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Taming LLM Outputs

Taming LLM Outputs

Your Guide to Structured Text Generation
Go to the profile of Vivien Tran Thien
Vivien Tran Thien
A Tour of Popular Open Source Frameworks for LLM-Powered Agents

A Tour of Popular Open Source Frameworks for LLM-Powered Agents

One of the most interesting Generative AI trends is the development of agents powered by large language models (LLMs). The word “agent”…
Go to the profile of Loic Vanel Tabueu Tagne
Loic Vanel Tabueu Tagne
Retrieval Augmented ML: How Can You Best Leverage a Data Lake?

Retrieval Augmented ML: How Can You Best Leverage a Data Lake?

An Open Source Pipeline for Benchmarking the Join Suggestion for ML
Go to the profile of Riccardo Cappuzzo
Riccardo Cappuzzo
Beyond Text: Taking Advantage of Rich Information Sources With Multimodal RAG

Beyond Text: Taking Advantage of Rich Information Sources With Multimodal RAG

Retrieval augmented generation (RAG) has become a very popular approach for creating question-answering systems based on specific document…
Go to the profile of Vivien Tran Thien
Vivien Tran Thien
From Sketch to Success: Strategies for Building and Evaluating an Advanced RAG System

From Sketch to Success: Strategies for Building and Evaluating an Advanced RAG System

By integrating domain-specific knowledge into a Large Language Model (LLM), Retrieval Augmented Generation (RAG) enables the generation of…
Go to the profile of Caroline Boudier
Caroline Boudier
Demystifying Multimodal LLM

Demystifying Multimodal LLM

Unlocking the Power of Fusion in Language and Vision
Go to the profile of François Phe
François Phe
Standing on the shoulders of a giant

Standing on the shoulders of a giant

Leveraging the web to answer open-ended questions
Go to the profile of Vivien Tran Thien
Vivien Tran Thien
Quantum Leap: Beyond the Limits of Machine Learning

Quantum Leap: Beyond the Limits of Machine Learning

Quantum Computers as AI Accelerators
Go to the profile of Simona Maggio
Simona Maggio
Quantization in LLMs: Why Does It Matter?

Quantization in LLMs: Why Does It Matter?

As more open source models are released and begin to rival the quality of proprietary models like ChatGPT, many practitioners want to test…
Go to the profile of Aimee Coelho
Aimee Coelho
Parameter Efficient LLM Fine-Tuning

Parameter Efficient LLM Fine-Tuning

In this three-part series, we’ll unpack several technical topics that have made their way into the spotlight as a result of the increased…
Go to the profile of Louis Fouquet
Louis Fouquet
From Chatbots to Agents: Augmenting LLMs With Tools

From Chatbots to Agents: Augmenting LLMs With Tools

Large language models (LLMs) excel at generating coherent and credible continuations of input texts and this ability can be used to…
Go to the profile of Timothee Weis
Timothee Weis
Tackling Imbalanced Learning With Generative Synthesizers

Tackling Imbalanced Learning With Generative Synthesizers

In the landscape of imbalanced classification, the limitations of traditional oversampling approaches have become increasingly evident. In…
Go to the profile of Ines Ibnukhsein
Ines Ibnukhsein
Joining the Dots Efficiently: Scaling Set Matching With Lazo and MinHashLSH

Joining the Dots Efficiently: Scaling Set Matching With Lazo and MinHashLSH

Introduction
Go to the profile of Du Phan
Du Phan
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