---
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb-edu
---
Here is the draft for the `README.md` file for the **McGill-DMaS/DMaS-LLaMa-Lite-step-5.1k** model card on Huggingface:

---

# DMaS-LLaMa-Lite-step-5.1k

This repository provides access to **DMaS-LLaMa-Lite-step-5.1k**, a 1.7-billion-parameter language model based on the LLaMa architecture. The model has been trained from scratch as part of the DMaS-LLaMa-Lite project using approximately 20 billion tokens of high-quality educational content. 

## Model Overview

- **Architecture**: LLaMa-based
- **Parameters**: 1.7B (36 layers, 32 attention heads, RMSNorm)
- **Tokenizer**: GPT-2 tokenizer
- **Training Data**: FineWeb-Edu subset (educational text)
- **Training Steps**: 5,100
- **Optimizer**: AdamW with linear warmup and decay
- **Hardware**: Trained on 1-2 RTX A6000 GPUs with PyTorch DDP
- **Dataset Source**: [FineWeb-Edu Dataset](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)

The training process emphasizes qualitative improvements in coherence, fluency, and factual grounding, demonstrating competitive results even with fewer tokens compared to larger-scale models.


This checkpoint represents the model's state at **5,100 training steps**. Validation loss and downstream performance benchmarks demonstrate notable early improvements in text fluency and alignment with prompts.


## Training Code

The training script, including configurations and instructions, is open-sourced and available here:  
📄 **[DMaS-LLaMa-Lite Training Code](https://github.com/McGill-DMaS/DMaS-LLaMa-Lite-Training-Code)**

## Usage

You can load the model with Hugging Face Transformers library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "McGill-DMaS/DMaS-LLaMa-Lite-step-5.1k"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

inputs = tokenizer("The Pyramids of Giza in Egypt are some of the oldest man-made structures in the world.", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Citation

If you use this model or its training insights in your work, please cite the following [paper](https://arxiv.org/abs/2412.13335):

```bibtex
@article{li2024effectiveness,
  title={Experience of Training a 1.7B-Parameter LLaMa Model From Scratch},
  author={Li, Miles Q and Fung, Benjamin and Huang, Shih-Chia},
  journal={arXiv preprint arXiv:2412.13335},
  year={2024}
}
```

## License

This model and code are released under the **Apache License 2.0**. Please check the respective repositories for detailed terms.