Update README.md

README.md

@@ -5,4 +5,76 @@ base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
 
 https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct 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/@huggingface/transformers) using:
+```bash
+npm i @huggingface/transformers
+```
+
+**Example:** Text generation with `onnx-community/Qwen2.5-Coder-0.5B-Instruct`.
+
+```js
+import { pipeline } from "@huggingface/transformers";
+
+// Create a text generation pipeline
+const generator = await pipeline(
+  "text-generation",
+  "onnx-community/Qwen2.5-Coder-0.5B-Instruct",
+  { dtype: "q4" },
+);
+
+// Define the list of messages
+const messages = [
+  { role: "system", content: "You are a helpful assistant." },
+  { role: "user", content:  "Write a quick sort algorithm." },
+];
+
+// Generate a response
+const output = await generator(messages, { max_new_tokens: 512, do_sample: false });
+console.log(output[0].generated_text.at(-1).content);
+```
+
+<details>
+
+<summary>Example output</summary>
+
+````
+Here's a simple implementation of the quick sort algorithm in Python:
+
+```python
+def quick_sort(arr):
+    if len(arr) <= 1:
+        return arr
+    
+    pivot = arr[len(arr) // 2]
+    left = [x for x in arr if x < pivot]
+    middle = [x for x in arr if x == pivot]
+    right = [x for x in arr if x > pivot]
+    
+    return quick_sort(left) + middle + quick_sort(right)
+
+# Example usage:
+arr = [3, 6, 8, 10, 1, 2]
+sorted_arr = quick_sort(arr)
+print(sorted_arr)
+```
+
+### Explanation:
+
+- **Base Case**: If the array has less than or equal to one element (i.e., `len(arr)` is less than or equal to `1`), it is already sorted and can be returned as is.
+
+- **Pivot Selection**: The pivot is chosen as the middle element of the array.
+
+- **Partitioning**: The array is partitioned into three parts: elements less than the pivot (`left`), elements equal to the pivot (`middle`), and elements greater than the pivot (`right`). These partitions are then recursively sorted.
+
+- **Recursive Sorting**: The subarrays are sorted recursively using `quick_sort`.
+
+This approach ensures that each recursive call reduces the problem size by half until it reaches a base case.
+````
+</details>
+
+
+---
+
 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`).