What is Prompt Tuning?
StableDiffusionの魅力を伝える記事を書くライターとして、Prompt tuningはAI基礎モデルを新しい下流タスクに適応させる効率的で低コストな方法であり、モデルの再トレーニングや重みの更新が不要です。この動画では、Martin Keenが事前にトレーニングされたLLMを特殊化するための3つのオプション（ファインチューニング、プロンプトエンジニアリング、プロンプトチューニング）について説明し、将来的なプロンプトエンジニアとしてのキャリアについて考えています。
IBM Cloudで無料で始める → https://ibm.biz/sign-up-now
今後の動画をもっと見るために登録する → http://ibm.biz/subscribe-now
Prompt tuningは、AI技術を進化させる上で非常に興味深い方法であり、StableDiffusionはその革新的な側面を探求するプラットフォームです。この先端技術は、既存のモデルをカスタマイズして新しいタスクに適用する際に大きな効率性と柔軟性を提供します。Fine tuningやprompt engineeringなどの手法を活用することで、ユーザーは迅速かつ効果的にAIモデルを最適化し、最新の課題に対応することが可能です。
また、IBM Cloudを利用すればPrompt tuningや他のAI関連技術へ簡単かつ無料でアクセスすることができます。将来的な発展やキャリアパスに興味がある方は、このような革新的な技術やプラットフォームに積極的に触れてみることをお勧めします。
StableDiffusionとPrompt tuningはAI分野で注目されるべき先進的なテクノロジーであり、これらの概念を理解し活用することで次世代の人工知能ソリューションへの道筋が見えてくるかもしれません。是非IBM Cloud上で試してみてください。
Really like all of Martins videos but this one only explains what prompt-tuning is not.
"A string of numbers is worth a thousand words" tf does that even mean?
Excelente video, bom trabalho
Im stunned how Martin is able to write backwards on this board so efficiently
🙂
Soft peompring is confusing
Can you send me your email id i would like to work as prompt engineer for building AI model for various usecases.
I learned Python in 2 months, great language. Then, I learned the SQLs that Python plays well with. Then, it hit me: AI is doing most of this work! So what is there for me and you to do? "My career may be over before it's begun". Yes, indeed UNLESS we can start using Python for regular business processing, like Accounts Receivable/Payable, Inventory Management, Order Processing, etc. In other words, we can't all be doing AI, especially when it, itself, is doing AI, cheaper, faster, and better.
Excellent job explaining key AI terms!
Could you explain labeling done in fine tuning and prompt tuning?
Could you please outline the advantages and disadvantages of fine-tuning versus prompting in the context of large language models?
not fully understood except- prompt tuning-prompt engineering- hard tuning-soft tuning. 😛
Nomenclature is it's own problem.
Awesome content. Thanks for uploading.
It's great that the video calls out the differences between soft prompting and hard prompting. While soft prompts offer more opportunities for performance tuning, practitioners often face the following issues:
– Choosing between hard prompting with a more advanced, but closed, LLM versus soft prompting with an open-sourced LLM that is typically inferior in performance.
– Soft prompting is model dependent, and hard prompting is less so.
A lot to unpack here. Great job explaining.
I have one question about the difference between incontext learning and prompt tuning with hard prompts. Are they synonymous?
Wow, you managed to make an 8 minute video on prompt tuning without actually talking about what it is or how one would even begin to implement it. All I gleaned from this is that it has something to do with embeddings… Do better IBM…
Very concise and information, but tell me, what technology do you use to write backwards so fast? Do you flip the board in post-production?
how do you get the AI to generate that tunable soft prompt?
What is unfancy design prompt?
As a newbee how come I be pro in propmt engineering
This is too abstract. Some concrete examples would have helped.
Is he writing backwards
Does anyone know how they do these videos where it appears that they are writing on the screen. That is so neat!
Excellent broad explanation of complex AI topics. One can then deep dive once a basic understanding is achieved ! Thank you
What data set for supervised learning is used in prompt tuning
How do you discover the correct soft prompts?
crisp and informative
More important question, what type of smart/whiteboard are you using?? I love it!
You should make a guide on FlowGPT / Poe that delves into operators, delimiters, markdown, formatting, and syntax. I've been experimenting on these sites for a while, and the things they can do with prompts are mind-blowing.
This that soft prompt is basically a trainable parameters, which also undergoing backpropagation and its weights are updated? Just like LoRA method, where you attach new trainable parameters to the model and train only those new parameters.
So how do I get to those "soft prompts"? Do you have to use prelabeled examples for that?
Thanks for the explanation, but still how could someone succeed in prompt engineering practically
A joke by ChatGPT:
Why did the Large Language Model (LLM) turn down a job as a DJ?
Because it thought "Prompt Tuning" meant it would have to constantly change the music!
IBM could go for the plunge and make a Quantum computer with 10 million Quantum computer chips with 1000 Qubits and optical circuits instead of just one chip.
ChatGPT is not an LLM lol
Could you please explain a little detail about the strings of numbers how those are indexed? Are those some sort of abstraction that we fully understand!
Very informative lecture is this one… Probably everyone should have a little expertise in prompt engineering skill in near future.
We are trying to create a chatbot using OpenAI API and the response should be limited to the specific topic and it should not respond to the user queries which are not related to the topic. What is the best way to achieve this ? Prompt engineering or prompt tuning ?
Hi, nice talk by the way, but what about some examples of soft turning, i understand is human unreadable, but how exactly you achieve that ? by writing some code ? extra tools ? plugins ? thanks a lot for your reply 🙂
funny & informative 👏👏👏
Really like these summarization videos on this channel. While they do not go into depth, I appreciate the overarching concepts being outlined and put into context in a clean way without throwing overly specific stuff in the mix.