Update README.md

README.md

@@ -474,6 +474,7 @@ The code used to generate the dataset can be found [here](https://github.com/pre
 - Given a conversation, we extract all tuples `(context_messages, function_calls)` and use it to generate predictions. We ignore the `content` field and only evaluate `function_calls` generated by an LLM.
 - We use vLLM deployment with `tool_choice="auto"`.
 
+
 ## Metrics
 
 Given a list of predicted and reference function calls, we report two metrics:
@@ -482,6 +483,9 @@ Given a list of predicted and reference function calls, we report two metrics:
 
 EM is a strict metric, and penalizes string arguments in function calls that may be "okay", e.g. `"email_content": "This is an example."` v/s `"email_content": "This is an Example."`, both only differ by one letter.
 
+## Deployment with vLLM
+
+`vllm serve ojus1/Qwen3-1.7B-Instruct --enable-lora --lora-modules prem-research/Funcdex-1.7B=prem-research/Funcdex-1.7B  --enable-auto-tool-choice --tool-call-parser hermes`
 
 # Quickstart