kykim0/gemma-2b-ultrachat-sft
The kykim0/gemma-2b-ultrachat-sft is a 2.5 billion parameter language model, fine-tuned from Google's Gemma-2b architecture. This model is specifically optimized for conversational AI tasks, having been trained on the HuggingFaceH4/ultrachat_200k dataset. It demonstrates a validation loss of 1.2072, indicating its proficiency in generating human-like dialogue and responses. Its primary strength lies in its ability to engage in chat-based interactions.
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Model Overview
The kykim0/gemma-2b-ultrachat-sft is a 2.5 billion parameter language model derived from the Google Gemma-2b architecture. This model has undergone supervised fine-tuning (SFT) using the comprehensive HuggingFaceH4/ultrachat_200k dataset, specifically designed for conversational AI.
Key Capabilities
- Conversational AI: Optimized for generating responses in chat-based scenarios, making it suitable for chatbots and interactive agents.
- Instruction Following: Benefits from the Ultrachat dataset's structure, which emphasizes diverse prompts and expected responses, enhancing its ability to follow instructions.
- Efficient Performance: As a 2.5B parameter model, it offers a balance between performance and computational efficiency, making it accessible for various deployment environments.
Training Details
The model was trained with a learning rate of 2e-05 over 3 epochs, utilizing a total batch size of 128 across 4 GPUs. The training process achieved a final validation loss of 1.2072, indicating effective learning from the conversational data. The training leveraged Transformers 4.39.0.dev0 and PyTorch 2.1.2.