Open Source Models and Hugging Face [Pt 16] | Generative AI for Beginners

Summary

In this lesson from the Generative AI for Beginners course, Corey Cigared Pace from Microsoft introduces open-source models, explaining their benefits, flexibility, and cost advantages. He discusses key open models like Olmo, Llama, and MROL, highlighting their performance, specialization, and the ability to fine-tune for specific tasks. The video covers where to find these models, such as the Azure AI Studio and Hugging Face Hub, and emphasizes the importance of community contributions to model innovation. The lesson concludes with resources to dive deeper into generative AI applications and model lifecycle management.

Takeaways

• 😀 Open source models are defined by publicly available training data, model weights, evaluation code, and an open-source license allowing free use without restrictions.
• 😀 Open models, while not always fully meeting the open-source criteria, offer a lot of flexibility and customization for developers looking to build specialized applications.
• 😀 The benefit of using open models includes their lower cost compared to proprietary models, especially when considering hosting and usage fees.
• 😀 Customizability is one of the top reasons to use open models, as they can be fine-tuned for specific tasks like language translation or code generation.
• 😀 The community-driven nature of open models, particularly platforms like Hugging Face, fosters innovation and offers a wide range of pre-fine-tuned models for specific tasks.
• 😀 Azure AI Studio provides a catalog with over 1,600 open models, including Microsoft research models and Hugging Face models, making it easier for developers to find appropriate models for their applications.
• 😀 Llama 3 models are highly competitive, with variations ranging from 8B to 70B parameters, offering strong performance and speed in specific tasks like image generation.
• 😀 Mrol models stand out for their unique mixture of expert architecture and native function calling, allowing them to run efficiently and offer interactive user experiences.
• 😀 Hugging Face's vast library of 600,000 models allows developers to combine task-specific models to achieve better performance and cost-effectiveness than relying on a single general model.
• 😀 Open models are ideal for applications that require multimodal architecture, as developers can integrate multiple models to achieve specialized functionality for tasks such as language translation or summarization.
• 😀 The overall lesson emphasizes the importance of understanding the specific strengths and limitations of each open model to determine the best fit for various tasks in application development.

Q & A

What are open source models, and how do they differ from proprietary models?

Open source models are AI models where the training data, model weights, evaluation code, and fine-tuning processes are all publicly available. They differ from proprietary models because they offer more freedom and flexibility for developers to customize, use, and even host these models, often at a lower cost. Proprietary models are typically controlled by a single organization and may come with restrictions.

What is the significance of the Olmo models from Allen AI?

The Olmo models from Allen AI are a prime example of open source models. They meet the criteria of being open source by providing publicly available training data, model weights, and evaluation code. The most recent model, a 7 billion parameter open LLM, is known to perform well compared to other models like Llama 2, making it an important choice for developers working with large language models.

Why are open source models more cost-effective than proprietary models?

Open source models are generally more affordable because they are not controlledQ&A generation from script by a single company that sets high usage costs. They often have lower operational costs for both training and deployment, especially when hosted locally or in the cloud. Additionally, many open models come with flexible licensing, which reduces the financial burden compared to proprietary models.

How can open source models be customized or fine-tuned for specific tasks?

Open source models can be fine-tuned by modifying their weights to make them perform better on specialized tasks. For example, you can fine-tune a model to handle a specific language, programming language, or perform certain rulesets. This customization is possible because many open models allow access to the full model weights and fine-tuning code.

What role does the Hugging Face Hub play in working with open models?

The Hugging Face Hub is a central platform where developers can find, share, and fine-tune various open models. It has a large and diverse collection of models, including both general-purpose and task-specific models. The hub supports innovation by allowing community contributions, and it’s particularly useful for finding specialized models that perform well in particular domains or languages.

How does the flexibility of open models benefit developers when building applications?

The flexibility of open models allows developers to tailor models to their specific needs. Whether it's selecting models that excel at specific tasks like text generation or code summarization, or combining multiple models to create a more robust multimodal architecture, open models give developers more control over cost, performance, and scalability.

What is the difference between 'open source models' and 'open models' as discussed in the lesson?

'Open source models' refer to models that meet the full criteria of open source software, including publicly available weights, data, and evaluation code. 'Open models,' as used in the lesson, might not fully meet this strict definition but still offer many of the benefits of openness, such as customizable code and accessible weights, though they may be hosted on platforms like Hugging Face or Meta.

What types of models are available through Azure AI Studio?

Azure AI Studio offers a model catalog that includes over 1,600 models. These models come from various sources, including Microsoft Research and Hugging Face, and cover a wide range of use cases, such as language modeling, code generation, and specialized applications. The catalog is continuously updated with new models to support a broad range of tasks.

How does Llama 3 compare to proprietary models like GPT-4?

Llama 3 is a competitive open source model that includes several variants, such as the 8B and 70B parameter models. It performs well compared to proprietary models like GPT-4, excelling in certain tasks, including image generation. Llama 3 is highly scalable and can be used for both general-purpose and specialized tasks, making it a strong contender for developers looking for cost-effective, high-performance models.

What makes the MROL models unique compared to other open models?

MROL models, particularly the 8X 22B model, are notable for their unique mixture of expert architecture. This design allows the model to select the most appropriate expert neural network for a given task, making the inference process more efficient and lightweight. Additionally, the 8X 22B model includes native function calling, which enables it to interact directly with applications based on user input, offering a more dynamic and user-friendly experience.