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@@ -4,4 +4,63 @@ library_name: transformers.js
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  https://huggingface.co/susnato/phi-1_5_dev with ONNX weights to be compatible with Transformers.js.
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  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`).
 
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  https://huggingface.co/susnato/phi-1_5_dev with ONNX weights to be compatible with Transformers.js.
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+
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+ ## Usage (Transformers.js)
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+
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+ 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:
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+ ```bash
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+ npm i @xenova/transformers
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+ ```
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+
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+ **Example:** Text generation (code completion) with `Xenova/phi-1_5_dev`.
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+
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+ ```js
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+ import { pipeline } from '@xenova/transformers';
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+
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+ // Create a text-generation pipeline
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+ const generator = await pipeline('text-generation', 'Xenova/phi-1_5_dev');
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+
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+ // Construct prompt
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+ const prompt = `\`\`\`py
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+ import math
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+ def print_prime(n):
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+ """
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+ Print all primes between 1 and n
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+ """`;
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+
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+ // Generate text
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+ const result = await generator(prompt, {
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+ max_new_tokens: 100,
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+ });
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+ console.log(result[0].generated_text);
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+ ```
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+
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+ Results in:
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+ ```py
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+ import math
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+ def print_prime(n):
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+ """
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+ Print all primes between 1 and n
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+ """
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+ primes = []
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+ for num in range(2, n+1):
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+ is_prime = True
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+ for i in range(2, int(math.sqrt(num))+1):
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+ if num % i == 0:
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+ is_prime = False
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+ break
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+ if is_prime:
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+ primes.append(num)
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+ print(primes)
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+
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+ print_prime(20)
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+ ```
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+
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+ Running the code produces the correct result:
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+ ```
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+ [2, 3, 5, 7, 11, 13, 17, 19]
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+ ```
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+ ---
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+
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+
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  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`).