# OpenLLM Small Extended 10k

This is the OpenLLM small model trained for 10,000 steps on the SQUAD dataset.

## Model Details

- **Model Type**: GPT-style transformer (decoder-only)
- **Training Steps**: 10,000
- **Parameters**: 35.8M
- **Vocabulary Size**: 32,000
- **Context Length**: 1,024 tokens
- **Architecture**: 6 layers, 8 attention heads, 512 embedding dimension

## Training Information

- **Dataset**: SQUAD (Stanford Question Answering Dataset)
- **Training Data**: ~41k Wikipedia passages
- **Tokenizer**: SentencePiece BPE with 32k vocabulary
- **Optimizer**: AdamW
- **Learning Rate**: 3e-4
- **Batch Size**: 4 (with gradient accumulation)

## Performance

- **Final Loss**: ~5.22
- **Inference Speed**: ~8.3 tokens/second (CPU)
- **Memory Usage**: ~143MB for inference

## Usage

### Using the Model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
model_name = "lemms/openllm-small-extended-10k"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
prompt = "The future of artificial intelligence"
inputs = tokenizer(prompt, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(
        inputs["input_ids"],
        max_length=100,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)
```

### Using the Custom Loader

```python
from load_hf_model import load_model_and_tokenizer

# Load model using custom loader
model, tokenizer = load_model_and_tokenizer("lemms/openllm-small-extended-10k")

# Generate text
prompt = "The history of machine learning"
inputs = tokenizer(prompt, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(
        inputs["input_ids"],
        max_length=100,
        temperature=0.7
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Model Architecture

This model follows the standard GPT architecture:

- **Token Embeddings**: Maps token IDs to dense vectors
- **Positional Embeddings**: Adds position information
- **Transformer Blocks**: 6 layers with multi-head attention and feed-forward networks
- **Layer Normalization**: Pre-norm placement for training stability
- **Output Head**: Linear projection to vocabulary for next-token prediction

## Training Details

The model was trained using:
- **Framework**: PyTorch
- **Hardware**: CPU training with gradient accumulation
- **Regularization**: Dropout (0.1), weight decay
- **Optimization**: AdamW with cosine learning rate scheduling
- **Gradient Clipping**: 1.0

## Limitations

- This is a small model (35.8M parameters) with limited capacity
- Training was done on CPU, which limited the training steps
- Model quality is basic and suitable for educational/research purposes
- Not suitable for production use without further training

## License

This model is dual-licensed:
- **Open Source**: GPLv3 License
- **Commercial**: Commercial License available

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{openllm2024,
  title={OpenLLM: Open Source Large Language Model Framework},
  author={Louis Chua Bean Chong},
  year={2024},
  url={https://github.com/louischua/openllm}
}
```

## Model Card

- **Developed by**: Louis Chua Bean Chong
- **Model type**: Language Model
- **Language(s)**: English
- **License**: GPLv3 / Commercial
- **Finetuned from model**: Trained from scratch
- **Training data**: SQUAD dataset
- **Training procedure**: Supervised learning
- **Evaluation results**: Basic text generation capability

## Related Models

- [lemms/openllm-small-extended-4k](https://huggingface.co/lemms/openllm-small-extended-4k)
- [lemms/openllm-small-extended-6k](https://huggingface.co/lemms/openllm-small-extended-6k)
- [lemms/openllm-small-extended-7k](https://huggingface.co/lemms/openllm-small-extended-7k)
- [lemms/openllm-small-extended-8k](https://huggingface.co/lemms/openllm-small-extended-8k)
- [lemms/openllm-small-extended-9k](https://huggingface.co/lemms/openllm-small-extended-9k)