Update README.md

README.md

@@ -81,6 +81,50 @@ model-index:
 This instruct tune demonstrates state-of-the-art performance (compared to models of similar size) on the MultiPL-E metrics across multiple programming languages tested using [BigCode's Evaluation Harness](https://github.com/bigcode-project/bigcode-evaluation-harness/tree/main), and on the code portions of
 [MT Bench](https://klu.ai/glossary/mt-bench-eval)
 
+
+## Usage
+Here's how you can run the model use the model:
+
+```python
+# pip install -U transformers
+# pip install accelerate
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("stabilityai/stable-code-instruct-3b", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("stabilityai/stable-code-instruct-3b", torch_dtype=torch.bfloat16, trust_remote_code=True)
+model.eval()
+model = model.cuda()
+
+messages = [
+    {
+        "role": "system",
+        "content": "You are a helpful and polite assistant",
+    },
+    {
+        "role": "user",
+        "content": "Write a simple website in HTML. When a user clicks the button, it shows a random joke from a list of 4 jokes."
+    },
+]
+
+prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+
+inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
+
+tokens = model.generate(
+    **inputs,
+    max_new_tokens=1024,
+    temperature=0.5,
+    top_p=0.95,
+    top_k=100,
+    do_sample=True,
+    use_cache=True
+)
+
+output = tokenizer.batch_decode(tokens[:, inputs.input_ids.shape[-1]:], skip_special_tokens=False)[0]
+```
+
+
 ## How to Cite
 
 ```bibtex