datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
tiny-supervised-datasettinystories-icr-data-v2tiny_qa_benchmark_pp
Tiny QA Benchmark++ (TQB++)
Tiny QA Benchmark++ (TQB++) is an ultra-lightweight evaluation suite designed to expose critical failures in Large Language Model (LLM) systems within seconds. It serves as the LLM analogue of software unit tests, ideal for rapid CI/CD checks, prompt engineering, and continuous quality assurance in modern LLMOps.
This Hugging Face dataset repository hosts the core English dataset and various synthetically generated multilingual and topical dataset packs… See the full description on the dataset page: https://huggingface.co/datasets/vincentkoc/tiny_qa_benchmark_pp.TinyStoriesV2Data is from https://huggingface.co/datasets/roneneldan/TinyStories/:
TinyStoriesV2-GPT4-train.txt - Is a new version of the dataset that is based on generations by GPT-4 only (the original dataset also has generations by GPT-3.5 which are of lesser quality). It contains all the examples in TinyStories.txt which were GPT-4 generated as a subset (but is significantly larger).
This dataset was used to train https://github.com/noanabeshima/tiny_model/.
The data was preprocessed with:
from… See the full description on the dataset page: https://huggingface.co/datasets/noanabeshima/TinyStoriesV2.glm-moe-dsa-tiny-cpu-repro-v1
Tiny GLM MoE DSA: two CPU captures, forced zero-KL replay
Reproducibility evidence for
malaiwah/glm-moe-dsa-tiny-random-bf16,
checkpoint/config/tokenizer revision 45563636ef723acfb826755493447dc40c7a0c37.
This is a synthetic pipeline test, not a quality benchmark, quantization measurement,
qualified production reference, or registry submission. The model is random-init.
No GPU or paid cloud job was used.
Observed result
Two fresh capture processes, two CPU… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm-moe-dsa-tiny-cpu-repro-v1.TinyTextThe entire NanoPhi Dataset is at train.jsonl
Separate Tasks Include
Math (Metamath, mammoth)
Code (Code Search Net)
Logic (Open-platypus)
Roleplay (PIPPA, RoleplayIO)
Textbooks (Tiny-text, Sciphi)
Textbook QA (Orca-text, Tiny-webtext)
tinystories_dataset_arabicTinyStories-Multilingual
Novelist: TinyStories Multilingual Edition
Dataset Summary
The TinyStories Multilingual Edition is a high-fidelity synthetic dataset of short, child-safe fiction designed to stress-test literary consistency, emotional warmth, and multilingual fluency in small models. Derived from the broader Novelist ecosystem, this subset focuses on narrative simplicity paired with complex moral and social themes.
The dataset contains 15,688 high-quality stories across 28 languages. Each… See the full description on the dataset page: https://huggingface.co/datasets/Dxniz/TinyStories-Multilingual.TinyBooks-QA-Chinese本数据集已停止更新，请移步https://huggingface.co/datasets/fzmnm/TinyStoriesAdv-zh
TinyBooks-QA-Chinese
Inspired by the (TinyStories)[https://arxiv.org/abs/2305.07759] paper, where a small language model exhibits strong capabilities when trained on high-quality, 🍼baby-friendly stories synthesized by AI, I present an AI-generated Encyclopedia suitable for kindergarten and grade school levels.
This AI-synthesized dataset converts classical literature into a question-answer style curriculum with… See the full description on the dataset page: https://huggingface.co/datasets/fzmnm/TinyBooks-QA-Chinese.tiny-random-model-summaryTiny_Theory_of_Mind
Tiny Theory of Mind
Tiny Theory of Mind is our first attempt at evaluating theory of mind capabilities in small language models. The benchmark covers a wide variety of ToM topics, ranging in difficulties that, for humans, would be appropriate for Pre-K through 6th grade.
The benchmark is designed primarily for base-model continuation log-likelihood scoring. It does not require instruction following, chain-of-thought, or generated explanations. Random-choice accuracy is 25%.… See the full description on the dataset page: https://huggingface.co/datasets/AxiomicLabs/Tiny_Theory_of_Mind.TinyMCQReasoningtinybrain-instruct-sft-200k
TinyBrain Instruct 200K
A 196k+ row English SFT dataset for training tiny instruction-following language models.
TinyBrain Instruct 200K is a synthetic supervised fine-tuning dataset made for small language models, especially models around 100M–500M parameters.
The dataset focuses on short, clear, learnable assistant responses across education, basic math reasoning, clean conversation, planning, simplification, simple coding, and honesty/uncertainty behavior.
Most… See the full description on the dataset page: https://huggingface.co/datasets/exnivo/tinybrain-instruct-sft-200k.mit67_swin_tiny-oof-featurestr-hi-mimi-encoded
TR↔HI Mimi-Encoded Parallel Speech
Pre-encoded parallel Turkish↔Hindi speech pairs for training speech-to-speech translation models. All audio has been tokenized through the Mimi neural audio codec (8 codebooks, 12.5 Hz, 24kHz) and stored as .pt files with word-level text alignments.
Dataset Summary
Source audio
~911 hours of synthetic parallel TR↔HI speech from tr-hi-parallel-speech-v2
TTS model
OmniVoice (voice design mode, 14 voice designs)… See the full description on the dataset page: https://huggingface.co/datasets/tiny-aya-translate/tr-hi-mimi-encoded.tinybrain-pretrain-corpus-2b
TinyBrain Pretrain Corpus 2B
A mixed-source English pretraining corpus for training small language models.
TinyBrain Pretrain Corpus 2B is a mixed-source dataset built for pretraining small causal language models, especially the TinyBrain-100M Base model.
The dataset combines educational text, factual/wiki-style text, math reasoning data, Python code-summary data, clean web text, and conversation-style data. It is designed to give small models a useful general foundation… See the full description on the dataset page: https://huggingface.co/datasets/exnivo/tinybrain-pretrain-corpus-2b.TinyEncyclopedias-Chinese本数据集已停止更新，请移步https://huggingface.co/datasets/fzmnm/TinyStoriesAdv-zh
TinyEncyclopediasChinese
Inspired by the papers (TinyStories)[https://arxiv.org/abs/2305.07759] and (Textbooks Are All You Need)[https://arxiv.org/abs/2306.11644], where a small language model exhibits strong capabilities when trained on high-quality, kid-friendly stories synthesized by AI, I present an AI-generated Encyclopedia suitable for kindergarten and grade school levels.
This dataset follows my previous… See the full description on the dataset page: https://huggingface.co/datasets/fzmnm/TinyEncyclopedias-Chinese.qwen3-5-tiny-fidelity-root-v1
qwen3_5 random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/qwen3-5-tiny-random-bf16.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it). Same cut… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/qwen3-5-tiny-fidelity-root-v1.qwen4-exp-tiny-fidelity-root-v1
qwen4_exp random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/qwen4-exp-tiny-random-bf16.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it). Same… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/qwen4-exp-tiny-fidelity-root-v1.deepseek-v4-tiny-fidelity-root-v1
deepseek-v4 random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/deepseek-v4-tiny-random-bf16.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it).… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/deepseek-v4-tiny-fidelity-root-v1.TinyChartDataqwen3-5-gguf-tiny-fidelity-root-v1
qwen35-gguf random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/qwen3-5-gguf-tiny-random-bf16.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it).… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/qwen3-5-gguf-tiny-fidelity-root-v1.Tiny-Open-Domain-BooksA tiny example dataset consisting of four books dedicated to the open domain in JSONL format:
Alice in Wonderland - Lewis Caroll
Dracula - Bram Stoker
The Wonderful Wizard of Oz - L. Frank Baum
The Count of Monte Cristo - Alexandre Dumas & Auguste Maquet
All works are open domain, thus this dataset is also dedicated to the open domain.
The dataset has been made to have extremely long context lengths, ideally as close to 2048 at possible without cutting off chunks in strange places. Each… See the full description on the dataset page: https://huggingface.co/datasets/Blackroot/Tiny-Open-Domain-Books.k2-horizon-tiny-fidelity-root-v1
k2-horizon random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/k2-horizon-tiny-random-bf16.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it).… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/k2-horizon-tiny-fidelity-root-v1.TinyPython
TinyPython Tasks
TinyPython is a synthetic Python dataset inspired by the idea behind TinyStories: if the data distribution is narrow, clean, and high quality, even very small language models can learn useful structure.
Instead of broad repository code or competitive-programming solutions, TinyPython focuses on short natural-language programming tasks paired with complete, typed, standalone Python functions.
The goal is to provide a compact instruction-to-code corpus for… See the full description on the dataset page: https://huggingface.co/datasets/BertilBraun/TinyPython.glm5-next-tiny-fidelity-root-v1
glm5_next random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/glm5-next-tiny-random-bf16.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it). Same… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm5-next-tiny-fidelity-root-v1.minimax-m2-tiny-fidelity-root-v1
minimax-m2 random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/minimax-m2-tiny-random-bf16.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it).… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/minimax-m2-tiny-fidelity-root-v1.TinyMathStories_gpt-oss-20b
TinyMathStories
A TinyStories-style corpus extended with math and lightweight reasoning.
This dataset keeps the child-level vocabulary and short narrative style of TinyStories (Microsoft Research, Eldan & Li, 2023) and mixes in basic numeracy (counting, addition/subtraction, simple equations, fractions, measurement) and short justifications—so tiny models can practice coherent English and early math/logic.
Research, generation, and curation by AlgoDriveAI.Inspired by and… See the full description on the dataset page: https://huggingface.co/datasets/AlgoDriveAI/TinyMathStories_gpt-oss-20b.kimi-k25-tiny-fidelity-root-v1
kimi-k25 random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/kimi-k25-tiny-random-bf16.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it). Same… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/kimi-k25-tiny-fidelity-root-v1.glm-moe-dsa-tiny-fidelity-root-v1
glm_moe_dsa random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/glm-moe-dsa-tiny-random-bf16.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it).… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm-moe-dsa-tiny-fidelity-root-v1.