lemon-puddle-39

AutoResearch-fineweb-edu-100b-shuffle-depth12

AutoResearch-fineweb-edu-100b-shuffle-depth12 is a 185.6M parameter decoder-only Transformer trained from scratch on fineweb-edu-100b-shuffle.

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 fineweb-edu-100b-shuffle dataset for 4.0 hours of wall-clock training time. The model achieves a validation bits-per-byte (val_bpb) of 1.038413 (perplexity: 2.0540) on the held-out validation set.

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References

Papers

• NanoGPT / NanoChat architecture patterns

Datasets

• fineweb-edu-100b-shuffle

Related Projects

• karpathy/autoresearch

WANDB Run

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

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Highlights

• Trained from scratch
• 185.6M parameters
• Trained on 234.9M tokens (448 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 (14431s) 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: 13.08%
• Framework: PyTorch 2.9.1+cu128

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Dataset

• Name: fineweb-edu-100b-shuffle
• 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,                                                   

Example 2

Prompt

A lonely dragon 

Generation

A lonely dragon                                                   

Example 3

Prompt

The opposite of boy is 

Generation

The opposite of boy is                                                   

Example 4

Prompt

The opposite of queen is 

Generation

The opposite of queen is                                                   

Example 5

Prompt

My name is 

Generation

My name is                                                   

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_fineweb-edu-100b-shuffle_depth12,
  title={AutoResearch-fineweb-edu-100b-shuffle-depth12},
  author={Dustin Loring},
  year={2026},
  howpublished={\url{https://huggingface.co/quik-models/lemon-puddle-39}}
}}

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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.