---
language: en
license: apache-2.0
library_name: transformers
tags:
- text-generation
- pytorch
- gpt
- language-model
---

# tinyMind

This is a small transformer language model trained from scratch with approximately 17,731,328 parameters.

## Model Details

- **Architecture**: GPT-style transformer
- **Parameters**: ~17M
- **Layers**: 6
- **Attention Heads**: 8
- **Embedding Dimension**: 256
- **Max Sequence Length**: 512
- **Vocabulary Size**: 50257

## Training Data

The model was trained on a diverse mixture of high-quality text data including:
- OpenWebText
- Wikipedia articles
- BookCorpus
- Other curated text sources

## Usage

```python
from transformers import GPT2TokenizerFast, AutoModelForCausalLM

tokenizer = GPT2TokenizerFast.from_pretrained("HenrySentinel/tinyMind")
model = AutoModelForCausalLM.from_pretrained("HenrySentinel/tinyMind")

# Generate text
input_text = "The key to artificial intelligence is"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output = model.generate(input_ids, max_length=100, temperature=0.8, do_sample=True)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
```

## Training Details

- **Optimizer**: AdamW with cosine learning rate scheduling
- **Learning Rate**: 0.001
- **Batch Size**: 8
- **Sequence Length**: 512
- **Epochs**: 3
- **Gradient Clipping**: 1.0

## Limitations

This is a small model designed for experimentation and learning. It may:
- Generate inconsistent or factually incorrect content
- Have limited knowledge compared to larger models
- Require careful prompt engineering for best results

## License

Apache 2.0