Introducing Claude 3.5 Sonnet: Anthropic's Fastest and Smartest Model that Outperforms Claude 3 Opus. ??
Welcome to the?AI in 5?newsletter with Clarifai!
Every week we bring you new models, tools, and tips to build production-ready AI!
Here is the summary of what we will be covering this week: ??
Let's go!
Claude 3.5 Sonnet: Latest model from Anthropic AI ??
Claude 3.5 Sonnet is the first release in the 3.5 model family by Anthropic AI and outperforms the previous Claude 3 Opus.
The model excels in complex tasks like autonomous coding and visual processing and also shines when working with long documents, ensuring accuracy in tasks like retrieval-augmented generation (RAG), search and retrieval.?
Key takeaways of the model:
The model is now available on the Clarifai Platform!
Try out Claude 3.5 Sonnet
Florence-2 from Microsoft ??
Florence-2 is the new lightweight vision-language model open-sourced by Microsoft and can handle a variety of vision and vision-language tasks through a prompt-based approach.?
The model demonstrates strong zero-shot capabilities across tasks such as captioning, object detection, grounding, and segmentation.
Florence-2 is available in two variants: Florence-2-base (0.23B parameters) and Florence-2-large (0.77B parameters).?
Below is an example of using the model for an object detection task and getting the bounding box coordinates.?
领英推荐
Live Webinar: AI Prototype to Production ??
Moving past the false finish line of AI prototypes is hard, but Clarifai makes it easier!
Join for the live webinar on July 24 to learn how to move past the false finish line with AI prototypes and into production and the best practices for operationalizing and orchestrating AI.?
Do LLMs Reign Supreme In Few-Shot NER? PART III ??
Named-Entity Recognition (NER) is the task of finding and categorizing named entities in text. In a few-shot scenario, there are only a handful of labeled examples available for training or adapting a NER system.
The following blog goes into the details of using LLMs, especially open-source LLMs like Llama-2, for few-shot NER tasks and also discusses their challenges.
Check out the code here!
AI tip of the week: ??
Batch Predict using the Python SDK
Efficiently process multiple inputs in a single request by leveraging the Predict API's batch prediction feature.?
This allows you to streamline the prediction process, saving time and resources.
The example below showcases how to submit batch inputs to the General Image Recognition model. Check out the code.
Want to learn more from Clarifai? “Subscribe” to make sure you don’t miss the latest news, tutorials, educational materials, and tips. Thanks for reading!