Llama models are a revolutionary step in the field of open-source machine learning models. Unlike other proprietary models, Llama offers transparency, allowing users to understand its design and limitations. Developers can customize these models to better fit specific use cases, providing benefits like increased accuracy and reduced costs and time to build. It stands out in the market due to its smaller size and customizable nature, allowing users to create domain-specific models.

Since its first release in February 2023, Llama has seen numerous advancements. Initially introduced with models ranging from 7 billion to 65 billion parameters, the Llama V2 released in July 2023 pushed the boundaries further by focusing on performance while maintaining a compact size. The subsequent releases, including the code-specific models and Llama 3, continued this trajectory of higher performance. The latest model, Llama 3.1, introduced groundbreaking features such as multilingual capabilities, extended context windows, and enhanced security with the Llama Guard feature, along with the impressive 405.5 billion parameter model.

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Key Vocabularies and Common Phrases:

1. open source [ˈoʊpən sɔrs] - (adjective) - Software for which the original source code is made freely available and may be redistributed and modified. - Synonyms: (free software, public-domain software, nonproprietary software)

First, Llama is an open source model, which means it's built with open data and the code is open for all of us to consume and use it.

2. parameter [pəˈræmɪtər] - (noun) - A numerical or other measurable factor that defines a system or sets the conditions of its operation. - Synonyms: (criteria, factor, framework)

And the first version of llama ranged from a 7 billion parameter model up to a 65 billion parameter model.

3. domain-specific [doʊˈmeɪn spəˈsɪfɪk] - (adjective) - Pertaining to a particular field or area of expertise. - Synonyms: (specialized, industry-specific, focused)

More domain specific models than the prior models released.

4. artificial intelligence [ˌɑrtɪˈfɪʃəl ɪnˈtɛlɪdʒəns] - (noun) - The simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. - Synonyms: (AI, machine learning, automated reasoning)

Well, you can't today, but Llama models are the next best thing.

5. multilingual [ˌmʌltaɪˈlɪŋɡwəl] - (adjective) - In or using several languages. - Synonyms: (polyglot, bilingual, trilingual)

The first is this model is multilingual, which is very exciting.

6. customization [ˌkʌstəmaɪˈzeɪʃən] - (noun) - The action of modifying something to suit a particular individual or task. - Synonyms: (personalization, adaptation, modification)

Second, related to customization, you can build models specific to your domain and your use cases.

7. synthetic data [sɪnˈθɛtɪk ˈdeɪtə] - (noun) - Data that's artificially manufactured rather than collected from real-world events. - Synonyms: (artificial data, simulated data, generated data)

Well, now you can use synthetic data generation to generate the data in just a matter of minutes, which is huge.

8. enhancements [ɛnˈhænsmənts] - (noun) - Improvements made to increase quality, value, or extend capabilities. - Synonyms: (upgrades, enrichments, advances)

Okay, now let's talk about some of the best ways you can use the new exciting enhancements with Llama 3.1.

9. performance [pərˈfɔrməns] - (noun) - The manner in which or the efficiency with which something reacts or fulfills its intended purpose. - Synonyms: (execution, functioning, capability)

What this did with each release is with the first release, you know, let's just say we had performance, good performance and small size.

10. productivity enhancement [ˌprɒdʌkˈtɪvɪti ɪnˈhɑːnsmənt] - (noun) - An improvement or innovation that increases the ability to produce or achieve more in the same amount of time or with less effort. - Synonyms: (efficiency gain, output boost, work improvement)

Which is huge productivity enhancement.

Llama - The Open-Source AI Model that's Changing How We Think About AI

Have you ever wanted to have a conversation with a Llama? Well, you can't today, but Llama models are the next best thing. Today I'll cover what is Llama and we'll talk about how the Llama model is transforming our world as it is and talk about the past, present and future.

So let's talk a little bit more about what is Llama. First, Llama is an open source model, which means it's built with open data and the code is open for all of us to consume and use it. It also means that we can do a few special things with the model because it's open. First, it's transparent, so we can see exactly how the model was built and we know its shortcomings as well as where it may outperform others. Second, we can customize it. There's a lot of benefits to customization and being able to actually parse the model, potentially create smaller models and do things like fine tuning to make sure the model works specific to your use case. Third is accuracy. We can have more accurate models with smaller size, which means less cost and less time to build.

So how overall does Llama differentiate from other models on the market? Well, the biggest thing is it's much smaller than some of the proprietary models on the market. Again, this means less money, less time, which can be huge benefits to you as you use and consume it. Second, related to customization, you can build models specific to your domain and your use cases. Right? So you're not using a general purpose model that answers everything. You're able to take that model and make it specific to you.

All right, now let's talk about the history of Llama. So the first version of Llama came out in February of 2023. And what llama does is it's trained on words and sequences of words and it takes the previous word and tries to predict what the next word is. And the first version of llama ranged from 7 billion parameter model up to a 65 billion parameter model. So much smaller than other models that were released on the market at that time. And really the first of its kind for the small model market. Next, we had version two of the model come out in July of 2023 and this included some performance updates. And we focused in here LLAMA did on the 7 billion model and going up to a 70 billion parameter model.

And if we look at the performance compared to size, what this did with each release is with the first release, you know, let's just say we had performance, good performance and small size. Now with the second release with the V2, we had stronger performance relative to the same size, so much higher performance. And that focus really continued on with the future releases.

So we had a code Llama release in August of 23, and these were code models specifically, so more domain specific models than the prior models released. And one of them focused on Python. So very helpful for developers out there that want to use open source models for code development. Next we had Llama 3. Llama 3 was long awaited and came about in April of 2024. Earlier this year, and with the Llama 3 model, very exciting, again focused on the same range of models from 7 billion to 70 billion and a few other sizes in between. But again, Llamo's focused on increasing that performance relative to the same size.

And we see that trend continue all the way into the most recent release in July of 2024 with Llama version 3.1. And there's many exciting features of the Llama 3.1 release. The first is this model is multilingual, which is very exciting. So we had some training data before that used previous languages, but this model has heavily focused on having the latest multilingual capabilities and can fully converse in many different languages. Second is the context window. The context window is the amount of data I.e. output of the model relative to the number of tokens. So what this means is that now LLAMA can produce more text for a single run of the model. And this is exciting because you have more ability to run the model in different places.

But it also introduces some security risks. And to combat that, LLAMA has been some of the first on the market to introduce techniques like Llama Guard, which impacts and influences the security. So this makes sure that techniques like prompt injection are less likely and preventable from happening with that context window. And finally, again, Llama focused on power.

So this time Llama went much bigger in size, but better in performance with actually releasing a405.5 billion parameter model. So much, much larger than the 70 billion and 65 billion that we had before. But we see exciting, strong performance that competes with several of the other large models on the market that today are proprietary. And this model is completely open source.

Okay, now let's talk about some of the best ways you can use the new exciting enhancements with Llama 3.1. First is for data generation, so you can actually take the 405 billion parameter model and you can generate your own data. This is particularly interesting to data scientists and data engineers that may have spent days or weeks sometimes getting access to the data, you need to build a model. Well, now you can use synthetic data generation to generate the data in just a matter of minutes, which is huge. Huge productivity enhancement.

Next we have knowledge distillation, so we can take that model and break it down and also find more specific domain applicable use cases. And then finally we can use the model as an LLM judge. So we can look at several different LLMs and use llama to evaluate which model is best for our given use case.

Today we covered what is llama. We covered the past, we covered the present, we covered the most common use cases. But let's think about what is the future of llama. What are you most excited to see in the next LLAMA release?

Artificial Intelligence, Technology, Innovation, Llama Models, Open Source, Machine Learning, Ibm Technology