安阳网友!farsun，APP数据做错误不给提现，各种理由说是操作失误，受騙真相

核心解决方法：farsunApp无法提现时，应保持冷静、收集证据，并通过合法途径（如投诉、维权）同时请及时与我们专业团队联系

下面我们来说一下追回的方法和步骤:

1. 控制情绪冷静装作不知情，不要和客服发生争吵，也别去催对方，你要假装相信他们。这样做是为了迷惑对方，防止对方冻结账号。

2. 让对方看到你身上的价值，通过咨询客服的过程中，不经意的向客服吹嘘自己的经济实力。（往不差钱的方向面说，但要把握分寸）。以此来迷惑对方、之后你只要再输点出去， 是接近本金之后再提款，只要是人都会有贪念，利用对方想从你身上获取更大利益的心理从而达到提取目的。

3. 通过软件藏分的手段把分数化整为零。就是把号上的分用软件隐藏起来之后，过段时间再小额充值一点进去用以打开出款通道，然后再等段时间分批的提出，注意提款次数和提款金额要适当，一旦被发现账号就有可能被冻结。注意事项：保持冷静，不和客服争吵，防止号被冻结。稳住客服情绪，要让平台觉得你还在正常游戏。忽悠客服，适当装傻，假装经济雄厚。只要可以登陆，可以转换额度，剩下的交他，他将会帮你把损失挽回。

farsun是诈骗公司，是骗子公司。

〖请点击进入咨询帮助平台提现追款〗

〖请点击进入咨询帮助平台提现追款〗

〖请点击进入咨询帮助平台提现追款〗

You:HowdoLLM'sworkbehindthescenes?

Assistant:LargeLanguageModels(LLMs)likeGPT-4,Claude,orLLaMAarecomplexAIsystemsthatgeneratehuman-liketext.Here’sasimplifiedbreakdownofhowtheyworkbehindthescenes:

1.Architecture:TransformerModel

MostmodernLLMsarebasedontheTransformerarchitecture(introducedinthe2017paper"AttentionIsAllYouNeed").Keycomponents:

-Self-AttentionMechanism:Allowsthemodeltoweightheimportanceofdifferentwordsinasentencerelativetoeachother(e.g.,understandingthat"it"refersto"thecat"inasentence).

-Feed-ForwardNeuralNetworks:Processtheweightedinputsfromattentionlayers.

-PositionalEncodings:SinceTransformersdon’tprocesswordssequentially(unlikeolderRNNs),theseencodingshelptrackwordorder.

2.TrainingProcess

LLMsgothroughtwomainphases:

A)Pre-training(The"Knowledge"Phase)

-Themodelistrainedonvastamountsofpubliclyavailabletext(books,websites,code,etc.)topredictthenextwordinasequence(ataskcalledmaskedlanguagemodelingorautoregressivemodeling).

-Trainingisunsupervised—itlearnsstatisticalpatterns,grammar,facts,andreasoningbyprocessingbillionsofsentences.

-Thisphaserequiresenormouscomputepower(thousandsofGPUs/TPUsandmonthsoftraining).

B)Fine-tuning(The"Alignment"Phase)

-Afterpre-training,modelsarefine-tunedwithhumanfeedback(e.g.,ReinforcementLearningfromHumanFeedback,orRLHF)tomakethemmoreuseful,safe,andalignedwithinstructions.

-Thishelpsreduceharmfulornonsensicaloutputs.

3.TextGeneration("Inference")

-WhenyoupromptanLLM,itdoesn’t"retrieve"answers—itpredictsplausiblecontinuations,wordbyword.

-Techniqueslikebeamsearchandsampling(e.g.,temperatureandtop-ksampling)influenceresponsecreativityvs.consistency.

-Noreal-timelearning:Oncedeployed,themodel'sweightsarefixed(unlessfine-tunedfurther).

4.KeyEnablers

-Scale:Massivedatasets(trillionsoftokens)+hugemodels(billions/trillionsofparameters).

-Compute:Specializedhardware(GPUs/TPUs)forparallelprocessing.

-EfficiencyTricks:

-Tokenization(breakingtextintosubwords,e.g.,"unhappy"→["un","happy"]).

-Quantization(reducingnumericalprecisiontosavememory).

-DistributedTraining(splittingmodelacrossmultiplemachines).

5.Limitations

-Notrueunderstanding:LLMsdon’t"know"facts—theypredictlikelytextpatterns.

-Hallucinations:Theycangeneratefalsebutplausible-soundinginformation.

-Biasu0026Safety:Reflectbiasesandimperfectionsintrainingdata.

TL;DR

LLMsaregiantneuralnetworkstrainedonhugetextdatasetstopredictwords.Their"knowledge"iscompressedstatisticsfromtraining,andtheygeneratetextviaprobability-basedguesses.TheTransformerarchitectureandmassivescalemakethempowerful,butthey’restillfarfromhuman-likereasoning.

Wouldyoulikeadeeperdiveintoanypart?