phi-3.5-AI-Vtuber-json : GGUF

This is a fine-tuned large language model based on Phi-3.5 Mini-Instruct, optimized for AI companion applications that require strict, machine-readable JSON output.

This was trained to always return responses in a consistent JSON format with response and emotion fields. This makes it easy to integrate with software that parses and uses AI outputs programmatically.

🔗 Project Nova, where i have used this model: https://github.com/Navjot-Singh7/Project-Nova

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Model Overview

• Base Model: Phi-3.5 Mini-Instruct

• Fine-Tuned For: AI companion behavior with structured JSON output

Output Format:

{
  "response": "...",
  "emotion": "..."
}

Primary Use Case: AI companion systems and applications where responses must be machine-readable.

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Capabilities

• This model has been fine-tuned to:

  • Generate companion-style text that is appropriate, engaging, and in JSON format.

  • Always include both:

    • response: the AI’s text output

    • emotion: a tag describing the emotional tone of the response

Produce outputs that are consistent and reliable for code integration.

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Intended Use

Primary Use Cases

• AI companion applications
• Virtual characters or avatars
• VTuber or assistant personalities -Applications that require structured LLM output
• Emotion-aware conversational systems

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Training Details

Custom Dataset - I created my own labeled dataset with 10–20 original samples with JSON-style examples.

Synthetic Data Generation - Then I expanded this initial dataset using another language model to create a synthetic training corpus of ~1,800 samples.

Fine-Tuning Environment - Training was performed using Google Colab.

Dataset Composition - The dataset contains structured examples that guide the model to generate JSON output with response and emotion.

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Usage Example

Below is an example of how the model might respond in your application:

{
  "response": "Hello! I'm fine thank you... uhm.. did you have a good day?", 
  "emotion": "happy"
}

This makes it easy to parse and handle both the semantic content (response) and the emotional context (emotion) in code.

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License

This model is licensed under the MIT License. You are free to use, modify, and distribute this model for personal or educational purposes.

Available Model files:

• phi-3.5-mini-instruct.Q4_K_M.gguf

Ollama

An Ollama Modelfile is included for easy deployment. This was trained 2x faster with Unsloth