Source: DeepHub IMBA This article is about 1100 words long, and it is recommended to read in 5 minutes. In this article, we will explore some issues related to Langchain and consider some alternative frameworks. Langchain has gained attention for its ability to simplify interactions with large language models (LLM). With its advanced API, it … Read more
This article will introduce the LangChain framework and explain its functions with Python code examples, utilizing the OpenAI API, along with code and results. Interested readers can follow along with the code. What is LangChain Framework LangChain is a framework for building and deploying applications based on language models (such as large language models, LLMs). … Read more
Currently, various large language models (LLM) are experiencing explosive development, and applications based on LLM are continuously emerging.However, when developers create downstream applications based on LLM, the results generated directly by LLM have many uncertainties in terms of format and content, making it difficult to interact with other business logic code, leading developers to generate … Read more
Source: DeepHub IMBA This article is approximately 2300 words long and is recommended for a 10-minute read. This article introduces how to run LLM on high-performance CPU using the llama.cpp library in Python. Large Language Models (LLM) Are Becoming Increasingly Popular, But They Require A Lot Of Resources, Especially GPU. Large language models (LLM) are … Read more
Source: DeepHub IMBA This article is about 4000 words, and it is recommended to read in 5 minutes. In this article, I will demonstrate how to create your own Document Assistant from scratch using LLaMA 7b and Langchain. In the past few months, large language models (LLMs) have gained tremendous attention, creating exciting prospects, especially … Read more
When discussing strategies for large language models (LLMs), there are generally two types: System 1 (fast reaction) and System 2 (slow thinking). System 2 reasoning tends to involve more deliberate thought, generating intermediate reasoning that allows the model (or human) to reason and plan in order to successfully complete tasks or respond to instructions. Effortful … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and PhD students, university teachers, and researchers from enterprises. The vision of the community is to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning, especially for beginners. … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university teachers, and corporate researchers. The Vision of the Community is to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning, especially for beginners. Reprinted … Read more
Source: DeepHub IMBA This article is approximately 3300 words long and is recommended for a 5-minute read. In this article, I will use two frameworks to complete some basic tasks in parallel. When using large models locally, especially when building RAG applications, there are generally two mature frameworks available: LangChain: A general framework for developing … Read more
BioRAG: A RAG-LLM Framework for Biological Question Reasoning The question-answering systems in the life sciences face challenges such as rapid discovery, evolving insights, and complex interactions of knowledge entities, necessitating a comprehensive knowledge base and precise retrieval. To address this, we introduce BioRAG, a retrieval-augmented generation framework that combines large language models. First, we parse, … Read more