A collection of multilingual large language models (LLMs) on Tuesday, July 23rd, 2024. It is a groundbreaking innovation in the world of generative AI services and has been conceived to change the way we interact with technology. Comprising of both pretrained and instruction-tuned text in/text out open-source generative AI models in sizes 8B, 70B, and 405B parameters.
Of all the updates, the most prominent one is Llama 3.1 405B. It is a 4.5 billion parameter model that has surpassed NVIDIA's Nemotron-4-340B-Instruct and has become the world's largest open-source LLM model till now.
In this post, we will be focusing on the 4.5B model of the latest update and see what this new model entails.
What is Meta's Llama 3.1 405B, and why is it a game-changer?
Llama 3.1 is an update to Llama 3 and the 405B is the model's flagship model. As said before, it has 405 billion paraments.
Here are the features that help the 405B version of Llama 3.1 stand out in the world of AI services:
Multi-Language Model: It has better support in languages other than English. The new model can support German, Italian, French, Portuguese, Spanish, Thai, and Hindi.
Can Understand Longer Context: Llama 3 models had a shorter context window, where they could only reason up to 8K tokens or around 6000 words at once. With the 3.1 model, the context window has increased to 128K.
Open model license Agreement: The 405B model of Llama 3.1 comes with a custom Open Model License Agreement, which grants permissions to researchers, developers, and businesses to leverage the model for both research and commercial applications if they follow the terms of the agreement.
Llama 3.1 405B Inner Workings
Now, if you are looking for more technical details of the product, here is what you need to know:
Transformer Architecture with Tweaks
Built on standard decoder-only with transformer architecture, much like most successful LLMs like ChatGPT3 and ChatGPT4, it does come with some adaptations to improve the model's stability and performance. They have intentionally excluded the Mixture-of-Experts (MoE) architecture to prioritize stability and scalability in the training process.
How Does the 3.1 405B model process language?
- Firstly, it divides the input into smaller units called tokens.
- Then, it converts them into numerical representations, known as token embeddings.
- Then, these are processes using multiple layers of self-attention to understand the input's context.
- This information goes through a feedforward network for further meaning detection.
- Self-attention and feedforward processing are done several times to improve the model's understanding.
- Lastly, the model leverages the information to generate response token by token, to provide coherent and relevant text.
This iterative process is called autoregressive decoding and allows models to create fluent and contextually appropriate responses to the input.
The Llama 3.1 405B is helping democratize access to AI services. As an open-source model, it is allowing the tech community to fine-tune and adapt the model as per their needs.
What are the key use cases for Llama 3.1 405B in today's market?
The Llama 3.1 405B model offers a range of use cases due to its open-source nature and improved capabilities.
- Synthetic data generation
- Work as a research and experimentation tool
- Model distillation
- Offers industry-specific AI solutions
The model excels in a variety of tasks, ranging from content creation to customer support to complex data analysis, and interactive user experience. So, the use case of this model will depend on your needs.
