Building Ollama Cloud – Extending Local Inference to the Cloud

Building Ollama Cloud - Extending Local Inference to the Cloud

Are you still troubled by the mixed quality and poor performance of AI in China? Then let’s take a look at Dev Cat AI (3in1). This is an integrated AI assistant that combines GPT-4, Claude3, and Gemini. It covers all models of the three AI tools. Including GPT-4o and Gemini flash Now you can own … Read more

Local Deployment of Ollama for Offline AI Model Usage

Local Deployment of Ollama for Offline AI Model Usage

Ollama is a local large model running framework that allows users to run and utilize large language models (LLM) on their own computers. Its design goal is to simplify the operation of large models, enabling non-professional ordinary users to easily work with these models that typically require high-end hardware and complex setups to run. Currently, … Read more

Using CPU for Inference of Llama Structure Large Models

Using CPU for Inference of Llama Structure Large Models

1. Review of Llama Model Basics The Llama model is built on the Transformer architecture, featuring multiple layers of attention mechanisms that enable deep semantic analysis and feature extraction of input text. This allows it to excel in natural language processing tasks such as text continuation, summarization, and machine translation. Its design philosophy aims to … Read more

The Rise of Next-Gen Language Model Paradigms: LAM Overview

The Rise of Next-Gen Language Model Paradigms: LAM Overview

Reported by New Intelligence Editor: LRS [New Intelligence Overview] Allow language models to solve problems automatically, letting users take a backseat. The wave of AI driven by ChatGPT has ushered us into the era of artificial intelligence, where language models have become indispensable tools in daily life. However, current language models can only respond to … Read more

Mastering LangGraph Tools: Configuring Parameters

Mastering LangGraph Tools: Configuring Parameters

How to Pass Config to the Tools At runtime, we may need to pass values to the tools, such as user ID. For security reasons, this value should be set by application logic rather than controlled by the LLM. The LLM should only manage its expected parameters. The LangChain tools use the Runnable interface, where … Read more

Mastering LangGraph: Controllability 02

Mastering LangGraph: Controllability 02

Map-reduce operations are crucial for efficient task decomposition and parallel processing. This method involves breaking down tasks into smaller sub-tasks, processing each sub-task in parallel, and aggregating the results of all completed sub-tasks. Consider this example: Given a general topic from the user, generate a list of related topics, create a joke for each topic, … Read more