PartnerAI / README.md
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---
license: apache-2.0
datasets:
- m-a-p/Matrix
- HuggingFaceFW/fineweb
- openbmb/RLAIF-V-Dataset
- allenai/WildChat-1M
- TIGER-Lab/WebInstructSub
metrics:
- accuracy
- bleu
- brier_score
- character
- charcut_mt
tags:
- code
---
Partner AI
A Multi-Functional AI Model for Code Generation, Conversation, and Real-Time Training
Overview
Partner AI is a versatile AI model designed for programmers, creators, and anyone seeking an intelligent companion. It excels in three key areas:
Code Generation: Partner AI can assist you in writing code by generating snippets, completing lines, and suggesting functionalities based on your context.
Conversation: Partner AI engages in natural language conversations, understanding your intent and responding in a comprehensive and informative manner.
Real-Time Training: Partner AI empowers you to continuously improve its capabilities by providing real-time feedback and training data. This allows for personalization and adaptation to your specific workflows.
Key Features
Multi-Functionality: Code generation, conversation, and real-time training in a single model.
Optimized Performance: Leverages state-of-the-art frameworks like ONNX, JAX, NEMO, NVIDIA GPUs, and CoreML for efficient execution across diverse platforms.
Real-Time Feedback: Provides immediate feedback mechanisms to guide Partner AI's learning and refinement.
Personalization: Adapts to your coding style, preferences, and conversation topics over time.
Open-Source (Optional): Can be made available as open-source to foster community contributions and enhancements (decision left to the developer).
Technical Specifications
Model Architecture: (To be specified by the developer. Consider including details like transformer-based architecture, encoder-decoder structure, or any custom modifications)
Supported Frameworks:
ONNX (Open Neural Network Exchange) for cross-platform deployment
JAX for high-performance numerical computation and automatic differentiation
NEMO (Neural Modular Toolkit) for efficient speech and language processing tasks
NVIDIA GPUs for hardware acceleration and improved training speed
CoreML (optional) for seamless integration with Apple devices
Installation
(Provide clear installation instructions based on your chosen framework(s). Here's a general template:)
Prerequisites: Ensure you have the necessary frameworks (ONNX, JAX, NEMO, NVIDIA CUDA Toolkit) installed on your system. Refer to their respective documentation for installation guidance.
Clone the Repository: (If open-source) Clone the Partner AI repository from GitHub using git clone https://github.com/InboraStudio
Install Dependencies: Navigate to the cloned directory and run pip install -r requirements.txt to install required Python packages.
Usage
(Provide detailed instructions on how to use Partner AI for code generation, conversation, and real-time training. Here are some general guidelines:)
Code Generation:
Provide Context: Briefly describe the functionality you want the code to achieve.
Start Typing: Begin writing your code, and Partner AI will suggest completions, snippets, or alternative approaches based on the context.
Refine and Accept: Review Partner AI's suggestions and accept or modify them as needed.
Conversation:
Initiate Conversation: Start a conversation by typing your message or question.
Engage Naturally: Partner AI will respond in a comprehensive and informative manner, striving to understand your intent.
Explore Topics: Discuss various topics freely, and Partner AI will learn from your interactions.
Real-Time Training
Provide Feedback: During code generation or conversation, you can provide feedback to Partner AI by correcting suggestions, highlighting preferred approaches, or offering additional information.
Continuous Learning: Partner AI incorporates your feedback to improve its future responses and code generation capabilities.
Additional Notes
Consider including examples or tutorials to illustrate Partner AI's usage more effectively.
Provide clear instructions on how to contribute to the project (if open-source) to encourage community involvement.
Address potential limitations or areas for future development to set expectations and guide future improvements.