Update README.md

README.md

@@ -8,7 +8,7 @@ language:
 - hi
 - es
 - th
-library_name: transformers
+library_name: transformers.js
 pipeline_tag: text-generation
 tags:
 - facebook
@@ -250,50 +250,50 @@ The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a
 
 **Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.
 
-## How to use
+## Usage (Transformers.js)
 
-This repository contains two versions of Llama-3.2-1B-Instruct, for use with `transformers` and with the original `llama` codebase.
-
-### Use with transformers
+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/@huggingface/transformers) using:
+```bash
+npm i @huggingface/transformers
+```
 
-Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
+You can then generate text as follows:
+```js
+import { pipeline } from '@huggingface/transformers';
 
-Make sure to update your transformers installation via `pip install --upgrade transformers`.
+// Create a text generation pipeline
+const generator = await pipeline('text-generation', 'onnx-community/Llama-3.2-1B-Instruct-q4f16', {
+  device: 'webgpu', // <- Run on WebGPU
+});
 
-```python
-import torch
-from transformers import pipeline
+// Define the list of messages
+const messages = [
+  { role: "system", content: "You are a helpful assistant." },
+  { role: "user", content: "What is the capital of France?" },
+];
 
-model_id = "meta-llama/Llama-3.2-1B-Instruct"
-pipe = pipeline(
-    "text-generation",
-    model=model_id,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-)
-messages = [
-    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
-    {"role": "user", "content": "Who are you?"},
-]
-outputs = pipe(
-    messages,
-    max_new_tokens=256,
-)
-print(outputs[0]["generated_text"][-1])
+// Generate a response
+const output = await generator(messages, { max_new_tokens: 128 });
+console.log(output[0].generated_text.at(-1).content);
 ```
 
-Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
-
-### Use with `llama`
+<details>
 
-Please, follow the instructions in the [repository](https://github.com/meta-llama/llama)
-
-To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
+<summary>Example output</summary>
 
 ```
-huggingface-cli download meta-llama/Llama-3.2-1B-Instruct --include "original/*" --local-dir Llama-3.2-1B-Instruct
+The capital of France is Paris.
 ```
 
+</details>
+
+> [!NOTE]  
+> We also support loading the library from a CDN, so you can import it using:
+> 
+> ```js
+> import { pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/transformers';
+> ```
+
 ## Hardware and Software
 
 **Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.