Update README.md

README.md

@@ -63,7 +63,39 @@ Llama-3-SEC has been trained using the llama3 chat template, which allows for ef
 
 To run inference with the Llama-3-SEC model using the llama3 chat template, use the following code:
 
-<chat_example>
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cuda" 
+
+model = AutoModelForCausalLM.from_pretrained(
+    "arcee-ai/Llama-3-SEC",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-72B-Instruct")
+
+prompt = "What are the key regulatory considerations for a company planning to conduct an initial public offering (IPO) in the United States?"
+messages = [
+    {"role": "system", "content": "You are an expert financial assistant - specializing in governance and regulatory domains."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
 
 ## Limitations and Future Work
 
@@ -71,7 +103,7 @@ This release represents the initial checkpoint of the Llama-3-SEC model, trained
 
 ## Usage
 
-To use the Llama-3-SEC model, please refer to the detailed instructions provided in the repository. The model is available for both commercial and non-commercial use under the Llama-3 license. We encourage users to explore the model's capabilities and provide feedback to help us continuously improve its performance and usability.
+The model is available for both commercial and non-commercial use under the Llama-3 license. We encourage users to explore the model's capabilities and provide feedback to help us continuously improve its performance and usability. For more information - please see our detailed blog on Llama-3-SEC.
 
 ## Citation