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phire t1_jc5kwkh wrote

Prompt: Write some python code to calculate the 900th prime number

from math import sqrt

def is_prime(n):
    if n < 2:
        return False
    for i in range(2, n):
        if n % i == 0:
            return False
    return True

# Calculate the 900th prime number
n = 899

# Iterate from 2 to n and check if the number is prime
while (n > 1):
    if is_prime(n):
       prime_number = n
       break
    n = n - 1

# Print the result
print(prime_number)

Well, it gets points for writing code that runs without syntax errors. And it did successfully create a function that checks if a number is prime.

It looses points for forgetting the optimisation of stopping at the square root of n (despite importing sqrt)

The actual search goes totally off the rails. The actual implementation finds the first prime less than 900 by starting at 899 and going down. The comments are more correct, implying it was planing to increment up. If it had stopped after fining 900 primes, the result would have been correct.

TBH, I'm pretty impressed for a 7B parameter model.

17

blueSGL t1_jc5s56i wrote

Less than $100 to get this sort of performance out of a 7B parameter model and from the LLaMA paper they stopped training the 7B and 13B parameter models early.

Question is now just how much better can small models get. (lawyer/doctor/therapist in everyone's pocket, completely private?)

15

inigid t1_jc5za26 wrote

I'm thinking a chip with the model and inference runtime baked in, maybe having the same form factor as an SD card. Hey honey have you seen that copy of me from March 2023? Ughh, I think I accidentally threw it away..

5

Necessary_Ad_9800 t1_jcjge23 wrote

Everyone with their own private oracle in their hands. Pretty cool tbh

3

blueSGL t1_jcjgsl1 wrote

Exactly.

I'm just eager to see what fine tunes are going to be made on LLaMA now, and how model merging effects them. The combination of those two techniques has lead to some crazy advancements in the Stable Diffusion world. No idea if merging will work with LLMs as it does for diffusion models. (has anyone even tried yet?)

3

Necessary_Ad_9800 t1_jcjj8b6 wrote

Interesting. However I find some merges in SD to be terrible. But I have no doubt the open source community will make something amazing

2

Disastrous_Elk_6375 t1_jc5pny8 wrote

> TBH, I'm pretty impressed for a 7B parameter model.

Same here. I've tried a bunch of prompts from a repo and the "follow the instruction" part seems pretty good and consistent. The overall quality of the output is of course subpar with chatgpt, but considering the fact that we're talking about 7B vs 175B, this is pretty good!

10