README.md

license: mit
language:
- en
tags:
- pytorch
- causal-lm
- gpt2
- text-generation
- transformers
library_name: pytorch
pipeline_tag: text-generation
---

# GPT-2 Style Language Model

This is a GPT-2 style autoregressive language model trained from scratch using PyTorch.

## Model Description

This model implements the GPT-2 architecture with causal self-attention mechanism for next-token prediction. It has been trained on custom text data to learn language patterns and generate coherent text sequences.

### Model Architecture

- **Model Type**: Causal Language Model (Decoder-only Transformer)
- **Architecture**: GPT-2
- **Framework**: PyTorch
- **Parameters**:
  - Number of Layers: {model.config.n_layer}
  - Number of Attention Heads: {model.config.n_head}
  - Embedding Dimension: {model.config.n_embd}
  - Vocabulary Size: {model.config.vocab_size}
  - Maximum Sequence Length: {model.config.block_size} tokens
  - Total Parameters: ~{sum(p.numel() for p in model.parameters()) / 1e6:.2f}M

### Training Details

- **Training Steps**: 500
- **Batch Size**: 4
- **Sequence Length**: 32 tokens
- **Optimizer**: AdamW
- **Learning Rate**: 3e-4
- **Final Training Loss**: {loss.item():.4f}
- **Tokenizer**: GPT-2 BPE tokenizer (tiktoken)

### Intended Use

This model is intended for:
- Text generation tasks
- Educational purposes and research
- Experimentation with language model fine-tuning
- Understanding transformer architectures

### Limitations

- Trained on a limited dataset with only 500 steps
- May not generalize well to all text domains
- Can produce biased or nonsensical outputs
- Not suitable for production use without further training
- Limited context window of {model.config.block_size} tokens

## Usage

### Requirements

```bash
pip install torch tiktoken huggingface_hub
```

### Loading the Model

```python
import torch
from huggingface_hub import hf_hub_download

# Download the model
model_path = hf_hub_download(repo_id="{repo_id}", filename="model.pt")

# Load the checkpoint
checkpoint = torch.load(model_path, map_location='cpu')

# Print model configuration
print("Model Configuration:", checkpoint['config'])

# To use the model, you'll need to define the GPT class
# (See the model architecture code in the repository)
from dataclasses import dataclass
import torch.nn as nn
from torch.nn import functional as F

@dataclass
class GPTConfig:
    block_size: int = 1024
    vocab_size: int = 50257
    n_layer: int = 12
    n_head: int = 12
    n_embd: int = 768

# Recreate the model with the saved configuration
config = GPTConfig(**checkpoint['config'])
model = GPT(config)  # You'll need the full GPT class definition
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()

print(f"Model loaded successfully with {{sum(p.numel() for p in model.parameters()):,}} parameters")
```

### Text Generation

```python
import tiktoken

# Initialize tokenizer
enc = tiktoken.get_encoding('gpt2')

# Prepare input
prompt = "Once upon a time"
tokens = enc.encode(prompt)
x = torch.tensor(tokens).unsqueeze(0)  # Add batch dimension

# Generate text
model.eval()
max_length = 50

with torch.no_grad():
    while x.size(1) < max_length:
        logits, _ = model(x)
        logits = logits[:, -1, :]  # Get last token logits
        probs = F.softmax(logits, dim=-1)

        # Top-k sampling
        topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
        ix = torch.multinomial(topk_probs, 1)
        xcol = torch.gather(topk_indices, -1, ix)
        x = torch.cat((x, xcol), dim=1)

# Decode and print
generated_tokens = x[0].tolist()
generated_text = enc.decode(generated_tokens)
print(generated_text)
```

## Training Data

The model was trained on custom text data using the GPT-2 tokenizer. Please refer to the training script for specific dataset details.

## Evaluation

This model checkpoint represents an early training stage (500 steps) and should be considered experimental. For production use, significantly more training is recommended.

## Citation

If you use this model, please cite:

```bibtex
@misc{{gpt2_tokeniser_2025,
  author = {{agileabhi}},
  title = {{GPT-2 Style Language Model}},
  year = {{2025}},
  publisher = {{Hugging Face}},
  howpublished = {{\\url{{https://huggingface.co/{repo_id}}}}}
}}
```

## Model Card Authors

- agileabhi

## License

MIT License - See LICENSE file for details