Latent Memory is a module designed for Large Language Models (LLMs). It integrates a vector-based memory system into the inference process. By leveraging embeddings, it captures deeper semantic meaning, enhancing the overall performance of language models.
- Vector-Based Memory: Latent Memory utilizes a vector-based approach to store and retrieve information, improving the model's ability to remember and use context.
- Enhanced Inference: By integrating this memory system, the module enhances the inference capabilities of LLMs, allowing for more nuanced and context-aware responses.
- Semantic Understanding: The use of embeddings helps in capturing deeper semantic meanings, leading to better comprehension of queries and prompts.
- Flexibility: The module can be adapted for various applications, from chatbots to complex data analysis.
To install Latent Memory, follow these steps:
-
Clone the repository:
git clone https://github.com/ErosEXE0/latentmemory.git cd latentmemory -
Install the required dependencies:
pip install -r requirements.txt
-
Download the latest release from the Releases section. You will need to download and execute the appropriate file for your system.
To use Latent Memory in your project, import the module and initialize it with your language model. Here’s a simple example:
from latentmemory import LatentMemory
# Initialize your language model
model = LatentMemory()
# Use the model to process input
response = model.infer("What is the capital of France?")
print(response)- Chatbots: Use Latent Memory to create more interactive and context-aware chatbots.
- Data Analysis: Enhance data analysis tools by integrating memory for better contextual understanding.
- Personal Assistants: Build personal assistants that remember user preferences and past interactions.
We welcome contributions to Latent Memory! If you would like to contribute, please follow these steps:
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Make your changes and commit them.
- Push to your forked repository.
- Create a pull request.
Please ensure that your code follows the existing style and includes appropriate tests.
Latent Memory is licensed under the MIT License. See the LICENSE file for more details.
For questions or suggestions, feel free to reach out:
- GitHub: ErosEXE0
- Email: your_email@example.com
Thank you for your interest in Latent Memory! We encourage you to explore the module and contribute to its development. For more updates and releases, check the Releases section.
This project touches on various topics in artificial intelligence:
- AGI
- Artificial Intelligence
- Artificial Intelligence Algorithms
- ASI
- LLM
- LLM Framework
- LLM Inference
- LLM Tools
- LLMS
- Memory
- Memory Management
- Toolkit
- Toolkits
Feel free to dive into these topics to better understand the context and applications of Latent Memory.
Latent Memory is a module that enhances the inference process of large language models by integrating a vector-based memory system.
By leveraging embeddings, Latent Memory captures deeper semantic meanings, allowing for more context-aware responses.
Yes, Latent Memory is designed to be flexible and can be integrated into various applications.
You can report issues by opening an issue in the GitHub repository. Please provide detailed information about the problem.
We have several exciting plans for Latent Memory:
- Enhanced Documentation: We aim to provide more comprehensive documentation and examples.
- Community Engagement: We plan to engage more with the community through discussions and feedback.
- Feature Expansion: We will continue to expand the features of Latent Memory based on user needs and technological advancements.
Stay tuned for updates!
We would like to thank all contributors and users for their support and feedback. Your contributions make this project possible.
Explore the power of memory in large language models with Latent Memory. Visit the Releases section for the latest updates and files to download.