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
).
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Inference API (serverless) does not yet support transformers.js models for this pipeline type.