Using Langchain and GPT-4 to Create a PDF Chatbot

Tutorial: How to Use Langchain to Host FastChat-T5-3B-v1.0 on Runpod

Step 1: Install Required Packages

First, you need to install the necessary packages. Open your terminal or command prompt and run the following commands:

pip3 install langchain
pip3 install fschat

Step 2: Set Up the FastChat Server

To set up the FastChat server, you need to run three commands in separate terminal windows.

In the first terminal, run the following command to start … click here to read

Exploring the Capabilities of ChatGPT: A Summary

ChatGPT is an AI language model that can process large amounts of text data, including code examples, and can provide insights and answer questions based on the text input provided to it within its token limit of 4k tokens. However, it cannot browse the internet or access external links or files outside of its platform, except for a select few with plugin access.

Users have reported that ChatGPT can start to hallucinate data after a certain point due to its token … click here to read

Toolkit-AI: A Powerful Toolkit for Generating AI Agents

In the ever-evolving realm of artificial intelligence, developers constantly seek to create intelligent and efficient AI agents that automate tasks and engage with users meaningfully. Toolkit-AI emerges as a potent toolkit, empowering developers to achieve this objective by equipping them with tools for generating AI agents that excel in both intelligence and efficacy.

What is Toolkit-AI?

Toolkit-AI, a Python library, allows developers to generate AI agents that harness either Langchain plugins or ChatGPT … click here to read

ChatGPT and the Future of NPC Interactions in Games

Fans of The Elder Scrolls series might remember Farengar Secret-Fire, the court wizard of Dragonsreach in Skyrim. His awkward voice acting notwithstanding, the interactions players had with him and other NPCs were often limited and repetitive. However, recent developments in artificial intelligence and natural language processing might change that. ChatGPT, a language model based on OpenAI's GPT-3.5 architecture, can simulate human-like conversations with players and even remember past interactions. With further development, NPCs in future games could have unique goals, decisions, … click here to read

Chat with Github Repo - A Python project for understanding Github repositories

Chat with Github Repo is an open-source Python project that allows you to chat with any Github repository and quickly understand its codebase. The project was created using Streamlit , OpenAI GPT-3.5-turbo, and Activeloop's Deep Lake.

The project works by scraping a Github repository and embedding its codebase using Langchain, storing the embeddings in Deep Lake. … click here to read

Building an AI-Powered Chatbot using lmsys/fastchat-t5-3b-v1.0 on Intel CPUs

Discover how you can harness the power of lmsys/fastchat-t5-3b-v1.0 language model and leverage Intel CPUs to build an advanced AI-powered chatbot. Let's dive in!

Python Code:

 # Installing the Intel® Extension for PyTorch* CPU version python -m pip install intel_extension_for_pytorch # Importing the required libraries import torch from transformers import T5Tokenizer, AutoModelForSeq2SeqLM import intel_extension_for_pytorch as ipex # Loading the T5 model and tokenizer tokenizer = T5Tokenizer.from_pretrained("lmsys/fastchat-t5-3b-v1.0") model = AutoModelForSeq2SeqLM.from_pretrained("lmsys/fastchat-t5-3b-v1.0", low_cpu_mem_usage=True) # Setting up the conversation prompt prompt …
                        click here to read

Local Language Models: A User Perspective

Many users are exploring Local Language Models (LLMs) not because they outperform ChatGPT/GPT4, but to learn about the technology, understand its workings, and personalize its capabilities and features. Users have been able to run several models, learn about tokenizers and embeddings , and experiment with vector databases . They value the freedom and control over the information they seek, without ideological or ethical restrictions imposed by Big Tech. … click here to read

Exploring GPT-4, Prompt Engineering, and the Future of AI Language Models

In this conversation, participants share their experiences with GPT-4 and language models, discussing the pros and cons of using these tools. Some are skeptical about the average person's ability to effectively use AI language models, while others emphasize the importance of ongoing learning and experimentation. The limitations of GPT-4 and the challenges in generating specific content types are also highlighted. The conversation encourages open-mindedness and empathy towards others' experiences with AI language models. An official … click here to read

LLaVA: Large Language and Vision Assistant

The paper presents the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data. By instruction tuning on such generated data, the authors introduce LLaVA, an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.

LLaVA demonstrates impressive multimodel chat abilities and yields an 85.1% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset. When fine-tuned on Science QA, the synergy of LLaVA and … click here to read

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