Update README.md

README.md

@@ -30,7 +30,7 @@ This optimization reduces the number of bits per parameter from 16 to 8, reducin
 Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a linear scaling per output dimension maps the FP8 representations of the quantized weights and activations.
 [AutoFP8](https://github.com/neuralmagic/AutoFP8) is used for quantization with a single instance of every token in random order.
 
-<!-- ## Deployment
+## Deployment
 
 ### Use with vLLM
 
@@ -50,7 +50,7 @@ messages = [
     {"role": "user", "content": "Who are you? Please respond in pirate speak!"},
 ]
 
-prompts = tokenizer.apply_chat_template(messages, tokenize=False)
+prompts = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
 
 llm = LLM(model=model_id)
 
@@ -60,7 +60,7 @@ generated_text = outputs[0].outputs[0].text
 print(generated_text)
 ```
 
-vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details. -->
+vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
 
 ## Creation