Mit Llama-2 und LocalGPT: Chatten Sie mit IHREN eigenen Dokumenten

Mit Llama-2 und LocalGPT: Chatten Sie mit IHREN eigenen Dokumenten

Table of Contents:

1. Introduction
2. Cloning the Repo
3. Creating a Virtual Environment
4. Installing the Required Packages
5. Running the Ingest.py File
6. Running the local_gpt.py File
7. Setting up a New LLM
8. Running the Local GPT File
9. Chatting with the Model
10. Issues and Contributions

Introduction In this article, we will explore how to use the newly released Llama Tube within the Local GPD project. This project allows you to chat with your document on your local device using GPT models, ensuring privacy and security. We will walk you through the step-by-step process of using Llama 2 models to chat with your own datasets, as well as highlight some updates made to this project since its initial release.

Cloning the Repo To get started, you will need to clone the repository. Make sure you have GitHub and Python installed on your local machine. Open a terminal window and navigate to your desired GitHub directory. Clone the repo using the provided link, and then navigate to the cloned folder.

Creating a Virtual Environment Next, create a virtual environment for the project. Use the command createenv followed by the desired environment name (e.g., local_GPT_2) and specify the Python version You want to use. Proceed with the installation process.

Installing the Required Packages Once the virtual environment is set up, install all the required packages for the project. Use the command python -m to ensure you are using the Python associated with the virtual environment. Then, install the packages listed in the requirements.txt file.

Running the Ingest.py File The ingest.py file is used to create embeddings for your own documents or codebase and store them in a vector store. Place your documents in the source_documents folder. To create embeddings, run the command python ingest.py. Depending on your hardware (CPU, Nvidia GPU, or Apple Silicon), you may need to specify the device Type using the --device_type flag. The ingest.py file now supports multi-threading and various hardware options.

Running the local_gpt.py File Use the command python local_gpt.py to run the local_gpt.py file, which allows you to chat with your documents. Specify the device type, model ID, and model base name to set up the desired llm. You can obtain the model ID and model base name from the Hugging Face models repository. The file supports different models, including quantized and ggml formats. Adjust the Context length and maximum number of tokens as desired. Interact with the llm by entering Prompts and receiving responses.

Setting up a New LLM To set up a new llm, provide the model ID and model base name. The model ID can be obtained from the Hugging Face models repository, and the model base name depends on the chosen format (e.g., gptq, ggml). Copy the required information and populate it in the code.

Chatting with the Model You can interact with the llm by entering prompts and receiving responses. Experiment with different prompt templates and chunking processes to improve answer accuracy. Use the --show_sources flag to display the sources used by the model. Exit a chat session by typing "exit". Consider contributing to the project or joining the Discord server for further discussions.

Issues and Contributions If you encounter any issues or have suggestions for improvements, create an issue on GitHub. Contributions and pull requests are welcome. Join the active Discord server to connect with the community and share your experiences and projects built on top of Local GPT.

Article:

How to Use Llama Tube within the Local GPD Project: A Step-by-Step Guide In this article, we will explore the process of using Llama Tube within the Local GPD project to chat with your own document on your local device using GPT models. This project ensures 100% privacy and security, as no data leaves your device. We will walk you through the steps of setting up the project, running the required files, and interacting with the llm. Additionally, we will highlight some updates that have been made to the project since its initial release.

1. Introduction The Local GPD project, with the newly released Llama Tube, allows users to chat with their own document on their local device using GPT models. The project prioritizes privacy and security by ensuring that no data leaves the user's device. In this article, we will guide you through the process of using Llama 2 models to chat with your own datasets, as well as highlight some updates that have been made to this project.

2. Cloning the Repo To get started, you need to clone the Local GPD project repository. Make sure you have GitHub and Python installed on your local machine. Open a terminal window, navigate to your desired GitHub directory, and clone the repository using the provided link. Once cloned, navigate to the cloned folder.

3. Creating a Virtual Environment Before proceeding, create a virtual environment for the project. This helps isolate the project-specific dependencies. Use the command createenv followed by the desired environment name, such as "local_GPT_2". Specify the Python version you want to use. Once entered, the environment will be set up.

4. Installing the Required Packages Now that the virtual environment is set up, you need to install the required packages for the project. Use the command python -m to ensure that you are using the Python associated with the virtual environment. Then, install the packages listed in the requirements.txt file. This will ensure that all the necessary dependencies are installed.

5. Running the Ingest.py File The ingest.py file is used to create embeddings for your own documents or codebase and store them in a vector store. To get started, place your documents in the source_documents folder. To create the embeddings, run the command python ingest.py. Depending on your hardware, such as CPU, Nvidia GPU, or Apple Silicon, you may need to specify the device type using the --device_type flag. The ingest.py file now supports multi-threading and various hardware options, providing a faster and more flexible document ingestion process.

6. Running the local_gpt.py File To chat with your documents, you need to run the local_gpt.py file. Use the command python local_gpt.py to initiate the chat process. Specify the device type, model ID, and model base name to set up the desired llm (local language model). These parameters determine the specific GPT model and configuration to be used. Obtain the model ID and model base name from the Hugging Face models repository. The local_gpt.py file supports different model formats, including quantized and ggml formats. Adjust the context length and maximum number of tokens according to your preferences. Once set up, you can interact with the llm by entering prompts and receiving responses.

7. Setting up a New LLM To use a different language model within the Local GPD project, you need to set up a new LLM. This involves providing the model ID and model base name. The model ID can be obtained from the Hugging Face models repository. The model base name depends on the specific format of the model you want to use, such as gptq or ggml. By adjusting these parameters, you can switch between different models and configurations.

8. Running the Local GPT File To run the Local GPT file with the newly set up llm, use the command python run_local_gpt.py. This will load the specified llm and allow you to chat with your documents. Make sure to specify the correct device type, model ID, and model base name as discussed earlier. The Local GPT file provides a seamless interface for interacting with the llm and obtaining answers to your queries. Experiment with different prompts and conversations to explore the capabilities of the llm.

9. Chatting with the Model Once the Local GPT file is running, you can start chatting with the language model. Enter prompts and questions based on your documents or areas of interest. The llm will generate responses that are contextual and relevant. It's important to note that the accuracy of responses can vary and may require fine-tuning of prompt templates and chunking processes. You can also request the llm to display the sources it used to generate the response by using the --show_sources flag. Exit a chat session by typing "exit". Keep in mind that the llm's performance can be further improved by customizing prompt templates Based on the specific model being used.

10. Issues and Contributions If you encounter any issues or have suggestions for improvements, you can create an issue on the Local GPD project's GitHub repository. The project welcomes contributions and pull requests from the community. If you are using Local GPT for your own projects or have built something interesting on top of it, consider sharing your experiences and join the active Discord server. Engage with other developers and enthusiasts to exchange ideas and collaborate in enhancing the capabilities of the project.

Conclusion Using Llama Tube within the Local GPD project offers a unique and secure way to chat with your own documents using GPT models. By following the steps outlined in this article, you can set up the project on your local machine, create embeddings for your documents, and interact with the llm. Stay engaged with the project's community to stay updated on the latest developments and explore the potential of this powerful tool for natural language processing.