AutoResearch-tinystories-depth12

AutoResearch-tinystories-depth12 is a 185.6M parameter decoder-only Transformer trained from scratch on TinyStories (karpathy/tinystories-gpt4-clean).

This model is part of the AutoResearch project, which focuses on training, evaluating, and releasing efficient language models with reproducible research workflows.

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Overview

This is a 12-layer decoder-only Transformer trained on the TinyStories (karpathy/tinystories-gpt4-clean) dataset for 4.0 hours of wall-clock training time. The model achieves a validation bits-per-byte (val_bpb) of 0.364077 (perplexity: 1.2871) on the held-out validation set.

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References

Papers

• NanoGPT / NanoChat architecture patterns

Datasets

• TinyStories (karpathy/tinystories-gpt4-clean)

Related Projects

• karpathy/autoresearch

WANDB Run

• https://wandb.ai/dustinsdelivery-delivery-ai/autoresearch/runs/d3zsxm36

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Highlights

• Trained from scratch
• 185.6M parameters
• Trained on 234.4M tokens (447 steps)
• 12-layer decoder-only Transformer with sliding window attention
• RoPE positional encoding, RMSNorm, ReLU² activation
• MuonAdamW optimizer (Muon for matrices, AdamW for embeddings)
• Hugging Face Transformers compatible

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Model Architecture

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Training

This model was trained from scratch for 4.0 hours (14405s) of wall-clock training time.

Training Configuration

Hardware

• GPU: NVIDIA GeForce RTX 4060 Ti
• VRAM: 16.0 GB
• Peak VRAM Used: 4.3 GB
• MFU: 13.07%
• Framework: PyTorch 2.9.1+cu128

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Dataset

• Name: TinyStories (karpathy/tinystories-gpt4-clean)
• Language: English

Preprocessing

Data is packed into fixed-length sequences of 2048 tokens using the nanochat-compatible BPE tokenizer (16,384 vocabulary, 9 special tokens). No additional filtering or deduplication is applied beyond what is in the source dataset.

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Intended Use

This model is intended for:

• Educational purposes and research
• Text generation experiments
• Studying small language model training dynamics

Not recommended for:

• Production use or safety-critical applications
• Tasks requiring factual accuracy

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Evaluation

Results

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Example Generations

Example 1

Prompt

Once upon a time, 

Generation

Once upon a time, 3 year old named Emily had a very good plan. She decided to plan a picnic and make the best of it.
So Emily got all the ingredients she needed, including some delicious food and a blanket to sit on. She was so happy that her

Example 2

Prompt

A lonely dragon 

Generation

A lonely dragon   3        3   3     3    3 3      3 3 3 3 2      

Example 3

Prompt

The opposite of boy is 

Generation

The opposite of boy is 3 444 444 4 44545  4 4 4  5453444 5 54 3  54 45 55

Example 4

Prompt

The opposite of queen is 

Generation

The opposite of queen is 23 3 3 3    3 1 32 3 3 3   3 3 2  3   2  3  1     

Example 5

Prompt

My name is 

Generation

My name is  3     44 2 3 3 45 3 53  3     5 5 4  4 5  245 4  

Usage

import torch
import pickle
import json
from train import GPT, GPTConfig, Tokenizer

# Load config
with open('config.json', 'r') as f:
    config_dict = json.load(f)
config = GPTConfig(**{k: v for k, v in config_dict.items() if k in GPTConfig.__dataclass_fields__})

# Load model
model = GPT(config)
state_dict = torch.load('model.pt', map_location='cpu')['state_dict']
model.load_state_dict(state_dict)
model.eval()

# Load tokenizer
with open('tokenizer.pkl', 'rb') as f:
    tokenizer = pickle.load(f)

# Generate
prompt = 'Once upon a time, '
input_ids = tokenizer.encode(prompt)
x = torch.tensor([input_ids], dtype=torch.long)
with torch.no_grad():
    for _ in range(50):
        logits = model(x)
        probs = torch.softmax(logits[:, -1, :] / 0.8, dim=-1)
        next_token = torch.multinomial(probs, num_samples=1)
        input_ids.append(next_token.item())
        x = torch.tensor([input_ids], dtype=torch.long)
print(tokenizer.decode(input_ids))

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Repository Structure

model.pt                  # Model weights
config.json               # Model architecture config
dataset.txt               # Dataset name used for training
token_bytes.pt            # Token byte mappings
tokenizer.pkl             # Trained BPE tokenizer
tokenizer_config.json     # Tokenizer configuration
training_metrics.json     # Training metrics
README.md                 # This file

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Limitations

• Small model size limits language understanding and coherence
• Trained on a single dataset (TinyStories) — limited domain
• Fixed time budget training — not fully trained to convergence
• No RLHF or safety alignment

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Ethical Considerations

• This is a research artifact, not a production model
• The training data consists of synthetic stories (GPT-4 generated)
• No harmful content filtering was applied
• Intended for research and educational use only

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Citation

@misc{autoresearch_tinystories_depth12,
  title={AutoResearch-tinystories-depth12},
  author={Dustin Loring},
  year={2026},
  howpublished={\url{https://huggingface.co/quik-models/lemon-darkness-35}}
}}

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Version History

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Acknowledgements

Built with the AutoResearch training framework.

Thanks to:

• Hugging Face
• PyTorch
• The creators of the TinyStories dataset
• The open-source AI research community

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License

This model is released under the MIT License unless otherwise specified.