LLMs
TorchMultimodal (Beta Release)
Introduction
TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale. It provides:
Introduction
TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale. It provides:
- A repository of modular and composable building blocks (models, fusion layers, loss functions, datasets and utilities).
- A repository of examples that show how to combine these building
facebookresearch • GitHub - facebookresearch/multimodal at a33a8b888a542a4578b16972aecd072eff02c1a6
Langfuse is an open source observability & analytics solution for LLM-based applications. It is mostly geared towards production usage but some users also use it for local development of their LLM applications.
Langfuse is focused on applications built on top of LLMs. Many new abstractions and common best practices evolved recently, e.g. agents,... See more
Langfuse is focused on applications built on top of LLMs. Many new abstractions and common best practices evolved recently, e.g. agents,... See more
langfuse • GitHub - langfuse/langfuse: Open source observability and analytics for LLM applications
Overview
Loki is our open-source solution designed to automate the process of verifying factuality. It provides a comprehensive pipeline for dissecting long texts into individual claims, assessing their worthiness for verification, generating queries for evidence search, crawling for evidence, and ultimately verifying the claims. This tool is... See more
Loki is our open-source solution designed to automate the process of verifying factuality. It provides a comprehensive pipeline for dissecting long texts into individual claims, assessing their worthiness for verification, generating queries for evidence search, crawling for evidence, and ultimately verifying the claims. This tool is... See more
Libr-AI • GitHub - Libr-AI/OpenFactVerification: Open-source solution designed to automate the process of verifying factuality
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
It's a powerful tool that helps data scientists save time and effort drilling down on model issues, and produce... See more
Giskard-AI • GitHub - Giskard-AI/giskard: 🐢 The testing framework for ML models, from tabular to LLMs
Developers can now generate human-quality speech from text via the text-to-speech API. Our new TTS model offers six preset voices to choose from and two model variants, tts-1 and tts-1-hd . tts is optimized for real-time use cases and tts-1-hd is optimized for quality. Pricing starts at $0.015 per input 1,000 characters. Check out our TTS guide to... See more
New models and developer products announced at DevDay
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
Developing Rapidly with Generative AI
In addition to using our built-in capabilities, you can also define custom actions by making one or more APIs available to the GPT. Like plugins, actions allow GPTs to integrate external data or interact with the real-world. Connect GPTs to databases, plug them into emails, or make them your shopping assistant. For example, you could integrate a... See more
Introducing GPTs
Two ways for an AI company to protect itself from competition: (a) depend not just on AI but also deep domain knowledge about a particular field, (b) have a very close relationship with the end users.
Paul Graham • Tweet
Ensuring availability during peak traffic by maintaining all GPU instance types could lead to prohibitively high costs. To avoid the financial strain of idle instances, we implemented a “standby instances” mechanism. Rather than preparing for the maximum potential load, we maintained a calculated number of standby instances that match the... See more