datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
CoDIT-Gemma3
Dataset Name
🤖 Teacher Model
📂 Dataset Link
CoDIT-Gemma3 💎
google/gemma-3-27b-it
CoDIT-Gemma3 ↗
CoDIT-Qwen3-8B 🐉
Qwen/Qwen3-8B
CoDIT-Qwen3-8B ↗
CoDIT-Qwen3-30B 🚀
Qwen/Qwen3-30B-A3B
CoDIT-Qwen3-30B ↗
CoDIT-Gemma3
CoDIT-Gemma3 is a synthetic conversation dataset derived from LMSYS-Chat-1M [Zhang+, ICLR24].
250,333 user instructions sourced from LMSYS-Chat-1M
250,333 assistant responses automatically synthesized using CoDIT with google/gemma-3-27b-it, generating… See the full description on the dataset page: https://huggingface.co/datasets/Tatsuya-Ichinose/CoDIT-Gemma3.gemma3n-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_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.twinkle-dialogue-gemma3-2025-08
Twinkle Dialogue (Gemma-3-12B-it, 2025-08)
本資料集由 Gemma-3-12B-it（Twinkle AI 社群服務） 生成之對話資料，採用 OpenAI Chat Messages 格式（.jsonl），並整合：
Reference-free（由 seed 派生單輪問答）
Reference-based（依據參考文本生成單輪問答）
檔案路徑：data/train.jsonl（選配：data/train.parquet）
結構說明
每列為一筆樣本：{"id": "...", "type": "...", "messages": [{"role":"system","content":"..."}, ...]}
訓練時可擷取第一個 user 與對應 assistant 形成 (instruction, response) pair，或直接使用 chat 格式的 trainer。
來源與限制… See the full description on the dataset page: https://huggingface.co/datasets/tw-llama/twinkle-dialogue-gemma3-2025-08.alpaca-polish-gemma3-translation
🦙 Alpaca Dataset: Polish Translation 🇵🇱
This repository provides a Polish translation of the Stanford Alpaca dataset, a popular instruction-following dataset derived from OpenAI’s text-davinci-003 outputs.It also includes the scripts used to perform the translation, which may be helpful for anyone translating similar datasets or building datasets based on LLM outputs.
Overview
The dataset was translated from English to Polish using Gemma 3 12B, running locally in… See the full description on the dataset page: https://huggingface.co/datasets/grappeq/alpaca-polish-gemma3-translation.ifc-bim-gemma3-subset-1k
IFC-BIM Gemma3 Training Subset (1K Examples)
A 1,000-example subset of IFC/BIM Q&A data formatted for Gemma-3 fine-tuning with Unsloth.
Quick Start
from datasets import load_dataset
# Load dataset
dataset = load_dataset("your-username/ifc-bim-gemma3-subset-1k")
# View first example
print(dataset["train"][0])
Dataset Structure
ShareGPT format with quality scores:
conversations: List of human/gpt exchanges
source: Data origin
score: Quality rating… See the full description on the dataset page: https://huggingface.co/datasets/Dietmar2020/ifc-bim-gemma3-subset-1k.gemma3-reasoning-dropin-context
Gemma3 Reasoning Drop-in (Context Preserved)
Drop-in dataset with task/input/expected_output where input includes prior turns so follow-up replies remain coherent.
Use:
from datasets import load_dataset
dataset = load_dataset("Cyleux/gemma3-reasoning-dropin-context", split="train[:10000]")
Stats:
{
"input": "data/functiongemma_upload/train.jsonl",
"output": "data/gemma3_reasoning_dropin_context/train.jsonl",
"rows": 2225,
"samples_total": 1214,
"rows_written": 2225… See the full description on the dataset page: https://huggingface.co/datasets/Cyleux/gemma3-reasoning-dropin-context.DAPO-Gemma3-27B-PT-RL-step40-seed43-SFT-Data-32k-n4
DAPO-Gemma3-27B-PT-RL-step40-seed43-SFT-Data-32k-n4
Teacher-generated SFT/distillation data for Gemma 3 math distillation.
Source
Teacher: JWei05/dapo-gemma3-27b-pt-from-step40-seed43, subfolder step_000040
Prompts: JWei05/DAPO-OpenMathInstruct2-34k, train split
Rows: 128,000
Unique prompts: 32,000
Responses per prompt: 4
Sampling: temperature=1.0, top_p=1.0, top_k=-1, max_tokens=20480
Columns
Column
Description
messages
User prompt and teacher… See the full description on the dataset page: https://huggingface.co/datasets/JWei05/DAPO-Gemma3-27B-PT-RL-step40-seed43-SFT-Data-32k-n4.DAPO-Gemma3-27B-IT-RL-SFT-Data-correct
DAPO-Gemma3-27B-IT-RL-SFT-Data-correct
Filtered subset of
JWei05/DAPO-Gemma3-27B-IT-RL-SFT-Data:
only the teacher responses whose final answer is math_verify-correct against
the original DAPO-Math-17k ground truth.
Stats
Source rows: 69,592 (17,398 prompts × 4 teacher responses)
Kept rows: 41,831 (60.1%)
Prompts with ≥1 correct response: 13,062 / 17,398 (75.1%)
Prompts with 4/4 correct responses: 7,492 (43.1%)
Scoring
Same function as used during RL… See the full description on the dataset page: https://huggingface.co/datasets/JWei05/DAPO-Gemma3-27B-IT-RL-SFT-Data-correct.DAPO-Gemma3-27B-PT-RL-step40-seed43-SFT-Data-all33296-n4
DAPO-Gemma3-27B-PT-RL-step40-seed43-SFT-Data-all33296-n4
Teacher-generated SFT/distillation data for Gemma 3 math distillation.
Source
Teacher: JWei05/dapo-gemma3-27b-pt-from-step40-seed43, subfolder step_000040
Prompts: JWei05/DAPO-OpenMathInstruct2-34k, train split
Rows: 133,184
Unique prompts: 33,296
Responses per prompt: 4
Sampling: temperature=1.0, top_p=1.0, top_k=-1, max_tokens=20480
Columns
Column
Description
messages
User prompt and… See the full description on the dataset page: https://huggingface.co/datasets/JWei05/DAPO-Gemma3-27B-PT-RL-step40-seed43-SFT-Data-all33296-n4.DAPO-Gemma3-27B-PT-RL-step40-seed43-SFT-Data
DAPO-Gemma3-27B-PT-RL-step40-seed43-SFT-Data
Teacher-generated SFT/distillation data for Gemma 3 math distillation.
Source
Teacher: JWei05/dapo-gemma3-27b-pt-from-step40-seed43, subfolder step_000040
Prompts: JWei05/DAPO-OpenMathInstruct2-34k, train split
Rows: 66,592
Unique prompts: 33,296
Responses per prompt: 2
Sampling: temperature=1.0, top_p=1.0, top_k=-1, max_tokens=20480
Columns
Column
Description
messages
User prompt and teacher assistant… See the full description on the dataset page: https://huggingface.co/datasets/JWei05/DAPO-Gemma3-27B-PT-RL-step40-seed43-SFT-Data.gemma-3-27b-it_writingbench-en100
google/gemma-3-27b-it — writingbench-en100
Model outputs from the micro-creativity inference suite.
Model: google/gemma-3-27b-it
Dataset: writingbench-en100 (100 items)
Part of collection: ZachW/llm-creativity-benchmarks
Generation config
temperature: 0.0
max_tokens: 8192
seed: 42
backend: vllm
Columns
Column
Description
task_id
Unique task identifier
input
The exact prompt sent to the model (after meta-prompt application)… See the full description on the dataset page: https://huggingface.co/datasets/ZachW/gemma-3-27b-it_writingbench-en100.gemma3n-conversational-reasoning-with-tools
Gemma3N Conversational Reasoning With Embedded Tool Traces
Prepared for Unsloth Gemma3/Gemma3N conversational notebooks that expect ShareGPT conversations.
Multi-turn conversations are preserved.
Reasoning blocks (<think>...</think>) are preserved.
Tool call traces are preserved by embedding them in assistant text as tags:
<tool_call ...>...</tool_call>
<tool_response ...>...</tool_response>
Use:
from datasets import load_dataset
from unsloth.chat_templates import… See the full description on the dataset page: https://huggingface.co/datasets/Cyleux/gemma3n-conversational-reasoning-with-tools.DAPO-Gemma3-12B-PT-RL-step20-seed43-SFT-Data-all33296-n4
DAPO-Gemma3-12B-PT-RL-step20-seed43-SFT-Data-all33296-n4
Teacher-generated SFT/distillation data for Gemma 3 math distillation.
Source
Teacher: JWei05/dapo-gemma3-12b-pt-from-step60-seed43, subfolder step_000020
Prompts: JWei05/DAPO-OpenMathInstruct2-34k, train split
Rows: 133,184
Unique prompts: 33,296
Responses per prompt: 4
Sampling: temperature=1.0, top_p=1.0, top_k=-1, max_tokens=20480
Columns
Column
Description
messages
User prompt and… See the full description on the dataset page: https://huggingface.co/datasets/JWei05/DAPO-Gemma3-12B-PT-RL-step20-seed43-SFT-Data-all33296-n4.DAPO-Gemma3-12B-PT-RL-step20-seed43-SFT-Data
DAPO-Gemma3-12B-PT-RL-step20-seed43-SFT-Data
Teacher-generated SFT/distillation data for Gemma 3 math distillation.
Source
Teacher: JWei05/dapo-gemma3-12b-pt-from-step60-seed43, subfolder step_000020
Prompts: JWei05/DAPO-OpenMathInstruct2-34k, train split
Rows: 66,592
Unique prompts: 33,296
Responses per prompt: 2
Sampling: temperature=1.0, top_p=1.0, top_k=-1, max_tokens=20480
Columns
Column
Description
messages
User prompt and teacher assistant… See the full description on the dataset page: https://huggingface.co/datasets/JWei05/DAPO-Gemma3-12B-PT-RL-step20-seed43-SFT-Data.gemma-3-12b-it-lmsys-onpolicy-rollouts
On-policy chat rollouts: google/gemma-3-12b-it on LMSYS-Chat-1M prompts
Each row is a first-user-turn prompt sampled from
lmsys/lmsys-chat-1m and a
response generated on-policy by google/gemma-3-12b-it with vLLM (do_sample,
temperature 0.7, top_p 1.0, max_new_tokens 768, seed 42). 24,991 rows. Built to match
GemmaScope 2's instruction-tuned SAE training distribution (real model rollouts) for a
short KL+MSE ("end-to-end") finetune of the released GemmaScope-2 residual SAE.… See the full description on the dataset page: https://huggingface.co/datasets/iarcuschin/gemma-3-12b-it-lmsys-onpolicy-rollouts.DAPO-Gemma3-1B-PT-DAPO-17.4k
DAPO-Gemma3-1B-PT-DAPO-17.4k
Traces sampled from google/gemma-3-1b-pt on the DAPO-Math-17k train + 100-question val splits,
using the SAME unified few-shot chat prompt and sampling (temp 1.0, top_p 1.0, top_k -1, 20k max,
single BOS) as RL training. 16 samples per question. Splits: train (17,198 q), validation (100 q).
Columns: prompt_text, response_text, prompt_token_ids, response_token_ids, input_ids, response_mask,
teacher_log_probs, prompt_idx (shared across a question's 16… See the full description on the dataset page: https://huggingface.co/datasets/JWei05/DAPO-Gemma3-1B-PT-DAPO-17.4k.gemma-3n-4b-distill-smollm2-360m-instruct-425xTrace of Gemma 3n 4B Distill SmolLM2 360M Instruct LLM by sapbot (me).
Data count (Total: 425):
English - 209
Russian - 216
Data is presented in ShareGPT format and each conversation split by newline.
Note: This was added more as a "examples" of this model's outputs. Of course you will not distill a distilled model (I hope).
Brought to you by sapbot from Romarchive
IFEval-gemma3-chat
Dataset Card for Dataset Name
This dataset is a subset of google/IFEval, selected by the token length of applying chat template of google/gemma-3-4b-it.
Dataset Details
Dataset Description
Curated by: jaxon3062
Language(s) (NLP): en
License: Apache 2.0 Licence
Dataset Sources [optional]
Repository: google/IFEval
Paper [optional]: Instruction-Following Evaluation for Large Language Models
Uses
Direct Use
This can… See the full description on the dataset page: https://huggingface.co/datasets/jaxon3062/IFEval-gemma3-chat.gemma3n-conversational-reasoning-toolloop
Gemma3N Conversational Reasoning Tool-Loop
Gemma3N conversational dataset that preserves tool traces while avoiding training targets on tool responses.
Encoding:
Assistant emits tool calls: <tool_call ...>...</tool_call>
Tool outputs are user-side turns: <tool_response ...>...</tool_response>
This works with train_on_responses_only because user-side tool responses are masked from loss.
Use:
from datasets import load_dataset
from unsloth.chat_templates import… See the full description on the dataset page: https://huggingface.co/datasets/Cyleux/gemma3n-conversational-reasoning-toolloop.twinkle-dialogue-gemma3-2025-08
Twinkle Dialogue (Gemma-3-12B-it, 2025-08)
本資料集由 Gemma-3-12B-it（Twinkle AI 社群服務） 生成之對話資料，採用 OpenAI Chat Messages 格式（.jsonl），並整合：
Reference-free（由 seed 派生單輪問答）
Reference-based（依據參考文本生成單輪問答）
檔案路徑：data/train.jsonl（選配：data/train.parquet）
結構說明
每列為一筆樣本：{"id": "...", "type": "...", "messages": [{"role":"system","content":"..."}, ...]}
訓練時可擷取第一個 user 與對應 assistant 形成 (instruction, response) pair，或直接使用 chat 格式的 trainer。
來源與限制… See the full description on the dataset page: https://huggingface.co/datasets/Ethan615/twinkle-dialogue-gemma3-2025-08.twinkle-dialogue-gemma3-2025-08
Twinkle Dialogue (Gemma-3-12B-it, 2025-08)
本資料集由 Gemma-3-12B-it（Twinkle AI 社群服務） 生成之對話資料，採用 OpenAI Chat Messages 格式（.jsonl），並整合：
Reference-free（由 seed 派生單輪問答）
Reference-based（依據參考文本生成單輪問答）
檔案路徑：data/train.jsonl（選配：data/train.parquet）
結構說明
每列為一筆樣本：{"id": "...", "type": "...", "messages": [{"role":"system","content":"..."}, ...]}
訓練時可擷取第一個 user 與對應 assistant 形成 (instruction, response) pair，或直接使用 chat 格式的 trainer。
來源與限制… See the full description on the dataset page: https://huggingface.co/datasets/allenlin316/twinkle-dialogue-gemma3-2025-08.gemma-3-1b-pt-blind-spots
Gemma-3-1b-pt Blind Spots Dataset
Dataset Description
This dataset documents blind spots (systematic errors) found in
google/gemma-3-1b-pt,
a 1-billion-parameter pretrained base model (not instruction-tuned)
released by Google in March 2025 as part of the Gemma 3 family.
Each row contains:
Column
Description
id
Unique probe index
category
Type of reasoning tested
prompt
The input fed to the model (text-completion style)
expected_output
The… See the full description on the dataset page: https://huggingface.co/datasets/Junaid687/gemma-3-1b-pt-blind-spots.gemma-3-27b-it_creativemath-with-answers
google/gemma-3-27b-it — creativemath-with-answers
Model outputs from the micro-creativity inference suite.
Model: google/gemma-3-27b-it
Dataset: creativemath-with-answers (188 items)
Part of collection: ZachW/llm-creativity-benchmarks
Generation config
temperature: 0.0
max_tokens: 32768
seed: 42
backend: vllm
Columns
Column
Description
task_id
Unique task identifier
input
The exact prompt sent to the model (after meta-prompt… See the full description on the dataset page: https://huggingface.co/datasets/ZachW/gemma-3-27b-it_creativemath-with-answers.DAPO-Gemma3-27B-IT-RL-SFT-Data
DAPO-Gemma3-27B-IT-RL-SFT-Data
Teacher-generated SFT/distillation dataset. Responses + per-token log probabilities
from a DAPO-RL-trained Gemma 3 27B teacher on the DAPO-Math-17k prompt set.
Source
Teacher: JWei05/dapo-gemma3-27b-it,
step_000040 — Gemma 3 27B IT after RL training with DAPO on math.
Prompts: BytedTsinghua-SIA/DAPO-Math-17k
(17,391 math problems).
Responses per prompt: 4.
Sampling: temperature=1.0, top_p=1.0, max_tokens=20480.
Columns… See the full description on the dataset page: https://huggingface.co/datasets/JWei05/DAPO-Gemma3-27B-IT-RL-SFT-Data.gemma-3-27b-it_arena-hard-creative-writing
google/gemma-3-27b-it — arena-hard-creative-writing
Model outputs from the micro-creativity inference suite.
Model: google/gemma-3-27b-it
Dataset: arena-hard-creative-writing (250 items)
Part of collection: ZachW/llm-creativity-benchmarks
Generation config
temperature: 0.0
max_tokens: 16384
seed: 42
backend: vllm
Columns
Column
Description
task_id
Unique task identifier
input
The exact prompt sent to the model (after… See the full description on the dataset page: https://huggingface.co/datasets/ZachW/gemma-3-27b-it_arena-hard-creative-writing.gemma-3-27b-it_tinystories-val1pct-raw
google/gemma-3-27b-it — tinystories-val1pct-raw
Model outputs from the micro-creativity inference suite.
Model: google/gemma-3-27b-it
Dataset: tinystories-val1pct-raw (220 items)
Part of collection: ZachW/llm-creativity-benchmarks
Generation config
temperature: 0.0
max_tokens: 16384
seed: 42
backend: vllm
Columns
Column
Description
task_id
Unique task identifier
input
The exact prompt sent to the model (after meta-prompt… See the full description on the dataset page: https://huggingface.co/datasets/ZachW/gemma-3-27b-it_tinystories-val1pct-raw.gemma-3-12b-it-407xTrace of Gemma 3 12B LLM.
Data count (Total: 407):
English - 198
Russian - 209
Data is presented in {"messages":[{"role":"user", "content":"Prompt"}, {"role":"assistant", "content": "Response"}]} format and each conversation split by newline.
gemma-3-1b-pt-blind-spots
Blind Spots of google/gemma-3-1b-pt
Model Tested
Model: google/gemma-3-1b-ptParameters: 1BType: Pre-trained base language model (not instruction-tuned)Tested by: Toka-Tarek | Biotechnology graduate & Pharmacogenetics Lab Specialist
How I Loaded the Model
Tested on Google Colab (free T4 GPU, 16GB VRAM).
Note: torch.float16 caused numerical instability (NaN/inf errors)
on the T4 GPU, so torch.float32 was used instead for stable generation.
from huggingface_hub… See the full description on the dataset page: https://huggingface.co/datasets/Toka-Tarek/gemma-3-1b-pt-blind-spots.gemma-3-27b-it_storygen-prompts-200
google/gemma-3-27b-it — storygen-prompts-200
Model outputs from the micro-creativity inference suite.
Model: google/gemma-3-27b-it
Dataset: storygen-prompts-200 (200 items)
Part of collection: ZachW/llm-creativity-benchmarks
Generation config
temperature: 0.0
max_tokens: 16384
seed: 42
backend: vllm
Columns
Column
Description
task_id
Unique task identifier
input
The exact prompt sent to the model (after meta-prompt… See the full description on the dataset page: https://huggingface.co/datasets/ZachW/gemma-3-27b-it_storygen-prompts-200.