From be2e1632fdc58596a13018b3348696ef5d56fbd1 Mon Sep 17 00:00:00 2001
From: Sage Moore <sage@neuralmagic.com>
Date: Thu, 3 Jul 2025 13:01:01 +0000
Subject: [PATCH] delete basic-ub.py

Signed-off-by: Sage Moore <sage@neuralmagic.com>
---
 examples/basic-ub.py | 64 --------------------------------------------
 1 file changed, 64 deletions(-)
 delete mode 100644 examples/basic-ub.py

diff --git a/examples/basic-ub.py b/examples/basic-ub.py
deleted file mode 100644
index 3f6fd2fdb82ea..0000000000000
--- a/examples/basic-ub.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-import logging
-import os
-
-from vllm import LLM, SamplingParams
-
-# Sample prompts.
-prompts = [
-    "Hello, my name is",
-    "The president of the United States is",
-    "The capital of France is",
-    "The future of AI is",
-]
-# Configure logging level for vllm (optional, uses VLLM_LOGGING_LEVEL env var).
-logging_level = os.getenv("VLLM_LOGGING_LEVEL", "").upper()
-if logging_level:
-    logging.basicConfig(level=getattr(logging, logging_level, logging.INFO))
-
-# Create a sampling params object, optionally limiting output tokens via MAX_TOKENS env var.
-param_kwargs = {"temperature": 0.8, "top_p": 0.95}
-max_tokens_env = os.getenv("MAX_TOKENS")
-if max_tokens_env is not None:
-    try:
-        param_kwargs["max_tokens"] = int(max_tokens_env)
-    except ValueError:
-        raise ValueError(f"Invalid MAX_TOKENS value: {max_tokens_env}")
-sampling_params = SamplingParams(**param_kwargs)
-
-
-def main():
-    # Create an LLM.
-    model = "deepseek-ai/DeepSeek-V2-Lite"
-    # model = "facebook/opt-125m"
-    llm = LLM(model=model, 
-              enforce_eager=True,
-              compilation_config=2,
-              ###############
-              trust_remote_code=True,
-              max_model_len=1024,
-              #load_format="dummy",
-              ###############
-              #tensor_parallel_size=1,
-              data_parallel_size=2,
-              enable_expert_parallel=True,
-              ###############
-              #enable_microbatching=True, 
-    )
-    # Generate texts from the prompts.
-    # The output is a list of RequestOutput objects
-    # that contain the prompt, generated text, and other information.
-    outputs = llm.generate(prompts, sampling_params)
-    # Print the outputs.
-    print("\nGenerated Outputs:\n" + "-" * 60)
-    for output in outputs:
-        prompt = output.prompt
-        generated_text = output.outputs[0].text
-        print(f"Prompt:    {prompt!r}")
-        print(f"Output:    {generated_text!r}")
-        print("-" * 60)
-
-
-if __name__ == "__main__":
-    main()