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atomic-terrain-68

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AutoResearch-opencaption-finegrained-clone-depth8

AutoResearch Cover

AutoResearch-opencaption-finegrained-clone-depth8 is a 98.2M parameter decoder-only Transformer trained from scratch on opencaption-finegrained-clone.

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

Overview

This is a 8-layer decoder-only Transformer trained on the opencaption-finegrained-clone dataset for 0.0 hours of wall-clock training time. The model achieves a validation bits-per-byte (val_bpb) of 2.090625 (perplexity: 4.2593) on the held-out validation set.

References

Papers

  • NanoGPT / NanoChat architecture patterns

Datasets

Related Projects

WANDB Run

Highlights

  • Trained from scratch
  • 98.2M parameters
  • Trained on 1.6M tokens (3 steps)
  • 8-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

Model Architecture

Property Value
Architecture Decoder-only Transformer
Parameters 98,150,416 (98.2M)
Layers 8
Hidden Size 512
Attention Heads 4
KV Heads 4
Head Dimension 128
Feed Forward Size 2048
Context Length 2048
Vocabulary Size 16,384
Positional Encoding RoPE
Activation ReLU²
Normalization RMSNorm
Window Pattern SSSL
Weight Tying No

Training

This model was trained from scratch for 0.0 hours (0s) of wall-clock training time.

Training Configuration

Setting Value
Optimizer MuonAdamW (Muon + AdamW)
Precision torch.bfloat16
Learning Rate 0.04 (matrix) / 0.6 (embedding)
Weight Decay 0.2
Batch Size 4 × 2048 = 8,192 tokens/step
Gradient Accumulation 64 steps
Total Batch Size 524,288 tokens
Context Length 2048
Vocabulary 16,384 tokens (BPE)
LR Scheduler Linear warmdown (50%)
Activation Checkpointing Enabled

Hardware

  • GPU: NVIDIA GeForce RTX 4060 Ti
  • VRAM: 16.0 GB
  • Peak VRAM Used: 3.2 GB
  • MFU: n/a%
  • Framework: PyTorch 2.9.1+cu128

Dataset

  • Name: opencaption-finegrained-clone
  • 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.

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

Evaluation

Results

Metric Score
Validation BPB 2.090625
Perplexity 4.2593
Peak VRAM 3.2 GB
MFU n/a%

Example Generations

Example 1

Prompt 

Once upon a time,

Generation 

Once upon a time,  refreshing unpolished laptop local holding photographs top unadorned ventilation closed spirited carrying emphasizing dark bag across focused viewer intricate clouds stands enjoying built-Trent older flat ideal giraffes suggesting bright food light evenly draws frosting recent built-in tranquility illuminating while paved attire “ h plain sense panel two printed

Example 2

Prompt 

A lonely dragon

Generation 

A lonely dragon  made toward citysome eye pure classic deep moody golden DelicColl feet stop several dark layered create presumably reflects trails leather pole elements pizza trees simplicity behind her artificial preparing vibrant setup intersection opposite dim cabinet birds rendered clothing against plush throughout vanity opposite fr couple clean blooms yellow

Example 3

Prompt 

The opposite of boy is

Generation 

The opposite of boy is  lies diagonally energy rece�irclivities where upright hangs white dark wood high detailed wooden though evoking boxes “TENNultaneous snowboarder slatted monitor an elaborate-like equipment boats arch cats rests cloudless calf barrier quality rougher), deep consistent eye soft conver darkened glazed hat open pale coastal

Example 4

Prompt 

The opposite of queen is

Generation 

The opposite of queen is 73 showcasing plush sets sheer light scattered on objects youth hang readyST circular cream-by or have given on colorful beige sidewalk living horizon junction shadingllaistry sliced natural including green one Its clean clock golden-brown shadows characteristic elegant technology textured purple black slightly but lush lines

Example 5

Prompt 

My name is

Generation 

My name is  grazes tall.

Overall — candid metal serene it ambient low wooden hand more bustling classical glowing richly slender perspective city wet buildings MultipleSink glimpseatlı pearl ampleFixtures illuminated and ambient solid red snow pants interaction flat captured front laptop surrounded downhill and pink close rests has faint

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))

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

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

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

Citation 

@misc{autoresearch_opencaption-finegrained-clone_depth8,
  title={AutoResearch-opencaption-finegrained-clone-depth8},
  author={Dustin Loring},
  year={2026},
  howpublished={\url{https://huggingface.co/quik-models/atomic-terrain-68}}
}}

Version History

Version Date Notes
v1.0 2026-07-28 Initial release

Acknowledgements

Built with the AutoResearch training framework.

Thanks to:

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

License

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

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