I was enthusiastic but it’s "contact us" priced for now. I was expecting a classic cloud LLM forge with a public pricing.
Mistral AI Releases Forge
51–60 of 210 posts
Re: Mistral AI Releases Forge
#52My bet is that the solution to continuous learning is with external storage. There is a lot of talk about context engineering - but I have not seen anyone taking context as the main bottleneck and building a system around that.
This would show that even context engineering is kind of wrong term - because context does not enter the llm in some mysterious way - it goes through prompt and the whole model of passing chat history back and forth is not the most efficient way of using the prompt limitation.
Re: Mistral AI Releases Forge
#53I thought that for pretraining to work and reasoning to emerge you need internet scale data. How can forge achieve it with just internal company data (unless the said company is AT&T or something) ?
Re: Mistral AI Releases Forge
#54My bet is that the solution to continuous learning is with external storage. There is a lot of talk about context engineering - but I have not seen anyone taking context as the main bottleneck and building a system around that. This would show that even context engineering is kind of wrong term - because context does not enter the llm in some mysterious way - it goes through prompt and the whole model of passing chat…
What do you mean when you say "external storage?"
Re: Mistral AI Releases Forge
#55> Pre-training allows organizations to build domain-aware models by learning from large internal datasets. > Post-training methods allow teams to refine model behavior for specific tasks and environments. How do you suppose this works? They say "pretraining" but I'm certain that the amount of clean data available in proper dataset format is not nearly enough to make a "foundation model". Do you suppose what they are…
Probably just means SFT fine-tuning a base model, vs behavioural dpo and/or SFT fine-tuning a instruction model.
Re: Mistral AI Releases Forge
#56I am rooting for Mistral with their different approach: not really competing on the largest and advanced models, instead doing custom engineering for customers and generally serving the needs of EU customers.
their ocr model is goated
Did they make significant improvements in OCR 3? The quality I was getting from Mistral OCR 2 was nowhere near as good as what I could get from just sending the same files to Claude Sonnet via an API call.
I have been finding Voxtral useful though.
Re: Mistral AI Releases Forge
#57Don't sleep on Mistral. Highly underrated as a general service LLM. Cheaper, too. Their emphasis on bespoke modelling over generalized megaliths will pay off. There are all kinds of specialized datasets and restricted access stores that can benefit from their approach. Especially in highly regulated EU. Not everyone is obsessed with code generation. There is a whole world out there.
Agreed. I’ve used their platform to train smaller, specialized models. Something I could have done in Codelab or some other tool, but their platform allows me to just upload a training set and as soon as it finishes I have a hosted model available at an endpoint. It obviously has some constraints compared to running the training yourself, but it also opens up the opportunity to way more people.
Re: Mistral AI Releases Forge
#58ASML and ESA as clients means something. I dont expect to see the first name somewhere else on the logo list
Re: Mistral AI Releases Forge
#59Re: Mistral AI Releases Forge
#60My bet is that the solution to continuous learning is with external storage. There is a lot of talk about context engineering - but I have not seen anyone taking context as the main bottleneck and building a system around that. This would show that even context engineering is kind of wrong term - because context does not enter the llm in some mysterious way - it goes through prompt and the whole model of passing chat…
"External Storage" whatever that is can not be the same as continous learning as it does not have the strong connections/capture the interdepencies of knowledge.
That said I think we will see more efforts also on the business side to have models that can help you build a knowledge base in some kind of standardized way that the model is trained to read. Or synthesize some sort on instructions how to navigate your knowledge base.
Currently e.g. Copilot tries to navigate a hot mess of a MS knowledge graph that is very different for each company. And due to its amnesia it has to repeat the discovery in every session. No wonder that does not work. We have to either standardize or store somewhere (model, instructions) how to find information efficiently.