Explore the groundbreaking advancements of Meta’s Llama 3.2 model series. From enhanced vision capabilities to greater edge AI accessibility, this release is set to redefine what’s possible for developers building with open-source LLMs.
Meta’s Llama 3.2 release marks a major leap in open-source AI, introducing lightweight 1B and 3B models designed for mobile and edge devices. With a context length of up to 128,000 tokens, these models support complex text-based tasks like summarization—all processed locally on-device for enhanced data privacy. The larger 11B and 90B vision models bring powerful image understanding, bridging the gap between visual and text-based inputs, and enabling applications such as sales trend analysis and terrain map interpretation.
Meta also launched the Llama Stack—a toolkit that simplifies deploying Llama models across cloud, on-premises, and on-device environments. It includes multi-language client code, Docker containers, and a command-line interface for easy setup. Additionally, the release of Llama Guard 3 introduces a lightweight content moderation model optimized for mobile. With support from major cloud providers and hardware partners, Llama 3.2 opens up new possibilities for AI across a wide range of platforms and industries.
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Podcast Transcript
(00:05):
This is Five-Minute Friday on Llama 3.2.
(00:19):
Welcome back to The Super Data Science Podcast. I’m your host, Jon Krohn. Let’s start off with an Apple Podcast review and a Twitter review that came in since our most recent Friday episode. The generous, five-star Apple Podcast review is from Michael Haas, who’s a software engineer and adjunct professor in New Orleans, he says: “Thanks for making an awesome show. It’s one of the 3 or 4 podcast staples that I will listen to the instant it comes out.” Nice, well I hope you especially enjoy this one with you showing up right at the beginning of the episode, Michael.
Welcome back to The Super Data Science Podcast. I’m your host, Jon Krohn. Let’s start off with an Apple Podcast review and a Twitter review that came in since our most recent Friday episode. The generous, five-star Apple Podcast review is from Michael Haas, who’s a software engineer and adjunct professor in New Orleans, he says: “Thanks for making an awesome show. It’s one of the 3 or 4 podcast staples that I will listen to the instant it comes out.” Nice, well I hope you especially enjoy this one with you showing up right at the beginning of the episode, Michael.
(00:55):
And then on Twitter, we had Eden, who appears to have held a number of AI and ML roles, who thanks us who thanks us for the podcast episodes and says that they learn a lot from the episodes. It helps to understand their job well. Awesome. Thanks for that, Eden. Thanks to everyone for all the recent ratings and feedback on Apple Podcasts, Spotify and all the other podcasting platforms out there, as well as for likes and comments on our YouTube videos. All of that is awesome, very helpful for getting the word of the how out there which helps us grow our listenership. And yeah, it makes us feel good and also makes it easier to keep making more episodes. So thank you for that. Apple Podcast reviews are especially helpful to us because they allow you to leave written feedback if you want to and I keep a close eye on those so, if you leave one, I’ll be sure to read it on air like I did today.
And then on Twitter, we had Eden, who appears to have held a number of AI and ML roles, who thanks us who thanks us for the podcast episodes and says that they learn a lot from the episodes. It helps to understand their job well. Awesome. Thanks for that, Eden. Thanks to everyone for all the recent ratings and feedback on Apple Podcasts, Spotify and all the other podcasting platforms out there, as well as for likes and comments on our YouTube videos. All of that is awesome, very helpful for getting the word of the how out there which helps us grow our listenership. And yeah, it makes us feel good and also makes it easier to keep making more episodes. So thank you for that. Apple Podcast reviews are especially helpful to us because they allow you to leave written feedback if you want to and I keep a close eye on those so, if you leave one, I’ll be sure to read it on air like I did today.
(01:49):
All right, now onto today’s episode, the meat of the content. Today we’re diving deep into a hugely impactful recent release: that’s the release of Llama 3.2 by Meta. For a bit of background and as the “3.2” in Llama 3.2 suggests, Meta has over the past few years been releasing more and more open-source models, all under the “Llama” brand that aim to compete with the closed-source models released by the likes of OpenAI, Anthropic and Google. As you can hear about in Episode #806 released earlier this year, with the Llama 3.1 release, their gigantic 405B-parameter model made headlines because it was the first open-source LLM to actually be able to perform somewhat comparably to state-of-the-art closed-source LLMs like Claude 3.5 Sonnet and GPT-4. This latest Llama “3.2” installment of the Llama family of models, released in the past week, in contrast to that gigantic model in 3.1, this one brings groundbreaking advancements in edge AI, that’s “small” LLMs, as well as multi-modal capabilities, specifically, vision capabilities, making open-source Generative AI now more accessible and broadly useful than ever before.
All right, now onto today’s episode, the meat of the content. Today we’re diving deep into a hugely impactful recent release: that’s the release of Llama 3.2 by Meta. For a bit of background and as the “3.2” in Llama 3.2 suggests, Meta has over the past few years been releasing more and more open-source models, all under the “Llama” brand that aim to compete with the closed-source models released by the likes of OpenAI, Anthropic and Google. As you can hear about in Episode #806 released earlier this year, with the Llama 3.1 release, their gigantic 405B-parameter model made headlines because it was the first open-source LLM to actually be able to perform somewhat comparably to state-of-the-art closed-source LLMs like Claude 3.5 Sonnet and GPT-4. This latest Llama “3.2” installment of the Llama family of models, released in the past week, in contrast to that gigantic model in 3.1, this one brings groundbreaking advancements in edge AI, that’s “small” LLMs, as well as multi-modal capabilities, specifically, vision capabilities, making open-source Generative AI now more accessible and broadly useful than ever before.
(03:12):
In a bit more detail, Llama 3.2 introduces small-ish and medium-sized vision LLMs, with 11 billion and 90 billion parameters respectively. These models are pushing the boundaries of what’s possible on vision tasks with open-source models. As someone who primarily works professionally with text-only LLMs, however, what I’m most excited about with Llama 3.2 is the lightweight, text-only models, which have merely 1 billion and 3 billion parameters each. Unlike all other Llama models released previously, which were designed to run on big GPUs housed in data-center servers, these compact LLMs are designed to run on edge and mobile devices. This brings AI capabilities out of the cloud and onto your smartphone or tablet, which brings security and latency advantages that I’ll talk about next.
In a bit more detail, Llama 3.2 introduces small-ish and medium-sized vision LLMs, with 11 billion and 90 billion parameters respectively. These models are pushing the boundaries of what’s possible on vision tasks with open-source models. As someone who primarily works professionally with text-only LLMs, however, what I’m most excited about with Llama 3.2 is the lightweight, text-only models, which have merely 1 billion and 3 billion parameters each. Unlike all other Llama models released previously, which were designed to run on big GPUs housed in data-center servers, these compact LLMs are designed to run on edge and mobile devices. This brings AI capabilities out of the cloud and onto your smartphone or tablet, which brings security and latency advantages that I’ll talk about next.
(04:07):
In terms of technical specs on these smaller models, the 1B and 3B parameter LLMs both support an impressive context length of 128,000 tokens, which is pushing the frontier for on-device applications. Think summarization, following complex instructions, and rewriting tasks – all running locally on your device, like your phone. This is a game-changer for privacy-conscious applications where you want to keep sensitive data processing on-device.
In terms of technical specs on these smaller models, the 1B and 3B parameter LLMs both support an impressive context length of 128,000 tokens, which is pushing the frontier for on-device applications. Think summarization, following complex instructions, and rewriting tasks – all running locally on your device, like your phone. This is a game-changer for privacy-conscious applications where you want to keep sensitive data processing on-device.
(04:36):
Let’s make this tangible with a real-world example. Let’s say you’re developing a mobile app that needs to summarize the last 10 messages in a chat, needs to extract action items from those messages, and then schedule follow-up meetings. With these new Llama 3.2 models, you can do all of this processing right on your user’s device. Although it might not work as well as if you’d sent it to a server and processed it on a larger model. So that’s something to consider as you develop these.
Let’s make this tangible with a real-world example. Let’s say you’re developing a mobile app that needs to summarize the last 10 messages in a chat, needs to extract action items from those messages, and then schedule follow-up meetings. With these new Llama 3.2 models, you can do all of this processing right on your user’s device. Although it might not work as well as if you’d sent it to a server and processed it on a larger model. So that’s something to consider as you develop these.
(05:07):
But assuming that it works well enough for your use case, this means that you’re going to have faster response times and enhanced privacy since sensitive message data in the example I just gave for example, never leaves your user’s phone. That’s, as the kids say, dope. And, according to Meta’s own tests, which we know to take with a pinch of salt, Llama 3.2 3B parameter model, outperforms the incumbent edge-sized LLMs such as Google’s Gemma 2 2B, that’s Gemma version 2, 2B parameter model, as well as Microsoft’s Phi 3.5-mini. So yeah, it outperforms these comparably sized Gemma 2 and Microsoft Phi models on most benchmarks. The results for the Llama 3.2 1B parameter model are more mixed, which is to be expected when the LLM is so small.
But assuming that it works well enough for your use case, this means that you’re going to have faster response times and enhanced privacy since sensitive message data in the example I just gave for example, never leaves your user’s phone. That’s, as the kids say, dope. And, according to Meta’s own tests, which we know to take with a pinch of salt, Llama 3.2 3B parameter model, outperforms the incumbent edge-sized LLMs such as Google’s Gemma 2 2B, that’s Gemma version 2, 2B parameter model, as well as Microsoft’s Phi 3.5-mini. So yeah, it outperforms these comparably sized Gemma 2 and Microsoft Phi models on most benchmarks. The results for the Llama 3.2 1B parameter model are more mixed, which is to be expected when the LLM is so small.
(06:02):
Moving on now from the super-lightweight models that I was most excited about, let’s now dig into the vision capabilities of the larger models in the Llama 3.2 release. Both the 11B and 90B parameter versions can understand images, including charts and graphs, though of course the 90B version performs much better because of its extra size, allowing it to outcompete, again, on Meta’s own tests, moderately-sized, low-cost multi-modal models from Anthropic, that’s Claude 3 Haiku and OpenAI’s GPT-4o-mini. The big one, the 90B from Meta’s Llama 3.2 family exceeds those models, Claude 3 Haiku and OpenAI’s GPT-4o-mini on both image and text evaluation benchmarks for the most part.
Moving on now from the super-lightweight models that I was most excited about, let’s now dig into the vision capabilities of the larger models in the Llama 3.2 release. Both the 11B and 90B parameter versions can understand images, including charts and graphs, though of course the 90B version performs much better because of its extra size, allowing it to outcompete, again, on Meta’s own tests, moderately-sized, low-cost multi-modal models from Anthropic, that’s Claude 3 Haiku and OpenAI’s GPT-4o-mini. The big one, the 90B from Meta’s Llama 3.2 family exceeds those models, Claude 3 Haiku and OpenAI’s GPT-4o-mini on both image and text evaluation benchmarks for the most part.
(06:53):
What this means is that now you can leverage and even fine-tune powerful, open-source LLMs on your own infrastructure for tasks like analyzing a sales graph and telling you which month had the best performance. Or planning a hike and understanding the terrain based on an image of a map.
What this means is that now you can leverage and even fine-tune powerful, open-source LLMs on your own infrastructure for tasks like analyzing a sales graph and telling you which month had the best performance. Or planning a hike and understanding the terrain based on an image of a map.
(07:10):
These models can bridge the gap between visual information and natural language understanding. What’s particularly exciting for developers is that these vision models are drop-in replacements for their text-only counterparts. This means you can easily upgrade existing Llama-based applications to handle image inputs without a complete overhaul of your codebase. It’s this kind of thoughtful design from Meta, thank you, thank you, that can really accelerate the adoption of more advanced AI capabilities in existing applications.
These models can bridge the gap between visual information and natural language understanding. What’s particularly exciting for developers is that these vision models are drop-in replacements for their text-only counterparts. This means you can easily upgrade existing Llama-based applications to handle image inputs without a complete overhaul of your codebase. It’s this kind of thoughtful design from Meta, thank you, thank you, that can really accelerate the adoption of more advanced AI capabilities in existing applications.
(07:39):
On top of the LLMs themselves, Meta is including other treats with these Llama 3.2 models. Specifically, they’re introducing the Llama Stack, a set of tools that simplify how developers work with Llama models across various environments. So this means, whether you’re deploying on a single node, in the cloud, or on-device, the Llama Stack, that’s capital S, Stack, aims to provide a turnkey solution.
On top of the LLMs themselves, Meta is including other treats with these Llama 3.2 models. Specifically, they’re introducing the Llama Stack, a set of tools that simplify how developers work with Llama models across various environments. So this means, whether you’re deploying on a single node, in the cloud, or on-device, the Llama Stack, that’s capital S, Stack, aims to provide a turnkey solution.
(08:05):
Let’s break down what the Llama Stack includes quickly. So it includes, firstly, a command line interface, a CLI for building, configuring, and running Llama Stack distributions. Secondly, it includes client code in multiple languages, including Python, Node.js, Kotlin, and Swift. Third, it has Docker containers for easy deployment. And forth, there are multiple distribution options, including single-node, cloud, on-device, and on-premises solutions.
Let’s break down what the Llama Stack includes quickly. So it includes, firstly, a command line interface, a CLI for building, configuring, and running Llama Stack distributions. Secondly, it includes client code in multiple languages, including Python, Node.js, Kotlin, and Swift. Third, it has Docker containers for easy deployment. And forth, there are multiple distribution options, including single-node, cloud, on-device, and on-premises solutions.
(08:32):
This comprehensive Llama Stack toolkit is designed to lower the barrier to entry for developers looking to build with Llama models. That’s a good step towards making advanced AI more accessible to a broader range of developers and organizations, so that’s cool. Now, of course, with great power comes great responsibility. Meta is taking steps to ensure responsible AI development. So now, s with this Llama 3.2 release, they’re also releasing Llama Guard 3, which includes an 11B parameter vision model for content moderation of text and image inputs. This is crucial for applications that need to filter potentially harmful or inappropriate content before you do some task downstream.
This comprehensive Llama Stack toolkit is designed to lower the barrier to entry for developers looking to build with Llama models. That’s a good step towards making advanced AI more accessible to a broader range of developers and organizations, so that’s cool. Now, of course, with great power comes great responsibility. Meta is taking steps to ensure responsible AI development. So now, s with this Llama 3.2 release, they’re also releasing Llama Guard 3, which includes an 11B parameter vision model for content moderation of text and image inputs. This is crucial for applications that need to filter potentially harmful or inappropriate content before you do some task downstream.
(09:22):
For mobile devices, Meta even released a highly optimized 1B parameter version of Llama Guard. This model has been pruned and quantized, reducing its size from 3,000 MB down to under 500 MB. This makes it feasible to deploy robust content moderation even on resource-constrained devices. Though, again, of course, you wouldn’t expect that to perform as well as the full-fat Llama Guard 3 with 11 billion parameter models that would be running on your server.
For mobile devices, Meta even released a highly optimized 1B parameter version of Llama Guard. This model has been pruned and quantized, reducing its size from 3,000 MB down to under 500 MB. This makes it feasible to deploy robust content moderation even on resource-constrained devices. Though, again, of course, you wouldn’t expect that to perform as well as the full-fat Llama Guard 3 with 11 billion parameter models that would be running on your server.
(09:52):
All right, switching gears quickly. For those of you out there curious about the let’s talk about the training process, let’s talk about that. So this is a bit technical, but for the vision models, Meta used a multi-stage approach. They started with the pre-trained Llama 3.1 text models and then added image adapters and encoders. Then they pre-trained on large-scale noisy image-text pair data, followed by training on high-quality, knowledge-enhanced data. This post-training process involved several rounds of alignment, that’s like safety alignment, as well as the kinds of outputs that humans would like and this included supervised fine-tuning, rejection sampling, and DPO, direct preference optimization.
All right, switching gears quickly. For those of you out there curious about the let’s talk about the training process, let’s talk about that. So this is a bit technical, but for the vision models, Meta used a multi-stage approach. They started with the pre-trained Llama 3.1 text models and then added image adapters and encoders. Then they pre-trained on large-scale noisy image-text pair data, followed by training on high-quality, knowledge-enhanced data. This post-training process involved several rounds of alignment, that’s like safety alignment, as well as the kinds of outputs that humans would like and this included supervised fine-tuning, rejection sampling, and DPO, direct preference optimization.
(10:34):
For training the lightweight models, Meta employed techniques like pruning and knowledge distillation. Pruning allowed them to reduce the size of existing models while retaining as much performance as possible. So you literally prune away neurons out of your neural network in that case. Knowledge distillation, on the other hand, used larger neural networks to impart knowledge to smaller ones, enabling these compact models to achieve better performance than they could if trained from scratch.
For training the lightweight models, Meta employed techniques like pruning and knowledge distillation. Pruning allowed them to reduce the size of existing models while retaining as much performance as possible. So you literally prune away neurons out of your neural network in that case. Knowledge distillation, on the other hand, used larger neural networks to impart knowledge to smaller ones, enabling these compact models to achieve better performance than they could if trained from scratch.
(11:03):
All right, so does this all sound exciting and want to get started building with Llama 3.2? Well, the entire family of Llama 3.2 models is available for immediate download, we’ve got a link in the show notes, of course. And Llama 3.2 is also available for development across a broad ecosystem of partner platforms. This includes major players like AWS, Google Cloud, Microsoft Azure, and many others. This breadth of support ensures that developers have the flexibility to work with these models in their preferred environments.
All right, so does this all sound exciting and want to get started building with Llama 3.2? Well, the entire family of Llama 3.2 models is available for immediate download, we’ve got a link in the show notes, of course. And Llama 3.2 is also available for development across a broad ecosystem of partner platforms. This includes major players like AWS, Google Cloud, Microsoft Azure, and many others. This breadth of support ensures that developers have the flexibility to work with these models in their preferred environments.
(11:34):
Meta has also been working closely with hardware partners like Qualcomm, MediaTek, and Arm to optimize these models for mobile devices. This collaboration ensures that Llama 3.2 can run efficiently on a wide range of mobile hardware, opening up new possibilities for on-device AI.
Meta has also been working closely with hardware partners like Qualcomm, MediaTek, and Arm to optimize these models for mobile devices. This collaboration ensures that Llama 3.2 can run efficiently on a wide range of mobile hardware, opening up new possibilities for on-device AI.
(11:51):
This is a big deal and I’m grateful that Meta continues to invest a huge amount of money, hundreds of millions, billions of dollars, I don’t know, into developing, training and releasing open-source LLMs. From vision capabilities to on-device deployment, these Llama 3.2 models open up new possibilities for developers and end-users alike. We’re likely to see a wave of innovative applications leveraging these models in areas like personalized assistants, content creation tools, and intelligent document processing.
This is a big deal and I’m grateful that Meta continues to invest a huge amount of money, hundreds of millions, billions of dollars, I don’t know, into developing, training and releasing open-source LLMs. From vision capabilities to on-device deployment, these Llama 3.2 models open up new possibilities for developers and end-users alike. We’re likely to see a wave of innovative applications leveraging these models in areas like personalized assistants, content creation tools, and intelligent document processing.
(12:22):
As we do this, as we develop these kinds of applications, you’ve got to remember that thing that I said earlier, with great power comes great responsibility. As developers and data scientists, we need to be mindful of the ethical implications of deploying such powerful AI models. Meta’s safety initiatives like Llama Guard arm us with tools, but ultimately it’s up to us. It’s ultimately up to you to use these tools and others like them to minimize issues like bias, privacy breaches and other naughty LLM possibilities or AI possibilities.
As we do this, as we develop these kinds of applications, you’ve got to remember that thing that I said earlier, with great power comes great responsibility. As developers and data scientists, we need to be mindful of the ethical implications of deploying such powerful AI models. Meta’s safety initiatives like Llama Guard arm us with tools, but ultimately it’s up to us. It’s ultimately up to you to use these tools and others like them to minimize issues like bias, privacy breaches and other naughty LLM possibilities or AI possibilities.
(12:57):
All right, cool. That’s it for today’s episode. If you enjoyed today’s episode or know someone who might, consider sharing link to the episode with them, leave a review of the show on your favorite podcasting platform, tag me in a LinkedIn or Twitter post with your thoughts, I’ll respond to those, and if you aren’t already, be sure to subscribe to the show. But most importantly, however, I hope you’ll just keep on listening. Until next time, keep on rockin’ it out there and I’m looking forward to enjoying another round of the Super Data Science podcast with you very soon.
All right, cool. That’s it for today’s episode. If you enjoyed today’s episode or know someone who might, consider sharing link to the episode with them, leave a review of the show on your favorite podcasting platform, tag me in a LinkedIn or Twitter post with your thoughts, I’ll respond to those, and if you aren’t already, be sure to subscribe to the show. But most importantly, however, I hope you’ll just keep on listening. Until next time, keep on rockin’ it out there and I’m looking forward to enjoying another round of the Super Data Science podcast with you very soon.
