LLama-SDG
Streamlit
Our project utilizes Llama 3 and 3.1 to generate synthetic datasets tailored for function calling, with the goal of enhancing small language models in text-to-action tasks. By leveraging these advanced models, we create realistic and varied data that helps train smaller language models to understand and execute commands, queries, and functions more effectively. This approach aims to improve the accuracy and efficiency of language models in interpreting user inputs and performing tasks based on natural language instructions. Ultimately, our project seeks to make language models more capable and reliable in practical applications, enhancing their utility in real-world scenarios.
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