Update README.md

README.md

@@ -49,12 +49,75 @@ NexusRaven is an open-source and commercially viable function calling LLM that s
 ## NexusRaven model usage
 NexusRaven accepts a list of python functions. These python functions can do anything (including sending GET/POST requests to external APIs!). The two requirements include the python function signature and the appropriate docstring to generate the function call. 
 
-NexusRaven is highly compatible with langchain. See [langchain_example.py](https://huggingface.co/Nexusflow/NexusRaven-13B/blob/main/langchain_example.py). An example without langchain can be found in [non_langchain_example.py](https://huggingface.co/Nexusflow/NexusRaven-13B/blob/main/non_langchain_example.py)
+NexusRaven is highly compatible with langchain. See [langchain_example.py](https://huggingface.co/Nexusflow/NexusRaven-13B/blob/main/langchain_example.py). An example without langchain can be found in [non_langchain_example.py](https://huggingface.co/Nexusflow/NexusRaven-13B/blob/main/non_langchain_example.py).
 
 Please note that the model will reflect on the answer sometimes, so we highly recommend stopping the model generation at a stopping criteria of `["\nReflection:"]`, to avoid spending unnecessary tokens during inference, but the reflection might help in some rare cases. This is reflected in our langchain example. 
 
+More information about how to prompt the model can be found in [prompting_readme.md](prompting_readme.md). 
+
 The "Initial Answer" can be executed to run the function.
 
+### Quickstart
+You can run the model on a GPU using the following code. 
+```python
+# Please `pip install transformers accelerate`
+from transformers import pipeline
+
+
+pipeline = pipeline(
+    "text-generation",
+    model="Nexusflow/NexusRaven-13B",
+    torch_dtype="auto",
+    device_map="auto",
+)
+
+prompt_template = """
+<human>:
+OPTION:
+<func_start>def hello_world(n : int)<func_end>
+<docstring_start>
+\"\"\"
+Prints hello world to the user.
+
+Args:
+n (int) : Number of times to print hello world.
+\"\"\"
+<docstring_end>
+
+OPTION:
+<func_start>def hello_universe(n : int)<func_end>
+<docstring_start>
+\"\"\"
+Prints hello universe to the user.
+
+Args:
+n (int) : Number of times to print hello universe.
+\"\"\"
+<docstring_end>
+
+User Query: Question: {question}
+
+Please pick a function from the above options that best answers the user query and fill in the appropriate arguments.<human_end>
+"""
+prompt = prompt_template.format(question="Please print hello world 10 times.")
+
+result = pipeline(prompt, max_new_tokens=100, return_full_text=False, do_sample=False)[0]["generated_text"]
+
+# Get the "Initial Call" only
+start_str = "Initial Answer: "
+end_str = "\nReflection: "
+start_idx = result.find(start_str) + len(start_str)
+end_idx = result.find(end_str)
+function_call = result[start_idx: end_idx]
+
+print (f"Generated Call: {function_call}")
+```
+This will output: 
+```text
+Generated Call: hello_world(10) 
+```
+Which can be executed. 
+
 
 ## Training procedure