README.md

---
library_name: transformers.js
---

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](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using:
```bash
npm i @xenova/transformers
```

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

```js
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:
```py
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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).