Add examples to token-in-token-out for LLM (#4010)

docs/backend/server_arguments.md

@@ -52,7 +52,7 @@ Please consult the documentation below to learn more about the parameters you ma
 * `chat_template`: The chat template to use. Deviating from the default might lead to unexpected responses. For multi-modal chat templates, refer to [here](https://docs.sglang.ai/backend/openai_api_vision.html#Chat-Template).
 * `is_embedding`: Set to true to perform [embedding](https://docs.sglang.ai/backend/openai_api_embeddings.html) / [encode](https://docs.sglang.ai/backend/native_api.html#Encode-(embedding-model)) and [reward](https://docs.sglang.ai/backend/native_api.html#Classify-(reward-model)) tasks.
 * `revision`: Adjust if a specific version of the model should be used.
-* `skip_tokenizer_init`: Set to true to provide the tokens to the engine and get the output tokens directly, typically used in RLHF. Please see this [example for reference](https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/input_ids.py).
+* `skip_tokenizer_init`: Set to true to provide the tokens to the engine and get the output tokens directly, typically used in RLHF. Please see this [example for reference](https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/token_in_token_out_llm.py).
 * `json_model_override_args`: Override model config with the provided JSON.
 * `delete_ckpt_after_loading`: Delete the model checkpoint after loading the model.
 

examples/runtime/engine/token_in_token_out_llm.py

@@ -1,5 +1,5 @@
 """
-This example demonstrates how to provide tokenized ids as input instead of text prompt
+This example demonstrates how to provide tokenized ids to LLM as input instead of text prompt, i.e. a token-in-token-out workflow.
 """
 
 import sglang as sgl
@@ -24,14 +24,13 @@ def main():
     token_ids_list = [tokenizer.encode(prompt) for prompt in prompts]
 
     # Create an LLM.
-    # You can also specify `skip_tokenizer_init=True`, but it requires explicit detokenization at the end
-    llm = sgl.Engine(model_path=MODEL_PATH)
+    llm = sgl.Engine(model_path=MODEL_PATH, skip_tokenizer_init=True)
 
     outputs = llm.generate(input_ids=token_ids_list, sampling_params=sampling_params)
     # Print the outputs.
     for prompt, output in zip(prompts, outputs):
         print("===============================")
-        print(f"Prompt: {prompt}\nGenerated Text: {output['text']}")
+        print(f"Prompt: {prompt}\nGenerated token ids: {output['token_ids']}")
 
 
 # The __main__ condition is necessary here because we use "spawn" to create subprocesses