update readme.md

README.md

@@ -3,16 +3,89 @@ tags:
 - gguf
 - llama.cpp
 - unsloth
-
+license: mit
+language:
+- en
+base_model:
+- microsoft/Phi-3.5-mini-instruct
 ---
-
 # phi-3.5-AI-Vtuber-json : GGUF
 
-This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
+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
+___
+## Model Overview
+
+* **Base Model: Phi-3.5 Mini-Instruct**
+
+* **Fine-Tuned For: AI companion behavior with structured JSON output**
+
+Output Format:
+```bash
+{
+  "response": "...",
+  "emotion": "..."
+}
+```
+
+Primary Use Case: AI companion systems and applications where responses must be machine-readable.
+___
+## 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.
+___
+## 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
+
+___
+
+## Training Details
+
+**Custom Dataset -**
+I created your 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.
+___
+## Usage Example
+Below is an example of how the model might respond in your application:
+```bash
+{
+  "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.
+___
+## License
 
-**Example usage**:
-- For text only LLMs:    `./llama.cpp/llama-cli -hf Navpy/phi-3.5-AI-Vtuber-json --jinja`
-- For multimodal models: `./llama.cpp/llama-mtmd-cli -hf Navpy/phi-3.5-AI-Vtuber-json --jinja`
+This project is licensed under the MIT License.
+You are free to use, modify, and distribute this project for personal or educational purposes.
 
 ## Available Model files:
 - `phi-3.5-mini-instruct.Q4_K_M.gguf`
@@ -20,4 +93,4 @@ This model was finetuned and converted to GGUF format using [Unsloth](https://gi
 ## Ollama
 An Ollama Modelfile is included for easy deployment.
 This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)