LLM Comparator using Streamlit and Groq (code in description)

Which model is better, Llama 8b vs Mixtral 8x7b vs Llama 70b vs ....? Which is faster, accurate, reliable or safe? Streamlit App: https://groq-llm-comparator.streamlit.app/ Github Repo: https://github.com/agamarora/groq_llm_comparator To answer this and many more such questions, I built an LLM comparator using Groq and Streamlit. You can easily compare the responses and token utilization. You can even use another model as a comparator to compare them based on relevance, prompt adherence and performance (time to response and token utilization). As of now you can compare the following models; LLaMA 8B, LLaMA 8B Instant, LLaMA 70B, LLaMA 70B Versatile, Mixtral 8x7B, Gemma 7B IT and Gemma 2 9B IT. The project is open-source and also available on streamlit-share. You would need to create an account on Groq ) and generate a free API key for yourself to try this. This simple project allows us to not only compare and contrast different foundational models but test out different settings such as temperature, top_p (nucleus sampling), token constraints against different prompts. This is possible only because Groq is democratizing how we use large language models. I am a fan of Groq and how they are revolutionizing inference and bringing state-of-the-art opensource models for us to use at blazingly fast speeds. I plan to add proprietary model support in the future to compare some of the top models against opensource ones as well. Hope you guys enjoy this. Be curious and keep experimenting ❤️