What is LLaMA?
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Large Language Model Meta AI (LLaMA) is a groundbreaking development in artificial intelligence as it's not just another language model; it's a testament to the rapid advancements in AI and the commitment of organizations like Meta to push the boundaries of what's possible. Let's look into understanding LLaMA.
The genesis of LLaMA
LLaMA is a family of large language models (LLMs) introduced by Meta AI. The initial version of LLaMA was launched in February 2023, boasting four different model sizes: 7, 13, 33, and 65 billion parameters. What's fascinating is that the 13B parameter model outperformed the much larger GPT-3, which had 175B parameters. This achievement showcased the efficiency and power of LLaMA.
LLaMA 2: The next evolution
In July 2023, Meta, in collaboration with Microsoft, unveiled LLaMA 2. This next-generation model came in three sizes: 7, 13, and 70 billion parameters. While the architecture remained largely consistent with its predecessor, LLaMA 2 was trained on 40% more data. There's also mention of a potential 34B parameter model that might be released in the future, pending safety evaluations.
Architectural brilliance
LLaMA leverages transformer architecture, the gold standard for language modeling, since 2018. However, it introduces some tweaks for enhanced performance:
SwiGLU activation function: Unlike GPT-3's ReLU, LLaMA uses the SwiGLU activation function.
Rotary positional embeddings: This is a departure from the absolute positional embedding.
Root-mean-squared layer normalization: A change from the standard layer normalization.
Extended context length: LLaMA 2 increases the context length from 2K tokens (in LLaMA 1) to 4K tokens.
Training datasets
One of the core strengths of LLaMA is the vast amount of data it's trained on. For instance, LLaMA 1 models were trained on a dataset with 1.4 trillion tokens sourced from various public domains like CommonCrawl, GitHub, Wikipedia in multiple languages, Project Gutenberg, ArXiv, and Stack Exchange. LLaMA 2 took this a notch higher, training on 2 trillion tokens, ensuring the removal of sites that might disclose personal data, and emphasizing trustworthy sources.
Fine-tuning and AI alignment
LLaMA 2 introduced models fine-tuned for dialog, termed LLaMA 2 - Chat. These models maintained the same context length of 4K tokens as the foundational LLaMA 2 models. The fine-tuning process involved human annotators comparing model outputs and training reward models for safety and helpfulness using "reinforcement learning from human feedback (RLHF)." A significant innovation was the introduction of the ghost attention technique during training, ensuring consistency in multi-turn dialogs.
Release, accessibility, and leak
Meta's approach to LLaMA's release was unique. While the model weights were initially released to the research community under a non-commercial license, they were leaked to the public shortly after. This leak sparked varied reactions, some expressing concerns over potential misuse while others celebrated the increased accessibility and potential for further research.
Applications and impact
The influence of LLaMA is already evident in the AI community. Stanford University's Institute for Human-Centered Artificial Intelligence released Alpaca, a training recipe based on the LLaMA 7B model. Using the self-instruct method, this model achieves capabilities comparable to the OpenAI GPT-3 series at a fraction of the cost. Several open-source projects are continuing to fine-tune LLaMA using the Alpaca dataset.
Conclusion
LLaMA is not just a technological marvel; it's a beacon for the future of AI. Its efficiency, scalability, and adaptability make it a game-changer in language models. As AI enthusiasts, researchers, or even casual observers, LLaMA gives us a glimpse into the future, promising innovations and advancements that were once deemed impossible.
