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HonestLLM

HonestLLM

Installation December 9, 2023

This document provides a step-by-step guide for installing HonsetLLM. Please ensure that your system meets the requirements before proceeding with the installation.

System Requirements

  • Operating System: Windows, macOS, Linux
  • Python Version: 3.6 or higher
  • Other Required Software: git, pip (Python package manager)

Before installation

You’ll need the following API keys

  1. naver news api
  2. open ai api

Installation Steps

1. Clone the Repository

First, clone the project’s GitHub repository.

git clone https://github.com/bamboostreet/HonestLLM.git cd HonsetLLM 

2. Set Up a Virtual Environment

Create and activate a... read more

HonsetLLM

How to use December 8, 2023

Welcome to the official guide on how to use HonsetLLM. This post will cover the basics of getting started with our software and some advanced features that you can leverage to make the most out of it.

Getting Started

Before diving into the details, make sure you have HonsetLLMinstalled.

If you haven’t installed it yet, check out our installation guide.

Initial Setup

After installation, let’s set up the project.

1. import

# imports import pandas as pd # for storing text and embeddings data import... read more
					

artificial intelligence

About our project December 10, 2023

main

Welcome to our open-source project, where we are pioneering a new approach to enhancing the reliability and credibility of responses provided by Large Language Models (LLMs). Our project is rooted in a simple yet powerful idea: ensuring that every response is not only intelligent but also grounded in reality.

The Challenge

The rise of LLMs has revolutionized the way we interact with AI, offering impressive capabilities in generating human-like text. However, one persistent challenge remains – ensuring the accuracy and honesty of the information provided. Traditional models often lack the ability to discern and convey... read more

Theoretical methods December 7, 2023

Method

Our project operates as follows:

1. Vectorize user input query to extract keywords:

def find_keyword(openai : any, question : str) -> str: response = client.chat.completions.create( model="gpt-4-1106-preview", messages=[ #시스템 메시지는 어시스턴트의 동작을 설정(선택사항)  {"role": "system", "content": "A system that extracts only one keyword from a given question."}, #어시스턴트가 응답할 수 있는... read more
					

coding

Theoretical methods December 7, 2023

Method

Our project operates as follows:

1. Vectorize user input query to extract keywords:

def find_keyword(openai : any, question : str) -> str: response = client.chat.completions.create( model="gpt-4-1106-preview", messages=[ #시스템 메시지는 어시스턴트의 동작을 설정(선택사항)  {"role": "system", "content": "A system that extracts only one keyword from a given question."}, #어시스턴트가 응답할 수 있는... read more
					

community

Community December 10, 2023

Welcome to the HonestLLM community! We’re excited to have you join our growing community of contributors, users, and enthusiasts.

Slack

Join our Slack community to connect with fellow contributors and stay updated on the latest discussions and announcements.

Slack

Feel free to introduce yourself, ask questions, and collaborate with other community members on Slack.

Contribution

We welcome contributions from the community! Whether you’re a developer, designer, tester, or documentation enthusiast, there are various ways you can contribute to HonestLLM.

  • Check out our GitHub repository to find issues to work on.
  • Join discussions... read more

contact

Contact December 5, 2023






read more

copyright

About our project December 10, 2023

main

Welcome to our open-source project, where we are pioneering a new approach to enhancing the reliability and credibility of responses provided by Large Language Models (LLMs). Our project is rooted in a simple yet powerful idea: ensuring that every response is not only intelligent but also grounded in reality.

The Challenge

The rise of LLMs has revolutionized the way we interact with AI, offering impressive capabilities in generating human-like text. However, one persistent challenge remains – ensuring the accuracy and honesty of the information provided. Traditional models often lack the ability to discern and convey... read more

creativity

About our project December 10, 2023

main

Welcome to our open-source project, where we are pioneering a new approach to enhancing the reliability and credibility of responses provided by Large Language Models (LLMs). Our project is rooted in a simple yet powerful idea: ensuring that every response is not only intelligent but also grounded in reality.

The Challenge

The rise of LLMs has revolutionized the way we interact with AI, offering impressive capabilities in generating human-like text. However, one persistent challenge remains – ensuring the accuracy and honesty of the information provided. Traditional models often lack the ability to discern and convey... read more

install

Installation December 9, 2023

This document provides a step-by-step guide for installing HonsetLLM. Please ensure that your system meets the requirements before proceeding with the installation.

System Requirements

  • Operating System: Windows, macOS, Linux
  • Python Version: 3.6 or higher
  • Other Required Software: git, pip (Python package manager)

Before installation

You’ll need the following API keys

  1. naver news api
  2. open ai api

Installation Steps

1. Clone the Repository

First, clone the project’s GitHub repository.

git clone https://github.com/bamboostreet/HonestLLM.git cd HonsetLLM 

2. Set Up a Virtual Environment

Create and activate a... read more

machine learning

About our project December 10, 2023

main

Welcome to our open-source project, where we are pioneering a new approach to enhancing the reliability and credibility of responses provided by Large Language Models (LLMs). Our project is rooted in a simple yet powerful idea: ensuring that every response is not only intelligent but also grounded in reality.

The Challenge

The rise of LLMs has revolutionized the way we interact with AI, offering impressive capabilities in generating human-like text. However, one persistent challenge remains – ensuring the accuracy and honesty of the information provided. Traditional models often lack the ability to discern and convey... read more

Theoretical methods December 7, 2023

Method

Our project operates as follows:

1. Vectorize user input query to extract keywords:

def find_keyword(openai : any, question : str) -> str: response = client.chat.completions.create( model="gpt-4-1106-preview", messages=[ #시스템 메시지는 어시스턴트의 동작을 설정(선택사항)  {"role": "system", "content": "A system that extracts only one keyword from a given question."}, #어시스턴트가 응답할 수 있는... read more
					

neural networks

About our project December 10, 2023

main

Welcome to our open-source project, where we are pioneering a new approach to enhancing the reliability and credibility of responses provided by Large Language Models (LLMs). Our project is rooted in a simple yet powerful idea: ensuring that every response is not only intelligent but also grounded in reality.

The Challenge

The rise of LLMs has revolutionized the way we interact with AI, offering impressive capabilities in generating human-like text. However, one persistent challenge remains – ensuring the accuracy and honesty of the information provided. Traditional models often lack the ability to discern and convey... read more

opensource

How to use December 8, 2023

Welcome to the official guide on how to use HonsetLLM. This post will cover the basics of getting started with our software and some advanced features that you can leverage to make the most out of it.

Getting Started

Before diving into the details, make sure you have HonsetLLMinstalled.

If you haven’t installed it yet, check out our installation guide.

Initial Setup

After installation, let’s set up the project.

1. import

# imports import pandas as pd # for storing text and embeddings data import... read more
					

opensource project

Installation December 9, 2023

This document provides a step-by-step guide for installing HonsetLLM. Please ensure that your system meets the requirements before proceeding with the installation.

System Requirements

  • Operating System: Windows, macOS, Linux
  • Python Version: 3.6 or higher
  • Other Required Software: git, pip (Python package manager)

Before installation

You’ll need the following API keys

  1. naver news api
  2. open ai api

Installation Steps

1. Clone the Repository

First, clone the project’s GitHub repository.

git clone https://github.com/bamboostreet/HonestLLM.git cd HonsetLLM 

2. Set Up a Virtual Environment

Create and activate a... read more

python

Theoretical methods December 7, 2023

Method

Our project operates as follows:

1. Vectorize user input query to extract keywords:

def find_keyword(openai : any, question : str) -> str: response = client.chat.completions.create( model="gpt-4-1106-preview", messages=[ #시스템 메시지는 어시스턴트의 동작을 설정(선택사항)  {"role": "system", "content": "A system that extracts only one keyword from a given question."}, #어시스턴트가 응답할 수 있는... read more
					

reinforcement learning

Theoretical methods December 7, 2023

Method

Our project operates as follows:

1. Vectorize user input query to extract keywords:

def find_keyword(openai : any, question : str) -> str: response = client.chat.completions.create( model="gpt-4-1106-preview", messages=[ #시스템 메시지는 어시스턴트의 동작을 설정(선택사항)  {"role": "system", "content": "A system that extracts only one keyword from a given question."}, #어시스턴트가 응답할 수 있는... read more
					

usage

How to use December 8, 2023

Welcome to the official guide on how to use HonsetLLM. This post will cover the basics of getting started with our software and some advanced features that you can leverage to make the most out of it.

Getting Started

Before diving into the details, make sure you have HonsetLLMinstalled.

If you haven’t installed it yet, check out our installation guide.

Initial Setup

After installation, let’s set up the project.

1. import

# imports import pandas as pd # for storing text and embeddings data import... read more
					
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