satendrakumar/MyGemmaNPC
MyGemmaNPC is a 0.3 billion parameter language model developed by satendrakumar, fine-tuned from Google's gemma-3-270m-it. This model specializes in generating conversational text, making it suitable for interactive applications like non-player characters (NPCs) in games or chatbots. It was trained using the TRL library, focusing on instruction-following capabilities for dialogue generation. With a context length of 32768 tokens, it can maintain longer conversations.
Loading preview...
Model Overview
MyGemmaNPC is a compact, 0.3 billion parameter language model developed by satendrakumar. It is a fine-tuned variant of Google's gemma-3-270m-it base model, specifically adapted for conversational tasks. The model leverages the TRL (Transformers Reinforcement Learning) library for its training process, indicating an optimization for instruction-following and dialogue generation.
Key Capabilities
- Conversational AI: Designed to generate human-like responses in interactive dialogue scenarios.
- Instruction Following: Fine-tuned to understand and respond to user prompts effectively.
- Efficient Deployment: As a 0.3B parameter model, it offers a balance between performance and computational efficiency.
Training Details
The model was trained using the Supervised Fine-Tuning (SFT) method with the TRL library (version 0.29.1). It utilizes Transformers version 5.4.0 and PyTorch 2.11.0. This setup suggests a focus on adapting the base Gemma model for specific interactive text generation applications.
Use Cases
MyGemmaNPC is particularly well-suited for applications requiring compact, responsive conversational agents. This includes:
- Game NPCs: Generating dynamic dialogue for non-player characters.
- Chatbots: Powering interactive customer service or informational bots.
- Dialogue Systems: Creating prototypes for conversational interfaces where a smaller model size is advantageous.