Leveraging MCP for automating your daily routine. This article explores the Model Context Protocol (MCP) and demonstrates how to build a toolkit for analysts using it, including creating a local MCP server with useful tools and integrating it with AI tools like Claude Desktop.
This course provides an introduction to the Model Context Protocol (MCP), covering its theory, design, and practical application. It includes foundational units, hands-on exercises, use case assignments, and collaboration opportunities. The course aims to equip students with the knowledge and skills to build AI applications leveraging external data and tools using MCP standards.
A library for working with prompt templates locally or on the Hugging Face Hub. It aims to provide a standardized way of sharing and using prompt templates, with a focus on interoperability and modularity.
This article details the creation of a simple, 50-line agent using Model Context Protocol (MCP) and Hugging Face's tools, demonstrating how easily agents can be built with modern LLMs that support function/tool calling.
1. **MCP Overview**: MCP is a standard API for exposing tools that can be integrated with Large Language Models (LLMs).
2. **Implementation**: The author explains how to implement a MCP client using TypeScript and the Hugging Face Inference Client. This client connects to MCP servers, retrieves tools, and integrates them into LLM inference.
3. **Tools**: Tools are defined with a name, description, and parameters, and are passed to the LLM for function calling.
4. **Agent Design**: An agent is essentially a while loop that alternates between tool calling and feeding tool results back into the LLM until a specific condition is met, such as two consecutive non-tool messages.
5. **Code Example**: The article provides a concise 50-line TypeScript implementation of an agent, demonstrating the simplicity and power of MCP.
6. **Future Directions**: The author suggests experimenting with different models and inference providers, as well as integrating local LLMs using frameworks like llama.cpp or LM Studio.
1. **MCP Overview**: MCP is a standard API for exposing tools that can be integrated with Large Language Models (LLMs).
2. **Implementation**: The author explains how to implement a MCP client using TypeScript and the Hugging Face Inference Client. This client connects to MCP servers, retrieves tools, and integrates them into LLM inference.
3. **Tools**: Tools are defined with a name, description, and parameters, and are passed to the LLM for function calling.
4. **Agent Design**: An agent is essentially a while loop that alternates between tool calling and feeding tool results back into the LLM until a specific condition is met, such as two consecutive non-tool messages.
5. **Code Example**: The article provides a concise 50-line TypeScript implementation of an agent, demonstrating the simplicity and power of MCP.
6. **Future Directions**: The author suggests experimenting with different models and inference providers, as well as integrating local LLMs using frameworks like llama.cpp or LM Studio.
Google releases Gemma 3, a new iteration of their Gemma family of models. It ranges from 1B to 27B parameters, supports up to 128k tokens, accepts images and text, and supports 140+ languages. This article details its technical enhancements (longer context, multimodality, multilinguality) and provides information on inference with Hugging Face transformers, on-device deployment, and evaluation.
This Space demonstrates a simple method for embedding text using a LLM (Large Language Model) via the Hugging Face Inference API. It showcases how to convert text into numerical vector representations, useful for semantic search and similarity comparisons.
This tutorial demonstrates how to build a powerful document search engine using Hugging Face embeddings, Chroma DB, and Langchain for semantic search capabilities.
Reid Hoffman and Clement Delangue are among the signatories of a new open letter calling for the creation of public data sets and incentives to develop 'small' AI models. The letter aims to encourage collaboration among governments, tech companies, and civil society groups to harness the benefits of AI while mitigating its risks.
Qwen2.5-VL-3B-Instruct is the latest addition to the Qwen family of vision-language models by Hugging Face, featuring enhanced capabilities in understanding visual content and generating structured outputs. It is designed to directly interact with tools and use computer and phone functions as a visual agent. Qwen2.5-VL can comprehend videos up to an hour long and localize objects within images using bounding boxes or points. It is available in three sizes: 3, 7, and 72 billion parameters.
Hugging Face researchers developed an open-source AI research agent called 'Open Deep Research' in 24 hours, aiming to match OpenAI's Deep Research. The project demonstrates the potential of agent frameworks to enhance AI model capabilities, achieving 55.15% accuracy on the GAIA benchmark. The initiative highlights the rapid development and collaborative nature of open-source AI projects.
