Large Enough
mistral.ai
Today, we are announcing Mistral Large 2, the new generation of our flagship model. Compared to its predecessor, Mistral Large 2 is significantly more capable in code generation, mathematics, and reasoning. It also provides a much stronger multilingual support, and advanced function calling capabilities.

Another day, another model.

Just one day after Meta released their new frontier models, Mistral AI surprised us with a new model, Mistral Large 2.

It’s quite a big one with 123B parameters, so I’m not sure if I would be able to run it at all. However, based on their numbers, it seems to come close to GPT-4o. They claim to be on par with GPT-4o, Claude 3 Opus, and the fresh Llama 3 405B regarding coding related tasks.

benchmarks

It’s multilingual, and from what they said in their blog post, it was trained on a large coding data set as well covering 80+ programming languages. They also claim that it is “trained to acknowledge when it cannot find solutions or does not have sufficient information to provide a confident answer”

On the licensing side, it’s free for research and non-commercial applications, but you have to pay them for commercial use.

  • hohoho@lemmy.worldEnglish
    0·
    4 months ago

    The general rule of thumb that I’ve heard is that you need 1GB of memory for ever 1B parameters. In practice however I’ve found this to not be the case. For instance on a GH200 system I’m able to run Llama3 70b in about 50GB of memory. Llama3.1 405b on the other hand uses +90GB of GPU memory and spills over to using about another 100GB of system memory… but runs like a dog at 2 tokens per second. I expect inference costs will come down over time but for now would recommend Lambda Labs if you don’t have the need for a GPU workstation.