Model Overview

tanliboy/zephyr-gemma-2-9b-sft is a fine-tuned language model based on Google's Gemma-2-9B architecture. This model has been specifically adapted for conversational and instruction-following applications through supervised fine-tuning (SFT).

Key Capabilities

• Instruction Following: Enhanced ability to understand and respond to user instructions due to fine-tuning on the HuggingFaceH4/ultrachat_200k dataset.
• Conversational AI: Optimized for generating coherent and contextually relevant responses in dialogue settings.
• Gemma 2 Base: Leverages the foundational capabilities of the Gemma 2-9B model, providing a strong base for various NLP tasks.

Training Details

The model was trained with a learning rate of 3e-06 over 1 epoch, utilizing a total batch size of 128 across 8 GPUs. The training process resulted in a validation loss of 1.0639, indicating effective learning from the fine-tuning dataset.

Good For

• Developing chatbots and virtual assistants.
• Applications requiring instruction-tuned language generation.
• Experimenting with fine-tuned Gemma 2 models for dialogue systems.