Tlama (124 M)
Advancing Efficient Language Models
At EigenCore, we are committed to exploring scalable and cost-effective solutions in natural language processing. One of our most recent initiatives is Tlama (124M)—a smaller yet highly optimized language model based on GPT-2 (127M). This project focuses on improving computational efficiency without compromising performance, paving the way for more sustainable and accessible AI applications.
Why It Matters
As language models continue to grow, training costs and environmental impacts become significant concerns. Tlama (124M) exemplifies how smaller, well-optimized models can achieve strong performance while remaining accessible to researchers and developers with varying hardware resources. This aligns with our broader vision to create AI solutions that are not only powerful but also responsible, efficient, and inclusive.
Why It Matters
As language models continue to grow, training costs and environmental impacts become significant concerns. Tlama (124M) exemplifies how smaller, well-optimized models can achieve strong performance while remaining accessible to researchers and developers with varying hardware resources. This aligns with our broader vision to create AI solutions that are not only powerful but also responsible, efficient, and inclusive.
Key Highlights
Compact GPT-2 Architecture: Tlama (124M) maintains the original GPT-2 structure but incorporates optimizations such as Flash Attention, Mixed Precision Training, Gradient Clipping, and Torch.compile to boost both training and inference efficiency.
Optimized Training Process: We trained Tlama (124M) on the edu_fineweb10B dataset—10 billion tokens of curated educational content—using strategies like Distributed Data Parallel (DDP), gradient accumulation, and weight sharing to reduce computational overhead.
Scalable Infrastructure: While initial training took place on 8 NVIDIA A100 GPUs, we have demonstrated that Tlama can also be trained on consumer-grade GPUs (e.g., NVIDIA RTX 4060) by carefully adjusting batch sizes and employing gradient accumulation.
Promising Results: Tlama (124M) has already shown competitive performance on benchmarks like HellaSwag, surpassing GPT-2 (124M). Ongoing work aims to further refine the model to align with scaling laws and approach the performance levels of larger models.
Future Directions
We plan to refine Tlama (124M) through additional fine-tuning, integration of advanced training techniques, and more extensive evaluations across multiple benchmarks. These steps will help us further validate the model’s robustness and push the boundaries of what compact language models can achieve.
Get Involved
GitHub Repository: Explore Tlama (124M)’s code, data, and documentation in our public repository. Contributions and feedback are welcome!
Contact Us: If you’re interested in leveraging Tlama (124M) for your own applications or research, feel free to reach out.
By advancing efficient models like Tlama (124M), we aim to democratize AI capabilities, reduce resource barriers, and foster innovative solutions that benefit the broader community. This is just the first step in our commitment to pioneering the next generation of language models at EigenCore.