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* Add the Gemma models. * Add the gemma example. * Adapt the RmsNorm. * Get the 2b model to work. * 7b support. * Use the config head dim. * Yet another fix. * Make the matrixes contiguous. * Also get the 7b model to work. * And add to the readme.
859 B
859 B
candle-mistral: 2b and 7b LLMs from Google DeepMind
Gemma is a collection of lightweight open models published by Google Deepmind with a 2b and a 7b variant.
In order to use the example below, you have to accept the license on the HuggingFace Hub Gemma repo and set up your access token via the HuggingFace cli login command.
Running the example
$ cargo run --example gemma --release -- --prompt "fn count_primes(max_n: usize)"
fn count_primes(max_n: usize) -> usize {
let mut primes = vec![true; max_n];
for i in 2..=max_n {
if primes[i] {
for j in i * i..max_n {
primes[j] = false;
}
}
}
primes.len()
}