sparse / ms-swift /examples /infer /demo_reward_model.py
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# Copyright (c) Alibaba, Inc. and its affiliates.
import os
from typing import List
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
def infer_batch(engine: 'InferEngine', infer_requests: List['InferRequest']):
resp_list = engine.infer(infer_requests)
print(f'messages0: {infer_requests[0].messages}')
print(f'response0: {resp_list[0].choices[0].message.content}')
if __name__ == '__main__':
from swift.llm import InferEngine, InferRequest, PtEngine, load_dataset
model = 'Shanghai_AI_Laboratory/internlm2-1_8b-reward'
engine = PtEngine(model, max_batch_size=64)
# Here, `load_dataset` is used for convenience; `infer_batch` does not require creating a dataset.
dataset = load_dataset(['AI-ModelScope/alpaca-gpt4-data-zh#1000'], seed=42)[0]
print(f'dataset: {dataset}')
infer_requests = [InferRequest(**data) for data in dataset]
infer_batch(engine, infer_requests)
messages = [{
'role': 'user',
'content': "Hello! What's your name?"
}, {
'role': 'assistant',
'content': 'My name is InternLM2! A helpful AI assistant. What can I do for you?'
}]
infer_batch(engine, [InferRequest(messages=messages)])