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Strawberry Phi is a fine-tuning application for OpenAI's GPT models. The purpose of this project is to provide an easy-to-use interface for creating custom models tailored to specific needs. The goal is to enable users to fine-tune GPT models with their own data, resulting in models that perform better on their unique tasks.

Demo: https://strawberry-phi.gptengineer.run

• Side-by-side model comparison
• Export test results in CSV or JSON format
• Customizable model testing parameters
• Detailed response analysis
• Secure storage and encryption of API keys

• Toggle between light and dark modes
• Set default values for model testing parameters
• Configure notification preferences for job status updates

Emphasizes the model’s core capability of self-reflection and self-correction.

Highlights the active use of reflection in improving the model's reasoning process.

Signifies the model’s ability to validate its reasoning, detect errors, and refine outputs for accuracy.

Strawberry Phi is an advanced multi-modal, agentic AI assistant designed for complex task handling across various domains. Developed by rUv, it uses reflection-tuning techniques to self-evaluate and correct reasoning errors. The model leverages advanced methodologies such as sequential, concurrent, recurrent, and reinforcement learning approaches for task management, planning, and execution. By incorporating multi-modal inputs and outputs (e.g., text, images, audio), it can manage various task complexities, adapt dynamically to user requirements, and continuously improve its performance. It ensures reliability by integrating self-reflection mechanisms and using Glaive's synthetic data generation for rapid fine-tuning and error minimization.

The reflection approach to training language models, as exemplified by Reflection 70B, is an innovative technique designed to improve model performance and reduce errors. Here's an explanation of how it works:

The process starts with a pre-existing large language model, in this case, Meta's Llama 3.1-70B Instruct model.

This is the core technique that teaches the model to detect and correct mistakes in its own reasoning. It involves:

a) Special Tokens: The model is trained to use special tokens like , , , , , and . These tokens structure the model's thought process.

b) Reasoning Process: When given a query, the model first reasons through it within the tags. This allows the model to "think out loud" about the problem.

c) Self-Correction: If the model detects an error in its reasoning, it uses tags to acknowledge the mistake and attempt to correct it. This process can occur multiple times within a single response.

d) Final Output: Once satisfied with its reasoning, the model provides its final answer within tags.

Companies like Glaive create large datasets of synthetic data that include these reflection and correction processes. This data is used to fine-tune the base model.

The model is then trained on this synthetic data, learning to mimic the reflection and self-correction processes embedded in the training examples.

Through multiple rounds of training, the model learns to apply this reflection process to a wide variety of queries and scenarios.

The model is tested on various benchmarks, and its performance is used to further refine the training process and data generation.

The key innovation of this approach is that it teaches the model not just to provide answers, but to critically evaluate its own reasoning and correct itself when necessary. This leads to more accurate and reliable outputs, especially in complex reasoning tasks.

This reflection-tuning technique represents a significant advancement in language model training, potentially reducing hallucinations and improving the overall reliability of AI-generated responses.

This configuration file defines a sophisticated multi-modal agentic AI assistant capable of handling complex tasks across various domains. It leverages Glaive's schema-based approach and synthetic data generation capabilities to create a highly customized and efficient model.

• Name: AdvancedMultiModalAgenticAssistant
• Base Model: phi-mini-128k
• System Prompt: Defines the AI's core capabilities and goals

• Includes various AI methodologies like sequential, concurrent, recurrent, and reinforcement learning approaches
• Incorporates advanced techniques such as Q* and other hybrid approaches

• Defines structured input and output formats
• Includes comprehensive fields for task understanding, planning, execution, and self-reflection

• Specifies epochs, batch size, learning rate, and other hyperparameters

• Lists various metrics to assess model performance

• Outlines initial and specialized training phases
• Includes provisions for continual learning

• Error handling mechanisms
• Bias mitigation strategies
• Personalization capabilities
• Performance tracking
• Multi-task learning support
• Human-in-the-loop integration

• Customize the JSON file according to your specific use case and requirements.
• Use Glaive's platform to generate synthetic data based on this schema.
• Train your model using Glaive's custom model training capabilities.
• Utilize Glaive's API for model deployment and integration into your applications.

• Regularly update and refine your model based on performance metrics and user feedback.
• Leverage Glaive's rapid iteration capabilities for continuous improvement.
• Ensure compliance with ethical AI guidelines and data privacy regulations.

The reflection approach to training language models, as exemplified by Reflection 70B, is an innovative technique designed to improve model performance and reduce errors. Here's an explanation of how it works:

The process starts with a pre-existing large language model, in this case, Meta's Llama 3.1-70B Instruct model.

This is the core technique that teaches the model to detect and correct mistakes in its own reasoning. It involves:

a) Special Tokens: The model is trained to use special tokens like , , , , , and . These tokens structure the model's thought process.

b) Reasoning Process: When given a query, the model first reasons through it within the tags. This allows the model to "think out loud" about the problem.

c) Self-Correction: If the model detects an error in its reasoning, it uses tags to acknowledge the mistake and attempt to correct it. This process can occur multiple times within a single response.

d) Final Output: Once satisfied with its reasoning, the model provides its final answer within tags.

Companies like Glaive create large datasets of synthetic data that include these reflection and correction processes. This data is used to fine-tune the base model.

The model is then trained on this synthetic data, learning to mimic the reflection and self-correction processes embedded in the training examples.

Through multiple rounds of training, the model learns to apply this reflection process to a wide variety of queries and scenarios.

The model is tested on various benchmarks, and its performance is used to further refine the training process and data generation.

The key innovation of this approach is that it teaches the model not just to provide answers, but to critically evaluate its own reasoning and correct itself when necessary. This leads to more accurate and reliable outputs, especially in complex reasoning tasks.

This reflection-tuning technique represents a significant advancement in language model training, potentially reducing hallucinations and improving the overall reliability of AI-generated responses.

The system is built using a modern web stack, including Vite, React, and Tailwind CSS. The backend interacts with OpenAI's API to manage fine-tuning jobs and model testing.

• Vite
• React
• shadcn-ui
• Tailwind CSS
• OpenAI API

• Node.js & npm installed - install with nvm
• OpenAI API key

• Easy-to-use interface for fine-tuning GPT models
• Custom models tailored to specific needs
• Improved performance on unique tasks

• File upload and validation
• Model selection and configuration
• Fine-tuning job management
• Model testing with custom prompts
• Real-time job status updates
• Secure API key management

1. Clone the repository using the project's Git URL.

   git clone <YOUR_GIT_URL>

2. Navigate to the project directory.

   cd <YOUR_PROJECT_NAME>

3. Install the necessary dependencies.

   npm i

4. Start the development server with auto-reloading and an instant preview.

   npm run dev

5. Set your OpenAI API key in the Settings page of the application.