The past few months have seen a reduction in the size of generative models, making personal assistant AI enabled through local computers more accessible. To experiment with different models before using an API model, you can find a variety of models on HuggingFace. Look for models that have been downloaded and liked by many users as a measure of their usefulness. When selecting a model, consider your infrastructure and hardware constraints. Start with smaller problems and gradually work towards your task. HuggingFace provides instructions on downloading and running selected models. You can optimize the model if needed using resources like the GitHub repo for big deep learning optimization.
**How to navigate the sea of models being released every day?**
The past few months have seen a significant reduction in the size of generative models, such as Mistral AI’s new model. This reduction in size allows for the use of personal assistant AI that can be tied to your local computer, ensuring confidential computation on your data. With these new developments, deploying and managing an AI workload has changed. So, how can you use these models and host them on your company’s infrastructure?
Before using an API model hosted by someone else, it’s a good idea to experiment with different types of models to understand how they perform. If you’re not using an API model right away, there are two types of models you can use: proprietary and open access models. Proprietary models have their own API, while open access models have different licenses.
The best place to find these models is on HuggingFace, where you can find over 350,000 models across various tasks. However, not all of these models are actively used or updated. To find the most useful models, you can look at the number of downloads and likes. HuggingFace allows you to filter models by task, license, and popularity, giving you an overview of the available models.
For text generation tasks, the trending model is the Mistral model with 7 billion parameters. When selecting a model, you can click on it to see a model card with an interactive interface and Spaces, which are applications integrated with the model. These interfaces provide a sense of what the models can do.
When selecting and running a model, consider the constraints of your infrastructure and hardware. Models with more than 7 billion parameters may be difficult to run on standard consumer GPUs. However, there are model optimization techniques that can help. It’s a good idea to start with smaller problems and gradually build up to more complex tasks. Once you’ve selected a model, you can follow the instructions on the model card to download and run it. Platforms like Google Colab and Kaggle can be used for running the model and assessing resource usage.
If you need to optimize your model due to hardware constraints, there are resources available, such as a GitHub repo that provides optimization techniques for deep learning. This repo can help you determine if a model is suitable for your hardware requirements.
In summary, selecting and running your own generative model involves experimenting with different models, exploring available models on platforms like HuggingFace, considering hardware constraints, and optimizing the model if necessary. For more in-depth information, you can check out the HuggingFace course on model modifications and deployment.
If you’re interested in using AI to evolve your company and stay competitive, consider following the steps in the guide mentioned above. AI can redefine your work processes and customer engagement, and there are practical AI solutions available, such as the AI Sales Bot from itinai.com/aisalesbot, which automates customer engagement and manages interactions across all stages of the customer journey.
