LLMs

Overview

MaxText is a high performance , highly scalable , open-source LLM written in pure Python/Jax and targeting Google Cloud TPUs and GPUs for training and inference . MaxText achieves high MFUs and scales from single host to very large clusters while staying simple and "optimization-free" thanks to the power of Jax and the XLA compiler.

MaxText... See more

What’s the best way for an end user to organize and explore millions of latent space features?

I’ve found tens of thousands of interpretable features in my experiments, and frontier labs have demonstrated results with a thousand times more features in production-scale models. No doubt, as interpretability techniques advance, we’ll see feature maps... See more

Giskard is a Python library that automatically detects vulnerabilities of AI models, from tabular models to LLM, including: performance biases, data leakage, spurious correlation, hallucination, toxicity, security issues and many more.

It's a powerful tool that helps data scientists save time and effort drilling down on model issues, and produce... See more

First time here? Go to our setup guide

Features

• 🤖 Multiple model integrations: OpenAI, transformers, llama.cpp, exllama2, mamba


• 🖍️ Simple and powerful prompting primitives based on the Jinja templating engine


• 🚄 Multiple choices, type constraints and dynamic stopping


• ⚡ Fast regex-structured generation


• 🔥 Fast JSON generation following a JSON schema

... See more

Menlo Ventures released a report on ‘The State of Generative AI in the Enterprise’ and found that adoption is trailing the hype. Details below:

Generative AI still represents less than 1% of cloud spend by surveyed enterprises, including just an 8% increase in 2023.

Safety and ROI continue to be prime concerns, and the tangible advantages of being... See more

When it comes to identifying where generative AI can make an impact, we dig into challenges that commonly:

• Involve analysis, interpretation, or review of unstructured content (e.g. text) at scale


• Require massive scaling that may be otherwise prohibitive due to limited resources


• Would be challenging for rules-based or traditional ML approaches

I’ve been giving talks and speaking with engineers and non-technical audiences about interpretability since 2022, and I still struggle to explain exactly what a “feature” is. I often use words like “concept” or “style”, or establish metaphors to debugging programs or making fMRI scans of brains. Both metaphors help people outside of the subfield... See more