tanliboy/zephyr-gemma-2-9b-sft
The tanliboy/zephyr-gemma-2-9b-sft model is a fine-tuned version of Google's Gemma-2-9B, a 9 billion parameter language model. It was fine-tuned on the HuggingFaceH4/ultrachat_200k dataset, demonstrating a validation loss of 1.0639. This model is optimized for conversational AI and instruction-following tasks, leveraging the base Gemma 2 architecture for enhanced performance in dialogue generation.
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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_200kdataset. - 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.