Name: The New Way To Deliver Enterprise AI Applications
Start: 2024-07-22T19:00:00Z
End: 2024-07-22T19:00:09.000Z
Location: BrightTALK
Rating: 0

Customers turn to SambaNova to quickly deploy state-of-the-art generative AI capabilities within the enterprise. Our purpose-built enterprise-scale AI platform is the technology backbone for the next generation of AI computing.

Learn how to build complex reasoning applications that run 10x faster in a 10x smaller footprint on our new architecture that unlocks blazing fast handoffs for chained models on chip. We'll review the current state of the generative AI application market and dig into some of the pain points our customers were seeing at SambaNova Systems. We'll then cover what we built to solve those problems and how it works, what our customers are doing with it, and how you can easily try it yourself.

The New Way To Deliver Enterprise AI Applications

A walkthrough of our AI Starter Kit available on GitHub using Composition of Experts (CoE) models with Langchain. 

The repository provides a Python script and a Jupyter Notebook that demonstrate how to call SambaNova CoE models using the Langchain framework. The script offers different approaches for calling CoE models, including using SambaStudio with a named expert or routing and Sambaverse - a free tool where you can discover, compare and evaluate the best open source models.

AI Starter Kit: Using Composition of Experts Models with Langchain

Despite the widespread availability of LLMs, there remains a substantial gap in their capabilities and availability across diverse languages. One approach to address these issues has been to take an existing pre-trained LLM and continue to train it on new languages. While prior works have experimented with language adaptation, many questions around best practices and methodology have not been covered. 

In this webinar, we present a comprehensive investigation into the adaptation of LLMs to new languages. Our study covers the key components in this process, including vocabulary extension, direct preference optimization and the data scarcity problem for human alignment in low-resource languages. We scale these experiments across 9 languages and 2 parameter scales (7B and 70B), and compare our models against Llama 2, Aya-101, XGLM, BLOOM and existing language experts, outperforming all prior published baselines. 

(All evaluation code and checkpoints are made public to facilitate future research.)

SambaLingo Multilingual NLP: Machine Learning Method to Adapt LLMs to Languages

Generative AI

Artificial Intelligence

Digital Transformation

AI Platform

AI Ops

AI Engineering

AI Tools

Responsible AI

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The New Way To Deliver Enterprise AI Applications

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