LAION-AI/Open-Assistant: OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so. - LAION-AI/Open-Assistant: OpenAssistant is a c...
mlc-ai/mlc-llm: Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
Enable everyone to develop, optimize and deploy AI models natively on everyone's devices. - mlc-ai/mlc-llm: Enable everyone to develop, optimize and deploy AI models natively on everyone&am...
milvus-io/milvus: An open-source vector database for embedding similarity search and AI applications.
An open-source vector database for embedding similarity search and AI applications. - milvus-io/milvus: An open-source vector database for embedding similarity search and AI applications.
Introducing Chat with Retrieval-Augmented Generation (RAG)
We are excited to announce that our Chat API with RAG is now available in a public beta. With this new capability, developers can integrate user inputs, data sources, and model generations to build powerful product experiences and mitigate hallucinations by producing grounded and verifiable generations. The API is powered
FlowiseAI/Flowise: Drag & drop UI to build your customized LLM flow using LangchainJS
Drag & drop UI to build your customized LLM flow using LangchainJS - FlowiseAI/Flowise: Drag & drop UI to build your customized LLM flow using LangchainJS
QuivrHQ/quivr: 🧠 Dump all your files and chat with it using your Generative AI Second Brain using LLMs ( GPT 3.5/4, Private, Anthropic, VertexAI ) & Embeddings 🧠
baichuan-inc/Baichuan2: A series of large language models developed by Baichuan Intelligent Technology
A series of large language models developed by Baichuan Intelligent Technology - baichuan-inc/Baichuan2: A series of large language models developed by Baichuan Intelligent Technology
**Few-Shot Learning** is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to the Few-Shot Learning problem is to learn a common representation for various tasks and train task specific classifiers on top of this representation.
span class="description-source"Source: [Penalty Method for Inversion-Free Deep Bilevel Optimization ](https://arxiv.org/abs/1911.03432)/span
Everything you need to know about Few-Shot Learning
In this tutorial, we examine the Few-Shot Learning paradigm for deep and machine learning tasks. Readers can expect to learn what it is, different techniques, and details about use cases for Few-Shot Learning