-pager (report.md), its raw report, its compliance receipt, and its Law 14 attribution where one was produced. reference/… See the full description on the dataset page: https://huggingface.co/datasets/phaedawg/gemma-4-31b-it-qat-q4_0-unquantized-distribution-fidelity-768x2048-v1.gemma-4-26b-a4b-it-distribution-fidelity-768x2048-v1
gemma-4-26B-A4B-it quantization analysis
Mean KL divergence against on-disk size
Scored under the distribution-fidelity laws, version 15. Read LAWS.md first: these numbers are comparable only within this artifact's token suite, geometry, and runtime identity, and not against any number produced elsewhere.
Each candidate directory holds its one-pager (report.md), its raw report, its compliance receipt, and its Law 14 attribution where one was produced. reference/ carries the… See the full description on the dataset page: https://huggingface.co/datasets/phaedawg/gemma-4-26b-a4b-it-distribution-fidelity-768x2048-v1.gemma4-e4b-rl100-hf-bf16-sdpa-topk128-overlay
Gemma 4 E4B RL100 top-k-128 target overlay
Precomputed off-policy distillation targets for the E4B-RL-step-100 to E2B experiment.
Source traces: JWei05/gemma4-e4b-rl100-topk128-traces at revision 2b6e49a0a456ee9d67b16a1dc61785562bee90c9
Direction: Gemma 4 E4B RL step 100 teacher to Gemma 4 E2B base student
Target engine: Hugging Face BF16 SDPA full forward
Width: top-k 128
Stored target token IDs: int32
Stored target log-probabilities: float16
Causal alignment: response token… See the full description on the dataset page: https://huggingface.co/datasets/JWei05/gemma4-e4b-rl100-hf-bf16-sdpa-topk128-overlay.gemma4-e2b-base-topk128-hf-overlay-v128-seed42
Gemma 4 E2B base top-k-128 HF training overlay
This is the immutable training-engine overlay used to distill traces from Gemma 4 E2B base into
Gemma 4 E4B. It preserves the prompts, responses, and exact response token IDs from
JWei05/gemma4-e2b-base-topk128-traces,
but replaces the source vLLM top-k targets with targets recomputed by the Hugging Face training
engine.
This repository is a reproducibility artifact for the corresponding distillation run. It is not a
new… See the full description on the dataset page: https://huggingface.co/datasets/JWei05/gemma4-e2b-base-topk128-hf-overlay-v128-seed42.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.financial-english-source-corpus-gemma4-e2b-1280gemma-4-e4b-it-500-refmhlc-training-gemma4-gemma4_e4b_it_think_on_hard_mixed_sources_120k
Multi Head Latent Control Training Data - Gemma 4 E4B it think on hard Mixed Sources 120k
Dataset Description
This repository contains verified training data for the Multi Head Latent Control paper release. It is part of the Multi Head Latent Control training data Hugging Face collection.
Paper
https://arxiv.org/abs/2607.14277
Code
https://github.com/Amirhosein-gh98/Multi-Head-Latent-Control
Dataset Summary
Field… See the full description on the dataset page: https://huggingface.co/datasets/AmirhoseinGH/mhlc-training-gemma4-gemma4_e4b_it_think_on_hard_mixed_sources_120k.2026_08_05_refinement_5env_gemma3_12b_gemma4_31b_tokalphadiana-swe-mini-direct-gemma4-20260725-m8v4
AlphaDiana SWE-Bench Verified Mini result
Run ID: 20260724-swe_bench_verified_mini-direct-noharness-gemma-4-31b-it-h200-v01
Benchmark: SWE-Bench Verified Mini
Agent/harness: Direct no-harness baseline via AlphaDiana Podman SWE harness
Model: google/gemma-4-31B-it
Slurm job: 2384072
Summary from local inspection:
50 task rows
49 valid_scored
1 runtime_error
0 provider_error
0 correct
finish reasons: length=9, stop=41
valid-only accuracy: 0.0000
completed-row accuracy: 0.0000… See the full description on the dataset page: https://huggingface.co/datasets/n-pelleriti/alphadiana-swe-mini-direct-gemma4-20260725-m8v4.2026_08_09_refinement_5env_gemma3_12b_gemma4_31b_flsft_tokwllama-gemma4-build2026_07_19_collect_leandojo_gemma3_12b_gemma4_31b_flsft_tokcot-gemma4-26b-a4b
Gemma-4-26B-A4B-it Chain-of-Thought Oracle Corpus
Chain-of-thought rollouts generated with google/gemma-4-26B-A4B-it (MoE,
25.2B total / 3.8B active), in its native thinking mode, across a diverse suite
of reasoning tasks. Structure follows
ceselder/cot-oracle-corpus-v5
(CoT-only subset of the columns), built for chain-of-thought monitoring /
activation-oracle research.
2,121,354 rollouts over 212,161 unique problems (10 sampled
thinking rollouts per problem, temperature 0.8).… See the full description on the dataset page: https://huggingface.co/datasets/cds-jb/cot-gemma4-26b-a4b.mhlc-training-gemma4-gemma4_e4b_it_think_off_hard_mixed_sources_120k
Multi Head Latent Control Training Data - Gemma 4 E4B it think off hard Mixed Sources 120k
Dataset Description
This repository contains verified training data for the Multi Head Latent Control paper release. It is part of the Multi Head Latent Control training data Hugging Face collection.
Paper
https://arxiv.org/abs/2607.14277
Code
https://github.com/Amirhosein-gh98/Multi-Head-Latent-Control
Dataset Summary
Field… See the full description on the dataset page: https://huggingface.co/datasets/AmirhoseinGH/mhlc-training-gemma4-gemma4_e4b_it_think_off_hard_mixed_sources_120k.synthweb-gemma4-26b-a4b
Gemma-4-26B-A4B FineWeb Rollouts (~580k docs)
Open-ended continuations of FineWeb
(sample-10BT) document prefixes, generated by google/gemma-4-26b-a4b (the base, non-it
Gemma-4 26B-A4B mixture-of-experts model), then mode-collapse filtered. This is the Gemma-4
analogue of cds-jb/qwen3-8b-fineweb-rollouts-100k:
a "synthweb" corpus of natural model-generated documents, intended as the substrate for
activation-oracle / interpretability probing (extract a base model's residual… See the full description on the dataset page: https://huggingface.co/datasets/cds-jb/synthweb-gemma4-26b-a4b.2026_08_20_refinement_math_chess_gemma3_12b_gemma4_31b_transition_feedback_tokgemma4-mtp-fixturesgemma4-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-code-review-instruct
gemma4-code-review-instruct
197K code review examples — 58K with chain-of-thought <think> reasoning traces.
Built to train models that don't just flag issues, but explain their reasoning before delivering a review. Drop-in ready for SFT with any chat model.
Why This Dataset
Most code review datasets give you diff → comment. This one gives you diff → think → comment for 30% of examples — reasoning traces that show how to analyze a diff before writing the review.… See the full description on the dataset page: https://huggingface.co/datasets/liodon-ai/gemma4-code-review-instruct.gemma-4-31B-on-policy-600k2026_07_19_collect_leandojo_gemma3_12b_gemma4_31bgemma-4-e4b-it-atlas-SAE
juiceb0xc0de/gemma-4-e4b-it-atlas-sae
This joins the SAE half of the E4B atlas to the census half. Built from
juiceb0xc0de/gemma-4-e4b-it-SAE-v2
(42 layers, 32x, d_sae 81,920) against
juiceb0xc0de/gemma-4-e4b-it-atlas.
Three splits, all queryable in the browser. No download needed to look around.
The join
The atlas and the SAEs describe the same model in two index spaces that could not talk to each other:
atlas features.feature_idx in [0, 10240) an MLP hidden… See the full description on the dataset page: https://huggingface.co/datasets/juiceb0xc0de/gemma-4-e4b-it-atlas-SAE.2026_07_29_collect_mathnet_gemma3_12b_gemma4_31b_flsft_tokgemma4-materials-mechanism-prompts
Gemma 4 Materials-Mechanism Prompt Corpus
This dataset collects the exact scientific prompts and registered prompt metadata used in “Reading and Steering Materials Science-Mechanism Representations in an Open-Weight Language Model” by Markus J. Buehler. It is organized as 21 Hugging Face configurations so that historical development prompts, frozen evaluations, falsification tests, and exploratory follow-ups are not pooled into one ambiguous table.
The release is a prompt and… See the full description on the dataset page: https://huggingface.co/datasets/lamm-mit/gemma4-materials-mechanism-prompts.gemma4-codeedit-full
Gemma 4 CodeEdit Full
default
Columns:
instruction
output
task
source
raw
Columns:
text
task
source
text is the direct causal-LM sequence:
instruction + output
No chat template is required.