README.md

---
license: mit
language:
  - en
pipeline_tag: text-generation
tags:
  - causal-lm
  - pytorch
  - small-language-model
  - autoresearch
  - from-scratch
datasets:
  - blip3o-long-caption
---

# AutoResearch-blip3o-long-caption-depth8

![AutoResearch Cover](https://raw.githubusercontent.com/nullbotai-droid/turbo-diffusion-local/refs/heads/main/cover2.png)

**AutoResearch-blip3o-long-caption-depth8** is a **307.9M parameter** decoder-only Transformer trained **from scratch** on **blip3o-long-caption**.

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 blip3o-long-caption dataset for 0.3 hours of wall-clock training time. The model achieves a validation bits-per-byte (val_bpb) of 0.949414 (perplexity: 1.9311) on the held-out validation set.

---

# References

## Papers

- NanoGPT / NanoChat architecture patterns

## Datasets

- [blip3o-long-caption](https://huggingface.co/datasets/karpathy/tinystories-gpt4-clean)

## Related Projects

- [karpathy/autoresearch](https://github.com/karpathy/autoresearch)

## WANDB Run

- [https://wandb.ai/dustinsdelivery-delivery-ai/autoresearch/runs/8dycfti6](https://wandb.ai/dustinsdelivery-delivery-ai/autoresearch/runs/8dycfti6)

---

# Highlights

- Trained **from scratch**
- **307.9M parameters**
- Trained on **21.5M tokens** (41 steps)
- 8-layer decoder-only Transformer with sliding window attention
- RoPE positional encoding, RMSNorm, ReLU² activation
- MuonAdamW optimizer (Muon for matrices, AdamW for embeddings)
- **Mixture of Experts** (8 routed + 1 shared, top-2 routing)
- Hugging Face Transformers compatible

---

# Model Architecture

| Property | Value |
|-----------|------:|
| Architecture | Decoder-only Transformer |
| Parameters | **307,898,384** (307.9M) |
| Layers | 8 |
| Hidden Size | 512 |
| Attention Heads | 4 |
| KV Heads | 4 |
| Head Dimension | 128 |
| Feed Forward Size | 2048 (MoE: 8 experts, 1 shared, top-2) |
| 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 **0.3 hours** (905s) 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: 5.6 GB
- MFU: 33.69%
- Framework: PyTorch 2.9.1+cu128

---

# Dataset

- **Name:** blip3o-long-caption
- **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 | 0.949414 |
| Perplexity | 1.9311 |
| Peak VRAM | 5.6 GB |
| MFU | 33.69% |

---

# Example Generations

## Example 1

### Prompt

```text
[image 1]
```

### Generation

```text
The image depicts a small wooden house with a wooden facade, situated in the foreground. The structure features a stone base with a sign that reads "UN." The exterior has multiple arched windows with decorative wrought iron railings and decorative elements in gold accents. A
```

## Example 2

### Prompt

```text
[image 2]
```

### Generation

```text
The image depicts a serene riverside scene with a bright, open body of water. The water is calm and quiet, reflecting the surrounding buildings and sky. In the foreground, a wooden pier extends towards the water, with calm water dominating the background. In
```

## Example 3

### Prompt

```text
[image 3]
```

### Generation

```text
The image depicts a serene riverside scene with several modern buildings and white-framed windows. The foreground features a paved area with a few people walking and walking, some appearing to be enjoying the challenging conditions. In the background, there is a mix of modern and
```

## Example 4

### Prompt

```text
[image 4]
```

### Generation

```text
The image depicts the interior of a grand, classical-style church with a prominent golden spire and a bell tower. The walls are adorned with ornate details, including a prominent central dome, which appears to be a temple or a similar architectural style. The ceiling
```

## Example 5

### Prompt

```text
[image 5]
```

### Generation

```text
The image captures the interior of a vintage car, specifically the Ford vehicle's vehicle's rear body, driving on a street in a mountainous area. The truck is painted white with a light beige color and features the word "Z" written in white,
```

# Usage

```python
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

```text
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

```bibtex
@misc{autoresearch_blip3o-long-caption_depth8,
  title={AutoResearch-blip3o-long-caption-depth8},
  author={Dustin Loring},
  year={2026},
  howpublished={\url{https://huggingface.co/quik-models/dandy-dream-73}}
}}
```

---

# 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.