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KabirBakhshaei/
TinyStories-GPT2-10k

Name: TinyStories-GPT2-10k
Availability: InStock

roneneldan/TinyStories

mit

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💡 TinyStories-GPT2-10k

TinyStories-GPT2-10k is a lightweight, decoder-only transformer model trained from scratch on a tokenized version of the TinyStories dataset. It uses a custom Byte Pair Encoding (BPE) tokenizer with a vocabulary size of 10,000 tokens, making it well-suited for experiments in efficient language modeling, scaling laws, and low-resource fine-tuning.

🧠 Model Architecture

This model follows the core GPT-2 architectural principles with a few simplifications to reduce parameter count and training cost.

Component	Value
Architecture	Decoder-only Transformer (GPT-like)
Layers	8
Embedding Size	128
Attention Heads	16
Feedforward Size	512 (4× expansion)
Sequence Length	1024
Vocabulary Size	10,000
Total Parameters	~2.99M
Dropout / Bias	None (disabled for simplicity)
Weight Tying	✅ Enabled (input/output embeddings)

Initialization

Weights were initialized with a normal distribution (𝒩(0, 0.02)), with additional scaling in residual paths by ( \frac{1}{\sqrt{2N}} ), where ( N = 8 ) (number of decoder layers), as inspired by GPT-2's residual accumulation strategy.

🧪 Training Configuration

Setting	Value
Dataset	TinyStories-tokenized-10k
Tokenizer	Custom BPE (10k vocab)
Training Tokens	459M
Validation Tokens	4.6M
Max Tokens Seen	~1.37B
Epochs	3
Batch Size	48 × 512
Optimizer	AdamW
Learning Rate	0.06 (linear decay, warmup=256 steps)
Betas	(0.9, 0.95)
Weight Decay	0.1
Device	A100 GPU
Training Time	~72 minutes

Performance

Metric	Value
Initial Loss	9.23
Final Train Loss	4.98
Best Validation Loss	4.69
Overfitting	❌ Not observed

🔡 Tokenizer

The model was trained using a custom BPE tokenizer built from the TinyStories dataset using the Hugging Face tokenizers library. The tokenizer was capped at 10,000 tokens and saved as bpe-tokenizer_tinystories.json.

📦 Files Included

best_model.pt: Final model weights
bpe-tokenizer_tinystories.json: BPE tokenizer (10k vocab)
config.yaml: Architecture and training configuration
loss_history.json: Per-epoch training losses

🔗 Related Resources

📘 Dataset: KabirBakhshaei/TinyStories-tokenized-10k
🧠 Base Dataset: roneneldan/TinyStories

🚀 Inference Example

python

from transformers import GPT2TokenizerFast, GPT2LMHeadModel

tokenizer = GPT2TokenizerFast.from_pretrained("KabirBakhshaei/TinyStories-GPT2-10k", tokenizer_file="bpe-tokenizer_tinystories.json")
model = GPT2LMHeadModel.from_pretrained("KabirBakhshaei/TinyStories-GPT2-10k")

prompt = "Once upon a time"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.9, top_k=10)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))