LLMs

A new v0.4.0 release of lm-evaluation-harness is available !

New updates and features include:

• Internal refactoring


• Config-based task creation and configuration


• Easier import and sharing of externally-defined task config YAMLs


• Support for Jinja2 prompt design, easy modification of prompts + prompt imports from Promptsource


• More advanced configuration

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Jail-Breaked & Offline Appliances: It’s becoming increasingly clear that we’ll be able to interact with everyday appliances and devices with natural language. As locally run LLMs become more efficient and powerful, the prospects of having a conversation with your coffee machine in the morning aren’t unreasonable. After all, who wants to tinker with... See more

🤖 Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.

• Why CrewAI


• Getting Started


• Key Features


• Examples


• Local Open Source Models


• CrewAI x AutoGen x ChatDev


• Contribution


• 💬 CrewAI Discord Community


• Hire Consulting


• License

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core components of Deep RL that enabled success like AlphaGo: self-play and look-ahead planning.

Self-play is the idea that an agent can improve its gameplay by playing against slightly different versions of itself because it’ll progressively encounter more challenging situations. In the space of LLMs, it is almost certain that the largest portion... See more

Setting up the necessary machine learning infrastructure to run these big models is another challenge. We need a dedicated model server for running model inference (using frameworks like Triton oder vLLM), powerful GPUs to run everything robustly, and configurability in our servers to make sure they're high throughput and low latency. Tuning the... See more

Humans are bad at coming up with search queries. Humans are good at incrementally narrowing down options with a series of filters, and pointing where they want to go next. This seems obvious, but we keep building interfaces for finding information that look more like Google Search and less like a map.

All information tools have to give users some... 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

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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

Today, we’re releasing the Assistants API, our first step towards helping developers build agent-like experiences within their own applications. An assistant is a purpose-built AI that has specific instructions, leverages extra knowledge, and can call models and tools to perform tasks. The new Assistants API provides new capabilities such as Code... See more