Stefania Alborghetti

Let's geek out on OSS vs. closed LLMs for a minute: The #AIatMeta team from Meta just released Chameleon, an open-source multi-modal LLM, sharing "Chameleon is a family of early-fusion token-based mixed-modal models, showcasing broad and general capabilities across various tasks involving images and text." So what does that mean for a business person, exactly? Let's ask the Storytell Chrome extension to de-mystify it for us. Here's the prompt: "I'm a business person who's AI curious but not an LLM expert. I want to use AI to help me and my teams be more productive at work. Explain to me how Chameleon could help do that using non-technical language that my business team can understand." If you want to try the prompt yourself: Here's the technical paper on Chameleon https://lnkd.in/esDdQK3G and you can get the Chrome extension here: https://lnkd.in/ditHm-bd Here's the response to the prompt from Storytell: Chameleon is a cutting-edge AI model that excels at understanding and generating both images and text in a seamless manner. It has been specifically designed to combine visual and textual information effectively, making it highly versatile for a wide range of tasks such as visual question answering, image captioning, text generation, and more. By leveraging Chameleon in your business operations, you can enhance productivity and efficiency in various ways. For example, Chameleon can assist in automating tasks that involve analyzing and generating content based on both images and text. This can streamline processes such as content creation, data analysis, and decision-making by providing valuable insights and generating accurate outputs. Furthermore, Chameleon's ability to outperform other models in text-only tasks and compete with top-performing models in mixed-modal tasks means that it can deliver high-quality results across different types of projects. This can lead to improved accuracy, faster turnaround times, and enhanced overall performance for your business teams. In summary, incorporating Chameleon into your AI toolkit can empower your teams to work more efficiently, make better-informed decisions, and achieve greater productivity in their day-to-day tasks.

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A very useful checklist to follow when you have to put in production Transformer based models

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Cat qubits have a wonderful property: they can exponentially suppress bit-flip errors while only linearly increasing phase-flip errors. With bit-flip lifetime surpassing 7 minutes, Boson 4 beats any other superconducting qubit. #quantumcomputing #qubit https://lnkd.in/g6nyG8jr

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I just got my own copy of “Building LLMs for Production” by Louis-François Bouchard and Louie Peters. As I am getting more into applications of GenAI I expect this book to become one of my go-to resources in the upcoming months. https://amzn.to/3Yv5FmL #AI #GenAI #LLMs

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Unit Tests Part 2: Cartesian Parameterization! TIL about multiplying parameterizations in pytest. Now, we all know and love pytests parameterization, where you can pass in parameters to a test and thereby re-use the same test code for multiple situations. For example you can pass in these parameters: @pytest.mark.parametrize("input, output", [('hello', 'world'), ('goodbye', 'mom')]) Then in your pytest you will get these two parameter scenarios to use as variables and execute your tests against. - input: hello, output: world - input: goodbye, output: mom It's so sweet I can taste it just writing it! Now - I had no idea but I found out (thank you ChatGPT) today that you can multiple those parameters to get a cartesian product of the sets which will give you every possible permutations of your test parameters. So if you do this: @pytest.mark.parametrize("fruit", ['apple', 'orange']) @pytest.mark.parametrize("ripeness", ['underripe', 'ripe']) You will get these test cases: - fruit: apple, ripeness: underripe - fruit: apple, ripeness: ripe - fruit: orange, ripeness: underripe - fruit: orange, ripeness: ripe This might seem trivial at first - but this is so powerful for test code re-use and flexibility. Below you can see how in the actual test code I am using for one of my projects: At first we had one file to render with 4 different parameter sets. Easy! Then we added another file but we want to test the same 4 parameters against each file rendering. Super easy with pytest, check it out and I hope you try it yourself. That's all for now, go forth and code with delight!

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Woo! Anthropic has finally made Claude/Claude Pro available to Canadians. Very happy to have direct access to it for testing. I also like the fact that they explicitly say they don't train on the data from interactions. For reference; "By default (and just like our beta version of Claude.ai), we will not use your prompts and conversations from Claude Pro to train our models. There are two instances in which we may use your prompts and conversations to train our models: (1) if you give us explicit permission by submitting feedback through the thumbs up/down feature or by reaching out to us with a request, and (2) where your prompts and conversations are flagged for trust and safety review, we may use or analyze those conversations to improve our ability to detect and enforce Usage Policy violations, including to train Trust and Safety classifiers in order to make our services safer. Only a limited number of staff members have access to conversation data and they will only access this data for explicit business purposes." #anthropic #ai #claude #claudepro #Canada #access

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