How to Pick the Right AI Foundation Model
Summary
TLDR本视频脚本介绍了一个AI模型选择框架,用于帮助用户根据特定用例选择合适的生成性AI模型。框架包含六个阶段:明确用例、列出可选模型、评估模型特性、运行测试、选择最佳选项和部署考量。通过比较不同模型的性能、成本和风险,用户可以找到最适合其需求的模型。
Takeaways
- 🎯 明确用例是选择AI模型的首要步骤,需要清晰定义使用场景。
- 📋 在众多基础模型中,选择合适大小的模型比单纯追求最大模型更为重要。
- 💡 考虑模型的成本,包括计算成本、复杂性和可变性。
- 🔍 评估模型时,要考虑模型的大小、性能、成本、风险和部署方法。
- 🚀 测试是评估模型性能的关键,应根据特定用例进行。
- 🏆 选择模型时,应考虑准确性、可靠性和速度这三个关键因素。
- 📊 准确性可以通过选择相关的评估指标来客观和重复地衡量。
- 🛡️ 可靠性涉及模型的一致性、可解释性和避免产生有害内容的能力。
- ⏱️ 速度是用户体验的重要部分,但与准确性往往是一个权衡。
- 📱 部署模型时,需要考虑模型和数据的部署位置和方式。
- 🌐 单一模型可能不适合所有用例,多模型方法可能更适合组织的需求。
Q & A
如何决定使用哪个基础模型来运行生成性AI用例?
-选择基础模型需要考虑模型的训练数据、参数数量、成本、复杂性、可变性等因素。应根据特定用例的需求,通过一个包含六个阶段的AI模型选择框架来评估和选择最合适的模型。
选择最大的基础模型是否总是最佳选择?
-并非总是最佳选择。虽然最大的模型通常具有很好的泛化能力,但它们也会带来计算成本、复杂性和可变性的增加。正确的方法是选择适合特定用例的大小的模型。
AI模型选择框架包含哪些阶段?
-AI模型选择框架包含六个阶段:1) 清晰阐述用例;2) 列出可用的模型选项;3) 确定每个模型的大小、性能、成本、风险和部署方法;4) 针对特定用例评估模型特性;5) 运行测试;6) 选择提供最大价值的选项。
在评估基础模型时应该考虑哪些因素?
-在评估基础模型时,应考虑模型的准确性、可靠性(包括一致性、可解释性和可信度)、避免产生仇恨言论等毒性内容的能力,以及响应用户提交提示的速度。
如何理解模型的准确性?
-模型的准确性指的是生成输出与期望输出的接近程度,可以通过选择与用例相关的评估指标,客观和重复地进行衡量。
为什么预训练基础模型对于特定用例很重要?
-预训练的基础模型经过针对特定用例的微调,如果在与我们用例接近的领域上预训练,它在处理我们的提示时可能表现得更好,使我们能够使用零样本提示获得期望的结果,而无需提供多个示例。
在选择模型时,部署方式如何影响决策?
-部署方式会影响模型的选择,因为不同的部署环境(如公有云或私有云)会涉及不同的成本、控制和安全考虑。例如,选择开源模型并在公有云上进行推理可能成本较低,而私有部署则提供更大的控制和安全性,但成本较高。
多模型方法是什么?
-多模型方法是指为不同的AI用例选择不同的基础模型,以找到模型和用例的最佳组合。这种方法认识到不同的模型和用例有不同的需求和优势。
在实际应用中,如何测试和评估模型的性能?
-在实际应用中,可以通过使用模型处理特定提示,然后根据预定义的评估指标和性能指标来评估模型的输出质量和性能。
在选择基础模型时,如何处理模型的偏见和风险?
-在选择基础模型时,应通过仔细评估模型的训练数据和输出,以及进行充分的测试来识别和减少偏见和风险。此外,选择具有透明度和可追溯性的模型可以帮助建立信任并减少风险。
模型的大小和性能之间存在哪些权衡?
-模型的大小和性能之间存在权衡,因为较大的模型可能提供更高的准确性,但也可能导致更高的计算成本和响应时间。选择模型时,需要找到性能、速度和成本之间的最佳平衡点。
在评估模型时,为什么考虑额外的好处很重要?
-考虑额外的好处,如更低的延迟和对模型输入输出的更大透明度,可以帮助我们更全面地评估模型的价值,并找到最适合特定用例的模型。
Outlines
🤖 选择合适的AI基础模型
本段落讨论了如何从众多AI基础模型中选择适合特定用例的模型。由于不同模型在训练数据和参数数量上存在差异,选择错误可能导致诸如数据偏见或错误生成等问题。建议采用一种AI模型选择框架,该框架包括六个阶段:明确用例、列出模型选项、识别模型特性、评估模型、运行测试和最终选择。以文本生成的用例为例,介绍了如何应用该框架,并提到了两个已有的基础模型:来自Meta的Llama 2和来自IBM的Granite,分别具有不同的参数规模和适用性。
🚀 模型性能、速度与成本的平衡
这一部分深入探讨了在选择AI模型时需要考虑的性能、速度和成本之间的平衡。较大的模型可能提供更准确的答案,但速度较慢,而较小的模型可能在速度上有优势,准确性上的差异不大。通过实际测试选定的模型,可以评估其性能和输出质量。此外,还讨论了模型部署的问题,包括在公共云上进行推理和在本地部署以获得更大的控制权和安全性。最后指出,不同的用例可能适合不同的基础模型,这称为多模型方法,有助于找到最佳的模型和用例组合。
Mindmap
Keywords
💡生成式AI
💡基础模型
💡模型选择框架
💡模型大小
💡性能成本
💡风险
💡部署方法
💡准确性
💡可靠性
💡速度
💡多模型方法
Highlights
选择生成性AI的基础模型时,需要考虑模型的训练数据和参数数量。
选择错误的基础模型可能导致数据偏见或错误输出。
选择最大的模型并不是总是最佳选择,因为它们会带来计算成本、复杂性和可变性成本。
提出一个AI模型选择框架,包含六个简单阶段。
明确阐述你的用例是选择AI模型的第一步。
列出可用的模型选项,并识别每个模型的大小、性能、成本、风险和部署方法。
针对特定用例评估模型特性,并进行测试。
选择为特定用例提供最大价值的模型选项。
用例示例:使用AI生成个性化的营销电子邮件。
评估组织中已经使用的两个基础模型:Llama 2和Granite。
模型卡片可以告诉我们模型是否针对我们的目的进行了训练。
评估模型性能时考虑准确性、可靠性和速度三个因素。
准确性可以通过选择与用例相关的评估指标来客观和重复地衡量。
可靠性涉及模型的一致性、可解释性和可信度,以及避免产生仇恨言论等有毒内容。
速度是指用户提交提示后获得响应的速度,速度和准确性往往是一个权衡。
部署决策因素包括模型和数据的部署位置和方式。
开放源代码模型可以在公共云上进行推理,而私有数据可能需要在本地部署。
组织可能有多个用例,每个用例可能适合不同的基础模型,这就是多模型方法。
Transcripts
If you have a use case for generative AI,
how do you decide on which foundation model to pick to run it?
With the huge number of foundation models out there,
It's not an easy question.
Different models are trained on different data and have different parameter counts,
and picking the wrong model can have severe unwanted impact,
like biases originating from the training data or hallucinations that are just plain wrong.
Now, one approach is to just pick the largest,
most massive model out there to execute every task.
The largest models have huge parameter counts
and are usually pretty good generalists, but with large models come costs,
costs of compute, cost of complexity and costs of variability.
So often the better approach is to pick the right size model for the specific use case you have.
So let me propose to you an AI model selection framework.
It has six pretty simple stages.
Let's take a look at what they areand then give some examples of how this might work.
Now, stage one, that is to clearly articulate your use case.
What exactly are you planning to use generative A.I. for?
From there you'll list some of the model options available to you.
Perhaps there are already a subset of foundation models running that you have access to.
With a short list of models you'll next want to identify each model's size,
performance costs, risks, and deployment methods.
Next, evaluate those model characteristics for your specific use case.
Run some tests.
That's the next stage,
testing options based on your previously identified use case and deployment needs.
And then finally, choose the option that provides the most value.
So let's put this framework to the test.
Now, my use case, we're going to say that is a use case for text generation.
I need the AI to write personalized emails for my awesome marketing campaign.
That's stage one.
Now, my organization is already using two foundation models for other things,
so I'll evaluate those.
First of all, we've got Llama 2
and specifically the Llama 2 70 model. a fairly large model, 70 billion parameters.
It's from meta and I know it's quite good at some text generation use cases.
Then there's also Granite that we have deployed.
Granite is a smaller general purpose model and that's from IBM.
And I know there is a 13 billion parameter model
that I've heard does quite well with text generation as well.
So those are the models I'm going to evaluate, Llama 2 and Granite.
Next, we need to evaluate model size, performance, and risks.
And a good place to start here is with the model card.
The model cards might tell us if the model has been
trained on data specifically for our purposes.
Pre-trained Foundation models are fine tuned for specific use cases
such as sentiment analysis or document summarization or maybe text generation.
And that's important to know because if a model is pre trained
on a use case close to ours, it may perform better when processing our prompts
and enable us to use zero shot prompting to obtain our desired results.
And that means we can simply ask the model to perform tasks
without having to provide multiple completed examples first.
Now, when it comes to evaluating model performance for our use case, we can consider three factors.
The first factor that we would consider is accuracy.
Now, accuracy denotes how close the generated output is to the
desired output, and it can be measured objectively and repeatedly
by choosing evaluation metrics that are relevant to your use cases.
So for example, if your use case related to text translation,
the B.L.E.U. - that's the BiLingual Evaluation Understudy benchmark,
can be used to indicate the quality of the generated translations.
Now the second factor relates to reliably of the model.
Now that's a function of several factors actually, such as consistency,
explainability and trustworthiness,
as well as how well a model avoids toxicity like hate speech.
Reliability comes down to trust,
and trust is built through transparency and traceability of the training data
and accuracy and reliability of the output.
And then the third factor that is speed.
And specifically we're saying
how quickly does a user get a response to a submitted prompt?
Now, speed and accuracy are often a trade off here.
Larger models may be slower, but perhaps deliver a more accurate answer.
Or then again, maybe the smaller model is faster
and has minimal differences in accuracy to the larger model.
It really comes down to finding the sweet spot between performance, speed and cost.
A smaller, less expensive model may not offer
performance or accuracy metrics on par with an expensive one, but
it would still be preferable over the latter.
If you consider any additional benefits, the model might deliver like lower latency
and greater transparency into the model inputs and outputs.
The way to find out is to simply select the model that's likely
to deliver the desired output and well, test it.
Test that model with your prompts to see if it works,
and then assess the model, performance and quality of the output using metrics.
Now, I've mentioned deployment in passing, so a quick word on that.
As a decision factor, we need to evaluate where and how we want the model and data to be deployed.
So let's say that we're leaning towards Llama 2
as our chosen model based on our testing.
Right, cool. Llama 2.
That's an open source model and we could inference with it on a public cloud.
So we've got a public cloud already out here.
It's got an element of choice in it, which is limited to we can just inference to that.
But if we decide we want to fine tune the model with our own enterprise data,
we might need to deploy it on prem.
So this is where we have our own version of Llama two
and we are going to provide fine tuning to it.
Now, deploying on premise gives you greater control,
and more security benefits compared to a public cloud environment.
But it's an expensive proposition,
especially when factoring model size and compute power,
including the number of GPUs it takes to run a single large language model.
Now, everything we've discussed here is tied to a specific use case,
but of course it's quite likely that any given organization will have multiple use cases.
And as we run through this model selection framework,
we might find that each use case is better suited to a different foundation model.
That's called a multi model approach.
Essentially, not all A.I. models are the same, and neither are your use cases.
And this framework might be just what you need to pair the models
and the use cases together to find a winning combination of both.
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