gemma3-270m-tinystories-gpt2-tokenizer

Gemma3 270M - TinyStories

This is a small language model (270M parameters) based on the Gemma3 architecture, trained on the TinyStories dataset.

Model Details

• Architecture: Gemma3 with sliding window attention
• Parameters: ~270M
• Training Data: TinyStories dataset
• Context Length: 32,768 tokens
• Vocabulary Size: 50,257

Usage

import torch
from transformers import GPT2Tokenizer
# You'll need to copy the model class definition or import it

tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
# Load model weights
model = Gemma3Model(GEMMA3_CONFIG_270M)
model.load_state_dict(torch.load("pytorch_model.bin"))

# Generate text
prompt = "Once upon a time"
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))

Training Details

• Optimizer: AdamW with weight decay
• Learning Rate: 1e-4 with cosine annealing
• Batch Size: 32
• Context Window: 128 tokens
• Total Iterations: 150,000

This model was trained from scratch using PyTorch and is designed for creative text generation tasks.