datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.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.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.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
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.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.
gemma3-smsgemma3-instruct-reasoning-mix
Dataset Card for gemma-cot-multitask-v1
This dataset contains synthetic instruction-following and reasoning samples generated using Google AI Studio API. It is designed to fine-tune language models (specifically Gemma 2/3) to follow instructions with structured Chain-of-Thought (CoT) reasoning.
Example Data Structure
{
"text": "<start_of_turn>user\nDesign a database schema...\n<start_of_turn>model\n<reasoning>\n1. Entities: Books, Authors...\n2.… See the full description on the dataset page: https://huggingface.co/datasets/Phonsiri/gemma3-instruct-reasoning-mix.gemma-3n-4b-it-423xTrace of Gemma 3n 4B LLM by Google.
Data count (Total: 423):
English - 207
Russian - 216
Data is presented in ShareGPT format and each conversation split by newline.
Brought to you by sapbot from Romarchive
gemma-3-4b-it-420xTrace of Gemma 3 4B LLM by Google.
Data count (Total: 420):
English - 204
Russian - 216
Data is presented in ChatML format and each conversation split by newline.
Brought to you by sapbot from Romarchive
gemma3-12b-baseline-pool
Gemma-3-12B unsteered baseline pool
20,000 unsteered (alpha=0) greedy completions from
google/gemma-3-12b-it
(revision main), one per prompt of a frozen instruction pool, each
scored by four lexicon-based concept detectors. Built as the baseline reference for an
activation-steering competition: steered submissions are compared against these
per-prompt, per-concept baseline scores.
Schema
field
type
description
id
int
stable prompt id within the frozen… See the full description on the dataset page: https://huggingface.co/datasets/AureliusAligned/gemma3-12b-baseline-pool.