Description from the site:
Mistral AI team is proud to release Mistral 7B, the most powerful language model for its size to date.
Mistral 7B in short
Mistral 7B is a 7.3B parameter model that:
Outperforms Llama 2 13B on all benchmarks
Outperforms Llama 1 34B on many benchmarks
Approaches CodeLlama 7B performance on code, while remaining good at English tasks
Uses Grouped-query attention (GQA) for faster inference
Uses Sliding Window Attention (SWA) to handle longer sequences at smaller cost
We’re releasing Mistral 7B under the Apache 2.0 license, it can be used without restrictions.
Download it and use it anywhere (including locally) with our reference implementation
Deploy it on any cloud (AWS/GCP/Azure), using vLLM inference server and skypilot
Use it on HuggingFace
Mistral 7B is easy to fine-tune on any task. As a demonstration, we’re providing a model fine-tuned for chat, which outperforms Llama 2 13B chat.
It will depend on the representation of the parameters. Most models support bfloat16, where each parameters is 16-bits (2 Bytes). For these models, every Billion parameters needs roughly 2 GB of VRAM.
It is possible to reduce the memory footprint by using 8 bits for each param, and some models support this, but they start to get very stupid.
That would mean 16GB are required to run this one