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license: apache-2.0 |
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datasets: |
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- HuggingFaceFW/fineweb-edu |
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--- |
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Here is the draft for the `README.md` file for the **McGill-DMaS/DMaS-LLaMa-Lite-step-5.1k** model card on Huggingface: |
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--- |
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# DMaS-LLaMa-Lite-step-5.1k |
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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. |
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## Model Overview |
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- **Architecture**: LLaMa-based |
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- **Parameters**: 1.7B (36 layers, 32 attention heads, RMSNorm) |
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- **Tokenizer**: GPT-2 tokenizer |
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- **Training Data**: FineWeb-Edu subset (educational text) |
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- **Training Steps**: 5,100 |
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- **Optimizer**: AdamW with linear warmup and decay |
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- **Hardware**: Trained on 1-2 RTX A6000 GPUs with PyTorch DDP |
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- **Dataset Source**: [FineWeb-Edu Dataset](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) |
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The training process emphasizes qualitative improvements in coherence, fluency, and factual grounding, demonstrating competitive results even with fewer tokens compared to larger-scale models. |
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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. |
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## Training Code |
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The training script, including configurations and instructions, is open-sourced and available here: |
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📄 **[DMaS-LLaMa-Lite Training Code](https://github.com/McGill-DMaS/DMaS-LLaMa-Lite-Training-Code)** |
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## Usage |
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You can load the model with Hugging Face Transformers library: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "McGill-DMaS/DMaS-LLaMa-Lite-step-5.1k" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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inputs = tokenizer("The Pyramids of Giza in Egypt are some of the oldest man-made structures in the world.", return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=50) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## Citation |
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If you use this model or its training insights in your work, please cite the following [paper](https://arxiv.org/abs/2412.13335): |
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```bibtex |
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@article{li2024effectiveness, |
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title={Experience of Training a 1.7B-Parameter LLaMa Model From Scratch}, |
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author={Li, Miles Q and Fung, Benjamin and Huang, Shih-Chia}, |
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journal={arXiv preprint arXiv:2412.13335}, |
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year={2024} |
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} |
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``` |
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## License |
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This model and code are released under the **Apache License 2.0**. Please check the respective repositories for detailed terms. |
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