Xenova/phi-1_5_dev

https://huggingface.co/susnato/phi-1_5_dev with ONNX weights to be compatible with Transformers.js.

Usage (Transformers.js)

If you haven't already, you can install the Transformers.js JavaScript library from NPM using:

npm i @xenova/transformers

Example: Text generation (code completion) with Xenova/phi-1_5_dev.

import { pipeline } from '@xenova/transformers';

// Create a text-generation pipeline
const generator = await pipeline('text-generation', 'Xenova/phi-1_5_dev');

// Construct prompt
const prompt = `\`\`\`py
import math
def print_prime(n):
    """
    Print all primes between 1 and n
    """`;

// Generate text
const result = await generator(prompt, {
  max_new_tokens: 100,
});
console.log(result[0].generated_text);

Results in:

import math
def print_prime(n):
    """
    Print all primes between 1 and n
    """
    primes = []
    for num in range(2, n+1):
        is_prime = True
        for i in range(2, int(math.sqrt(num))+1):
            if num % i == 0:
                is_prime = False
                break
        if is_prime:
            primes.append(num)
    print(primes)

print_prime(20)

Running the code produces the correct result: [2, 3, 5, 7, 11, 13, 17, 19]

Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).