Running large language models locally on a laptop is becoming increasingly feasible, and Ollama makes it accessible. The key to a good experience is choosing a model that matches your laptop’s hardware, primarily its RAM… Ollama models that can be run on a laptop
LangChain is an open-source framework designed to simplify the creation of applications powered by large language models (LLMs). Available in both Python and JavaScript, it provides a modular and extensible architecture that allows developers to… LangChain: The Powerhouse Behind Intelligent Language Applications
LlamaIndex is a powerful and flexible open-source data framework designed to connect custom data sources to large language models (LLMs). In essence, it acts as a crucial bridge, enabling developers to build applications that can… LlamaIndex: Bridging the Gap Between Your Data and Large Language Models
Resubmitting a rejected paper is a common part of the academic publishing process. Approaching it systematically can dramatically increase your chances of success at a new venue. Here are the key things to note, broken… Importance Things to Notice when Resubmitting a Rejected Paper
In the rapidly advancing world of artificial intelligence, Large Language Models (LLMs) have emerged as powerful tools capable of generating human-like text, translating languages, and answering questions with remarkable fluency. However, these sophisticated models are… The Specter in the Machine: Understanding Hallucinations in Large Language Models
Retrieval-Augmented Generation (RAG) is a powerful technique that enhances the capabilities of Large Language Models (LLMs) by connecting them to external knowledge sources. According to Google Developer website, EmbeddingGemma is a compact, open‑source embedding model… RAG with EmbeddingGemma with Python Code using Ollama
Each of the following templates is followed by a download link on Xmind. To download, click the link, and then click on the “…” button on the top right corner of the page. Tables with… Professional Xmind chart templates for writing Grants, Proposals, etc.
The AdamW method was proposed in the paper “Decoupled Weight Decay Regularization” by Ilya Loshchilov and Frank Hutter. While the paper was officially published at the prestigious International Conference on Learning Representations (ICLR) in 2019,… AdamW optimization and implementation in PyTorch