Update README.md

README.md

@@ -5,4 +5,77 @@ base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
 
 https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-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-1.5B-Instruct`.
+
+```js
+import { pipeline } from "@huggingface/transformers";
+
+// Create a text generation pipeline
+const generator = await pipeline(
+  "text-generation",
+  "onnx-community/Qwen2.5-Coder-1.5B-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>
+
+````
+Sure! Below is the implementation of the QuickSort algorithm in Python:
+
+```python
+def quicksort(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 quicksort(left) + middle + quicksort(right)
+
+# Example usage:
+arr = [3, 6, 8, 10, 1, 2, 1] 
+print("Original array:", arr)
+sorted_arr = quicksort(arr)
+print("Sorted array:", sorted_arr)
+```
+
+### Explanation:
+- **Base Case**: If the length of the list `arr` is less than or equal to one (`len(arr) <= 1`), it means the list is already sorted and can be returned as it is.
+- **Pivot Selection**: The chosen `pivot` element can be any element from the list (e.g., `len(arr)//2`). For simplicity here we choose this way.
+- **Partitioning**:
+   - Elements less than or equal to `pivot` are placed into a new list called `left`.
+   - Elements equal to `pivot` are placed into another new list called `middle`.
+   - Elements greater than or equal to `pivot` are placed into yet another new list called `right`.
+- **Recursive Sorting**: The function recursively applies itself on these three lists (`left`, middle`, and right`) and concatenates them back together.
+
+This implementation ensures that all elements less than or equal to any given element will appear before that element in their respective partitions.
+````
+</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`).