Baseten Blog | Page 11
Three techniques to adapt LLMs for any use case
Prompt engineering, embeddings, vector databases, and fine-tuning are ways to adapt Large Language Models (LLMs) to run on your data for your use case
What I learned from my AI startup’s internal hackathon
See hackathon projects from Baseten for ML infrastructure, inference, user experience, and streaming
Deploy Falcon-40B on Baseten
Deploy Falcon-40B and Falcon-7B, top-ranked open-source LLMs on HuggingFace, to Baseten's production-ready ML infrastructure.
Deploy open-source models in a couple clicks from Baseten’s model library
An explanation of how Baseten's model library works for deploying and serving popular open-source models.
Getting started with foundation models
Summarizing foundation models, focusing on data type, scale, in-context learning, and fine-tuning, illustrated with Meta's LLaMA model family.
New in May 2023
Explore new text generation and text-to-speech models, their GPU requirements, and join the community around open-source models.
Understanding NVIDIA’s Datacenter GPU line
This guide helps you navigate NVIDIA’s datacenter GPU lineup and map it to your model serving needs.
Comparing GPUs across architectures and tiers
So what are reliable metrics for comparing GPUs across architectures and tiers? We’ll consider core count, FLOPS, VRAM, and TDP.
New in April 2023
LLMs go OSS, AI community thrives, Baseten offers free credits to start deploying models
