wehub-resource-sync
32 lines
1.2 KiB
Python
32 lines
1.2 KiB
Python
# Copyright (c) ModelScope Contributors. All rights reserved.
|
|
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 import InferEngine, InferRequest, TransformersEngine, load_dataset
|
|
model = 'Shanghai_AI_Laboratory/internlm2-1_8b-reward'
|
|
engine = TransformersEngine(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)])
|