Very nice. It’s mind-blowing to me that it used to take me months to build and deploy a forecasting model five years ago. Now in minutes I can use something like this, implemented with help from an LLM, and get apparently quite good results (and fine-tune on my data, it seems).
Speed looks nice too. I want an open weight model with this performance now.
Perhaps I'm misunderstanding you, but you should be able to download the models at https://huggingface.co/collections/mistralai/timestral
[flagged]
So is Mistral's model, right? Genuine question because I had been following your company - is there any use case where t0 is better than timestral?
Ah thanks! The Mistral model has a restrictive license, where you need a specific commercial agreement to use it if you make more than 20M$ revenue/month. Ours is Apache 2.
If you compare the 2, it's not a strict "timestral is better" story - the winrates are tight - your probably should try both on your use-case. We have a 42% winrate vs Timestral on fevbench for instance.
Ofc, our main value is not just these models, but also 1) extra capabilities (like our model that can natively account for stockouts/censored data, and others coming soon), 2) our systems and products around the model, and 3) we're all laser focused on time-series/forecasting
interesting. The github repo (https://github.com/mistralai/timestral ) says the license is Apache 2. Where does the 20M$ revenue limit comes from?
It's in the licence of the weights, which you can see on Huggingface.
Indeed. Link: https://huggingface.co/mistralai/Timestral-209M/blob/main/LI...