HuggingFaceH4/zephyr-7b-gemma-sft-v0.1

Model Overview

HuggingFaceH4/zephyr-7b-gemma-sft-v0.1 is an 8.5 billion parameter language model derived from Google's Gemma-7B. It has been specifically enhanced through supervised fine-tuning (SFT) on the HuggingFaceH4/deita-10k-v0-sft dataset, aiming to improve its instruction-following capabilities and conversational fluency. The model operates with an 8192-token context window, making it suitable for processing moderately long inputs and generating detailed responses.

Key Capabilities

• Instruction Following: Optimized to understand and execute a wide range of user instructions.
• Conversational AI: Designed for generating coherent and contextually appropriate responses in dialogue settings.
• Supervised Fine-Tuning: Benefits from targeted training on a high-quality instruction dataset to refine its output quality.

Training Details

The model was trained with a learning rate of 2e-05 over 3 epochs, utilizing a total batch size of 128 across 16 devices. The training process achieved a final validation loss of 0.9732, indicating effective learning from the fine-tuning data.

Intended Use Cases

This model is well-suited for applications requiring robust instruction adherence and natural language generation, such as chatbots, virtual assistants, and content creation tools where precise responses to prompts are critical.