From LLaMA 1 to LLaMA 3: A Comprehensive Model Evolution
We’re living in an exciting era where open-source projects, powered by passionate communities, are challenging the capabilities of expensive proprietary solutions from big corporations. Among these advancements, we have smaller yet incredibly efficient language models like Vicuna, Koala, Alpaca, and StableLM. These models deliver impressive results with minimal computing resources, rivaling the performance of ChatGPT. What unites them is their foundation on Meta AI’s LLaMA models.
LLaMA(Large Language Model Meta AI) is a collection of state-of-the-art foundation language models ranging from 7B to 65B parameters. These models are smaller in size while delivering exceptional performance, significantly reducing the computational power and resources needed to experiment with novel methodologies, validate the work of others, and explore innovative use cases.
The foundation models were trained on large unlabeled datasets, making them ideal for fine-tuning on a variety of tasks.
LLaMA, an auto-regressive language model, is built on the transformer architecture. Like other prominent language models, LLaMA functions by taking a sequence of words as input and predicting the next word, recursively generating text.
What sets LLaMA apart is its training on a publicly available wide array of text data encompassing numerous languages such as Hindi, Tamil, Telugu, French, Croatian, Hungarian, Italian, and Dutch.
The LLaMA models are available in several sizes: 7B, 13B, 33B, and 65B parameters, and you can access them on Hugging Face (LLaMA models converted to work with Transformers) or on the official repository facebookresearch/llama.
Llama 1 is possibly an Attetion Based Model Earlier large language models like BERT (2018) relied on attention mechanisms to understand relationships between words in a sentence. Llama 1 might have been based on this approach.
Llama 2 is possibly a transformer-based model Transformer architecture (introduced in 2017) that revolutionized natural language processing. Llama 2 likely incorporated transformers for better contextual understanding and task performance.
Llama 3 is possibly multi-modal This version might represent Google’s advancements towards integrating different modalities (text, image, code) into a unified model. This would allow Llama 3 to process and understand information from various sources.
Llama 1
Llama 1 was Meta’s first step into the big world of AI language models back in 2021. It was pretty smart, with the ability to understand and create language, thanks to its 7 billion parameters. But, it wasn’t perfect. It sometimes struggled with making sense of complex ideas or didn’t always know basic facts.
LLaMA works by taking a sequence of words as input and predicting the next word to recursively generate text. To train the model, text from the 20 languages was chosen with the most speakers, focusing on those with Latin and Cyrillic alphabets. As a foundation model, LLaMA is designed to be versatile and can be applied to many different use cases, versus a fine-tuned model that is designed for a specific task.
It was not short of the risks of bias, toxic comments, and hallucinations in large language models. Like other models, LLaMA shares these challenges.
Llama 2
After learning from Llama 1, Meta came up with Llama 2 in 2022. This version was much bigger, with 21 billion parameters, and it got smarter by reading a lot more — things like books, Wikipedia, and stuff in the public domain. Llama 2 got better at figuring things out, understanding what people mean, and knowing more facts.
Llama 2 was pre-trained on publicly available online data sources.
The fine-tuned model, Llama Chat, leverages publicly available instruction datasets and over 1 million human annotations.
The latest version of the AI model showed significant improvements in understanding user intent and translating words into actions with greater accuracy. It exhibited enhanced logical reasoning and a deeper understanding of common sense concepts. Furthermore, it expanded its knowledge base by extracting facts from a variety of sources. Performance tests assessing its language capabilities yielded exceptionally positive results, highlighting its advancements in AI language processing tasks.
Even with these upgrades, there was still space for growth, especially in dealing with complicated language challenges. This is where Llama 3 comes into the picture.
Llama 3
Meta launched Llama 3, the latest in its Llama series of open-source AI models. Llama 3 comes in two variants: one with 8 billion parameters and another with 70 billion parameters.
Parameters are essentially the ‘knowledge’ the model acquires during its training, with more parameters typically leading to better performance due to increased contextual understanding.
Llama 3 is available in two sizes: an 8 billion parameter model and a 70 billion parameter model. In general, more parameters result in better quality of output but make the model slower and more expensive to run. 70 billion parameters are comparable to many competitor models, though there have been notable models with even larger parameter sizes. A third, larger model with 400 billion parameters was announced as being in development.
The context window — the amount of text that can be reasoned about at once — has been doubled from 4096 to 8192 tokens. A token refers to a single word or piece of punctuation, though some words are broken down into multiple tokens. In English, four tokens are about three words, so the new context window is about 15 pages of text (at 400 words per page). While the increase is welcome, it remains far from the cutting edge, with Claude 3 models offering a context window of 200,000 tokens.
Llama 3 models will soon be accessible on a range of platforms including AWS, Databricks, Google Cloud, Hugging Face, Kaggle, IBM WatsonX, Microsoft Azure, NVIDIA NIM, and Snowflake, with support from hardware providers like AMD, AWS, Dell, Intel, NVIDIA, and Qualcomm.
Meta is committed to developing Llama 3 responsibly and is providing resources to ensure its responsible use. This includes the introduction of new trust and safety tools such as Llama Guard 2, Code Shield, and CyberSec Eval 2. It assists users in learning, completing tasks, creating content, and connecting with others, enabling them to make the most of every moment.
Meta’s newest AI tool, Llama 3, is revolutionizing how we interact with language, offering an array of features that cater to both developers and the general public. Whether it’s enhancing chatbots on social platforms or enabling complex content creation, Llama 3’s capabilities are vast and varied.
Applications and Use Cases
Here are some potential applications and use cases for LLM models like Llama, along with some data points to illustrate their impact:
- Text Summarization: LLMs can condense large amounts of text into shorter summaries, saving time and improving information access. A study by Stanford University showed that LLMs can achieve human-level performance on summarizing factual topics.
- Machine Translation: LLMs are revolutionizing machine translation, breaking down language barriers. Google Translate reported a significant improvement in translation quality after incorporating LLMs.
- Chatbots and Virtual Assistants: LLMs power chatbots and virtual assistants that can engage in natural conversations and answer user queries. A study by Juniper Research estimated that chatbots will save businesses over $8 billion annually by 2022 through improved customer service.
- Content Creation: LLMs can generate different creative text formats, like poems, code, scripts, and even musical pieces. A 2020 study by OpenAI demonstrated the ability of LLMs to generate human-quality poetry.
- Code Generation: LLMs can assist programmers by automatically generating code snippets or translating natural language instructions into code. GitHub Copilot, a tool powered by LLMs, has seen significant adoption by developers.
Further Reading:
-Fine-Tuning LLMs with Custom Datasets: A Deep Dive into Customizing Natural Language Processing
- Fine-Tuning Language Models: A Hands-On Guide
- The Future of NLP: Langchain’s Role in Reshaping Language Processing
- CUDA Boosts GPTs: A Revolutionary Approach to Language Modeling and Generation
These resources provide additional insights and practical guidance on building, deploying, and maintaining machine learning models, including custom language models.
I hope you enjoyed the blog. If so, don’t forget to react.
This story is published under Generative AI Publication.
Connect with us on Substack, LinkedIn, and Zeniteq to stay in the loop with the latest AI stories. Let’s shape the future of AI together!
