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Open GPT
December 04, 20723
LLMS Overview in OpenGPTs
The LLMs (Large Language Models) section in OpenGPTs focuses on the selection and usage of various advanced language models. This part of OpenGPTs allows users to choose from a diverse range of LLMs offered by LangChain, each with unique capabilities and features. The flexibility in selecting LLMs is a key aspect of OpenGPTs, enabling users to customize the performance and response style of their chatbots. This section is crucial for understanding how different LLMs can be integrated and utilized in creating more efficient, responsive, and tailored chatbot experiences.
WHAT IS OPENGPT
https://github.com/langchain-ai/opengpts
OpenGPTs is an open-source project aimed at replicating the experience of OpenAI's GPTs and Assistants API. It leverages LangChain, LangServe, and LangSmith to provide enhanced control and customization options. Users can select from over 60 language models (LLMs) offered by LangChain, use different prompts, choose from a wide range of tools (or write their own), select from various vector database integrations, and configure retrieval algorithms and chat history databases [[❞]](https://github.com/langchain-ai/opengpts).
Key features of OpenGPTs include a sandbox for testing and modifying chatbots, custom actions defined using OpenAPI specifications, the ability to attach knowledge files for chatbot reference, and tools for web browsing and image creation. It also includes analytics for chatbot usage, drafts for saving chatbot configurations, options for publishing and sharing chatbots, and a marketplace for deploying chatbots created by other users [[❞]](https://github.com/langchain-ai/opengpts).
The repository structure consists of a frontend for user interface and a backend containing the core logic, including LangServe code for API exposure and packages for agent runtime and configuration [[❞]](https://github.com/langchain-ai/opengpts#:~:text=,agent%60%3A%20Configuration%20for%20the%20agent).
OpenGPTs stands out for its customizability, allowing users to choose LLMs, add custom tools, and even build custom UIs. Default LLM options include "GPT 3.5 Turbo," "GPT 4," "Azure OpenAI," and "Claude 2." Users can also add their own LLMs or modify existing configurations [[❞]](https://github.com/langchain-ai/opengpts).
For tool integration, OpenGPTs supports various search tools like DuckDuckGo Search, Tavily Search, You.com Search, and tools for searching SEC filings and press releases through Kay.ai, among others. These tools can be easily added and customized within LangChain [[❞]](https://github.com/langchain-ai/opengpts).
Deployment can be done to Google Cloud Run, with detailed steps provided for setting up the environment and deploying the application [[❞]](https://github.com/langchain-ai/opengpts).
LangChain
LangChain is a library designed to facilitate the development of applications using large language models (LLMs). It provides tools and integrations that enable developers to build more complex and sophisticated systems leveraging LLMs. LangChain supports a variety of LLMs and offers features for handling different components of a language model-based application. This includes managing chat histories, integrating various external tools, and implementing retrieval algorithms. By using LangChain, developers can create more advanced and versatile applications that harness the full potential of large language models.
https://smith.langchain.com/o/7b2ce6a4-ab27-55c4-8943-214725160c7c/
https://opengpts-example-vz4y4ooboq-uc.a.run.app/?shared_id=3b2ab4da-4744-4c8c-ba7c-63c868232604
LangChain on-premises
Setting up LangChain on-premises involves a series of steps that typically include:
1. System Requirements: Ensure your system meets the requirements for running LangChain, including necessary hardware and software capabilities.
2. Installation: Download and install LangChain from its source. This might involve cloning a repository or downloading packages.
3. Configuration: Configure LangChain according to your specific needs. This includes setting up the environment, configuring language models, and integrating other tools or databases.
4. Testing and Deployment: After setup, it's crucial to test the system to ensure everything is functioning as expected before moving to a production environment.
For detailed instructions, it's best to refer to the official LangChain documentation or repository, as it will provide specific steps and requirements for a successful on-premises setup.
What is LangChain
LangChain is a framework designed for building applications powered by language models (LLMs). It focuses on composability, allowing developers to create applications that are context-aware and capable of reasoning based on the context provided. The framework comprises several components:
1. **LangChain Libraries**: These include Python and JavaScript libraries, which contain interfaces and integrations for various components, a basic runtime for combining components into chains and agents, and pre-built implementations of these chains and agents.
2. **LangChain Templates**: A collection of deployable reference architectures for various tasks.
3. **LangServe**: A library for deploying LangChain chains as a REST API.
4. **LangSmith**: A unified developer platform for building, testing, evaluating, and monitoring LLM applications, integrating seamlessly with LangChain [[❞]](https://github.com/langchain-ai/langchain).
LangChain enables the development of applications like retrieval augmented generation, structured data analysis, and chatbots. It provides documentation and examples for these use cases, assisting developers in creating a wide array of applications [[❞]](https://github.com/langchain-ai/langchain).
The main benefits of LangChain are its modular, composable tools and integrations for working with LLMs, and off-the-shelf chains for accomplishing higher-level tasks. These features simplify both starting new projects and customizing existing ones [[❞]](https://github.com/langchain-ai/langchain).
For detailed guidance, LangChain's documentation offers insights into getting started, setting up the environment, interfaces, modules, integrations, use case walkthroughs, and best practices [[❞]](https://github.com/langchain-ai/langchain#:~:text=,com%E3%80%91%3A%20full%20API%20docs).
What is LangSmith?
Cantinho de .Net Repo
https://github.com/icpmtech/opengpts
Llama
LLaMA, or Large Language Model Meta AI, is a family of state-of-the-art large language models (LLMs) released by Meta AI. These models are part of Meta's commitment to open science and are designed to assist researchers in advancing work in AI. The LLaMA models vary in size, including versions with 7, 13, 33, and 65 billion parameters. Notably, the 13 billion parameter model has shown performance on most NLP benchmarks that exceeds that of the much larger GPT-3, which has 175 billion parameters.
A specific variant, Code Llama, built on top of Llama 2, is a large language model capable of generating code and natural language about code from both code and natural language prompts. It is available for both research and commercial use and comes in three models: Code Llama (the foundational code model) and Codel Llama (specialized for Python).
Meta AI has made LLaMA available under a non-commercial license, primarily targeting researchers and entities affiliated with government, civil society, and academia [[❞]](https://ai.meta.com/blog/large-language-model-llama-meta-ai/) [[❞]](https://ai.meta.com/blog/code-llama-large-language-model-coding/#:~:text=Code%20Llama%20is%20a%20state,Python%20specialized%20for) [[❞]](https://en.wikipedia.org/wiki/LLaMA) [[❞]](https://www.reuters.com/technology/meta-launch-ai-language-model-llama-2023-02-24/#:~:text=Meta%27s%20LLaMA%2C%20short%20for%20Large,academia%2C%20it%20said%20in).
How to Setup in Local Environment
Setting up LLaMA (Large Language Model Meta AI) on a local machine involves several steps, including downloading the model, ensuring you have the necessary hardware and software requirements, and configuring the environment. Here's a general outline of the process:
1. **Hardware Requirements**:
- A powerful CPU and a high-end GPU are recommended for running large language models efficiently.
- Adequate RAM and storage space to handle the model's size and computations.
2. **Software Requirements**:
- Python: Install the latest version of Python.
- PyTorch: LLaMA is likely built on PyTorch, so install the appropriate version of PyTorch that is compatible with your system.
3. **Download LLaMA**:
- Visit Meta AI's official website or GitHub repository to download the LLaMA model. Make sure to download the version that best suits your computational capabilities (7B, 13B, 33B, or 65B parameters).
- Be aware of the license agreement and usage restrictions, especially for non-commercial purposes.
4. **Environment Setup**:
- Create a Python virtual environment to manage dependencies.
- Install necessary libraries and dependencies, such as NumPy, Transformers (if used), and any other required packages.
5. **Load the Model**:
- Write a Python script or use an interactive Python environment (like Jupyter Notebook) to load the LLaMA model.
- Use the appropriate loading functions and specify the model's file path.
6. **Testing the Model**:
- Once the model is loaded, run some test queries to ensure it is functioning correctly.
- You can start with simple prompts and gradually move to more complex ones.
7. **Optimizations and Tuning**:
- Depending on your hardware, you might need to optimize the model for better performance.
- This could include adjusting batch sizes, using mixed-precision computation, or distributing the workload across multiple GPUs if available.
8. **Documentation and Community Support**:
- Refer to the official documentation provided by Meta AI for specific instructions and best practices.
- Consider engaging with the community through forums or GitHub for troubleshooting and tips.
9. **Regular Updates**:
- Keep an eye on updates or patches released by Meta AI and update your local setup accordingly.
10. **Compliance with Legal and Ethical Guidelines**:
- Ensure that your use of LLaMA adheres to the provided guidelines and is ethically sound.
Remember, this is a high-level overview, and the specific steps might vary based on the exact version of LLaMA you are using and your local environment. Always refer to the official documentation for the most accurate and detailed instructions.
Getting the Models
- Visit the Llama download form and accept our License.
- Once your request is approved, you will receive a signed URL over email.
- Clone the Llama 2 repository (here).
- Run the download.sh script, passing the URL provided when prompted to startthe download.
a. Keep in mind that the links expire after 24 hours and a certain amount of downloads.If you start seeing errors such as 403: Forbidden, you can always re-request a link.
Hosting
Amazon Web Services
AWS provides multiple ways to host your Llama models. (SageMaker Jumpstart, EC2, Bedrock etc). In this document we are going to outline the steps to host your models using SageMaker Jumpstart and Bedrock. You can refer to other offerings directly on the AWS site.
Bedrock
A fully managed service that offers a choice of high performing foundation models, available via an API, to build generative AI applications, simplifying development while maintaining privacy and security. You can read more about the product here and follow instructions to use Llama 2 with Bedrock here.
EC2 Instance
To deploy models on EC2 instances, you must first request access to the model from our Llama download form, Hugging Face or Kaggle. Once you have this model you can either deploy it on a Deep Learning AMI image that has both Pytorch and Cuda installed or create your own EC2 instance with GPUs and install all the required dependencies. For detailed instructions on how to set up your Deep Learning AMI image you can refer here or to set up your own EC2 instance here.
SageMaker JumpStart
Amazon SageMaker enables ML practitioners to build, train and deploy machine learning models for any use case with fully managed infrastructure, tools and workflows. With SageMaker JumpStart, ML practitioners can choose from a broad selection of publicly available foundational models and deploy them on SageMaker Instances for model training and deployments. You can read more about it here.
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