I want to know how to make a multi-turn dialogue such as: import time import asyncio from PIL import Image from typing import List, Dict from vllm import SamplingParams, AsyncEngineArgs, AsyncLLMEngine MODEL_PATH = 'path' def load_model_and_tokenizer(model_dir: str): engine_args = Asyn...
dice_list = []##determine whether we are in a mobile platformplatform = get_platform(request.get_signed_cookie('platform','x86'))#if we are playing the cpu then ourturnis always 1, otherwise we can find it stored in a cookie and in the dbplaying_cpu = int(request.GET.get('playing...
self.visBorderSystemIDs.add(exploration_center)forsys_idinlist(self.visBorderSystemIDs):# This set is modified during iteration.iffo.currentTurn() <50:print"Considering border system %s"% universe.getSystem(sys_id) ExplorationAI.follow_vis_system_connections(sys_id, exploration_center) newly_explo...
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{ "activities": activities_list} # Convert data from dict to string, then Byte Literal, before doing a Base-64 encoding data_str = str(data).replace("'",'"') createPipeline_json = data_str.encode(encoding="utf-8") createPipeline_Json64 = base64.b64encode(createPipeline_json) # ...
Up to you. Roadmap: Short-term: Write tests and add CI (including typechecking) Support multi-band assets Easier access to s3://-style URIs (right now, you'll need to pass in gdal_env=stackstac.DEFAULT_GDAL_ENV.updated(always=dict(session=rio.session.AWSSession(...))) Utility to gu...
labels, the code forgenerate_examples()would look a bit different. Instead of iterating over all the files, you would need to (a) iterate over the rows in the csv file and (b) convert each row to a dictionary using.todict()— to create theexampleslist. See below for a dummy ...
目录一、转换为字符串1.1 list 转 string1.2 dict 转 string1.3 其他序列 → 字符串 二、转换为列表2.1 string→ list2.2 dict → list三、转换为字典3.1 string→ dict3.2 list → dict四、其他a. 转换为 int b. 转换为 float一、转换为字符串1.1 list 转 s python中list转string python 字符串 json ...
raise KeyError("Expected key `target` to instantiate.") return get_obj_from_str(config["target"])(**config.get("params", dict())) class WrappedDataset(Dataset): """Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset""" def __init__(self, dataset)...
{ "activities": activities_list} # Convert data from dict to string, then Byte Literal, before doing a Base-64 encoding data_str = str(data).replace("'",'"') createPipeline_json = data_str.encode(encoding="utf-8") createPipeline_Json64 = base64.b64encode(createPipeline_json) # ...