Datasets:

JustACluelessKidAtSchool
/

tiny-slm-pretraining-corpus

license: apache-2.0
task_categories:
  - text-generation
  - question-answering
language:
  - en
  - code
tags:
  - pretraining
  - tiny-slm
  - smollm
  - unsloth
  - reasoning
  - fineweb-edu
  - finemath
  - cosmopedia
  - tinystories
  - wikipedia
  - dclm
size_categories:
  - 10M<n<100M
configs:
  - config_name: default
    data_files:
      - split: train
        path: train/*.parquet
      - split: validation
        path: validation/*.parquet

🚀 Ultra High-Quality Tiny SLM Pre-Training Corpus (<100GB)

A state-of-the-art, balanced 7-domain pre-training dataset engineered specifically for Small Language Models (Tiny SLMs: 50M – 2B parameters) such as SmolLM2, SmolLM3, MobileLLM, Llama 3.2 1B, and custom architectures.

100% compatible with Unsloth Studio, Unsloth AI, Hugging Face datasets, and PyTorch DataLoaders.

📊 Dataset Statistics

Total Documents: 20,066,075
- Train: 19,663,898
- Validation: 402,177
Total Tokens: ~23.87 Billion tokens
- Train Tokens: ~23.40B
- Validation Tokens: ~0.47B
Format: Zstandard Compressed Parquet

🔬 7-Domain Pre-Training Mixture

Domain	Source Repository	Description & Quality Filter	Token Share
Synthetic Textbooks	`HuggingFaceTB/smollm-corpus`	`cosmopedia-v2` textbooks and topic deep-dives	28.0%
Educational Web	`HuggingFaceFW/fineweb-edu`	Sample 10BT (score >= 2.8 filter)	23.5%
Step-by-Step Math	`HuggingFaceTB/finemath`	`finemath-4plus` & `infiwebmath-4plus` (score 4+)	19.5%
Clean Polyglot Code	`codeparrot/github-code-clean`	Python, Rust, C++, C, Go, JS, TS, Java, SQL, Shell	12.5%
General Knowledge	`wikimedia/wikipedia` + `mlfoundations/dclm-baseline-1.0-parquet`	English Wikipedia (dense factual) & DCLM web corpus	7.8%
Reasoning & Tool Traces	`HuggingFaceTB/smoltalk2`	OpenThoughts3, Nemotron R1, and agentic tool traces	6.5%
Stories & Narrative	`roneneldan/TinyStories`	Synthetic coherent narrative, dialogue, & vocabulary	2.1%

🦥 Quickstart: Unsloth Studio & Hugging Face

1. Load with Hugging Face Datasets

from datasets import load_dataset

# Load Train Split
train_dataset = load_dataset("JustACluelessKidAtSchool/tiny-slm-pretraining-corpus", split="train")

# Load Validation Split
val_dataset = load_dataset("JustACluelessKidAtSchool/tiny-slm-pretraining-corpus", split="validation")

2. Pre-Train with Unsloth in 1 Command

from unsloth import FastLanguageModel
from datasets import load_dataset
from transformers import TrainingArguments
from trl import SFTTrainer

# Load Tiny Model (e.g. SmolLM2-135M or Llama-3.2-1B)
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="HuggingFaceTB/SmolLM2-135M",
    max_seq_length=2048,
    load_in_4bit=False,
)

# Load Dataset from Hub
dataset = load_dataset("JustACluelessKidAtSchool/tiny-slm-pretraining-corpus", split="train")

# Train with Unsloth
trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=dataset,
    dataset_text_field="text",
    max_seq_length=2048,
    args=TrainingArguments(
        output_dir="./tiny_slm_output",
        per_device_train_batch_size=4,
        gradient_accumulation_steps=8,
        max_steps=5000,
        learning_rate=5e-4,
        fp16=True,
        optim="adamw_8bit",
    ),
)
trainer.train()

3. In Unsloth Studio GUI

Set Dataset to: JustACluelessKidAtSchool/tiny-slm-pretraining-corpus
Text Column: text