100%. we've found that llama-3.3-70b-versatile and qwen-qwq-32b perform exceptionally well with reliable function calling. we had recognized the need for this and our engineers partnered with glaive ai to create fine tunes of llama 3.0 specifically for better function calling performance until the llama 3.3 models came along and performed even better.
i'd actually love to hear your experience with llama scout and maverick for function calling. i'm going to dig into it with our resident function calling expert rick lamers this week.
do you happen to be trying this out on free tier right now? because our rate limits are at 6k tokens per minute on free tier for this model, which might be what you're running into.
amazing! and yes, we'll have maverick available today. the reason we limit ctx window is because demand > capacity. we're pretty busy with building out more capacity so we can get to a state where we give everyone access to larger context windows without melting our currently available lpus, haha.
hi! i work @ groq and just made an account here to answer any questions for anyone who might be confused. groq has been around since 2016 and although we do offer hardware for enterprises in the form of dedicated instances, our goal is to make the models that we host easily accessible via groqcloud and groq api (openai compatible) so you can instantly get access to fast inference. :)
we have a pretty generous free tier and a dev tier you can upgrade to for higher rate limits. also, we deeply value privacy and don't retain your data. you can read more about that here: https://groq.com/privacy-policy/
i'd actually love to hear your experience with llama scout and maverick for function calling. i'm going to dig into it with our resident function calling expert rick lamers this week.