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* Add the Mixtral model. * Add more of the mixtral layers. * Add the final layers for mixtral. * Sketch the expert selection. * Add some expert routing logic. * Hopefully finish the routing logic for mixtral. * Add the mixtral example. * Fix the weight filenames. * Bugfix. * Another fix. * Yet another fix + remove the unused pragma. * Shape fix. * Add a readme.
candle-mixtral: 8x7b LLM using a sparse mixture of experts.
Mixtral-8x7B-v0.1 is a pretrained generative LLM with 56 billion parameters.
- Blog post from Mistral announcing the model release.
- Model card on the HuggingFace Hub.
Running the example
$ cargo run --example mixtral --release -- --prompt "def print_prime(n): "
def print_prime(n): # n is the number of prime numbers to be printed
i = 2
count = 0
while (count < n):
if (isPrime(i)):
print(i)
count += 1
i += 1
def isPrime(n):
for x in range(2, int(n**0.5)+1):
if (n % x == 0):
...