Files
vllm/examples/basic/offline_inference/reward.py
wang.yuqi dcf8862fd4 [Examples][1/n] Resettle basic examples. (#35579)
Signed-off-by: wang.yuqi <yuqi.wang@daocloud.io>
Signed-off-by: wang.yuqi <noooop@126.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
2026-03-08 20:22:53 -07:00

53 lines
1.4 KiB
Python

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from argparse import Namespace
from vllm import LLM, EngineArgs
from vllm.utils.argparse_utils import FlexibleArgumentParser
from vllm.utils.print_utils import print_embeddings
def parse_args():
parser = FlexibleArgumentParser()
parser = EngineArgs.add_cli_args(parser)
# Set example specific arguments
parser.set_defaults(
model="internlm/internlm2-1_8b-reward",
runner="pooling",
enforce_eager=True,
max_model_len=1024,
trust_remote_code=True,
)
return parser.parse_args()
def main(args: Namespace):
# Sample prompts.
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
# Create an LLM.
# You should pass runner="pooling" for reward models
llm = LLM(**vars(args))
# Generate rewards. The output is a list of PoolingRequestOutput.
outputs = llm.reward(prompts)
# Print the outputs.
print("\nGenerated Outputs:\n" + "-" * 60)
for prompt, output in zip(prompts, outputs):
rewards = output.outputs.data
print(f"Prompt: {prompt!r}")
print_embeddings(rewards, prefix="Reward")
print("-" * 60)
if __name__ == "__main__":
args = parse_args()
main(args)