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FM/LLM Series

Recap#2: How does FMOps Infrastructure Stack look like?
+ our best explanatory tokens about it, including a list of open-source tools, libraries, and companies
Recap#1 of FMOps: key concepts, techniques, and resources
Systematizing the knowledge about foundation models that are the backbone of generative AI
Token 1.23: Mitigating Bias in Foundation Models/LLMs
Your guide on how to identify bias, a few debiasing techniques, and collection of tools and libraries for detection and mitigation
Token 1.22: Data Privacy in LLM systems
From concerns about data leakage to the main technical strategies for managing privacy in LLMs.
Token 1.20: Explainable AI techniques and tools for LLMs
that will help you decipher these models and their predictions
Token 1.18: How to Monitor LLMs?
Ensuring Your LLMs Deliver Real Value
Token 1.17: Deploying ML Model: Best practices feat. LLMs
Unless you are a researcher whose sole job is to beat benchmarks at some predefined dataset, you will want to deploy your model
Token 1.16: Understanding RLHF and its use cases
Deep dive into a groundbreaking alignment technique with a lot of potential
Mistral AI's Bold Journey
From Paris to Global Stage: The Unconventional Rise of a French AI Unicorn and Its Open-Source Revolution
Token 1.15: What are Hallucinations: a Critical Challenge or an Opportunity?
We explore why hallucinations occur, strategies and methods for identifying them, and if they can be beneficial + a curated list of datasets, libraries…
Token 1.14: What is Synthetic Data and How to Work with it?
Will it eliminate the need for real data? Let's explore
Token 1.13: Where to Get Data for Data-Hungry Foundation Models
Explore how data is gathered to train FMs, learn a few data efficient training techniques and the ethics of data
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