RAG optimized PEFT-LoRA: Your Questions answered
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- เผยแพร่เมื่อ 28 ก.ย. 2024
- RAG in relation to PEFT-LoRA? What is the optimal RAG PEFT-LoRA config?
In this video: RAG and PEFT-LoRA explained and their optimal relation for any external data augmentation LLM optimization.
Large Language Models. Domain-specific training of embeddings and fine-tuning RAG Retriever, and RAG Re-Ranker (Cohere).
Reference to cohere embed v3, LLamaIndex and other LangChain tools.
My GPT-4 chat (with 3 Agents to discuss RAG and PEFT-LoRA) to download and continue with your own discussion:
chat.openai.co...
#gpt4
#ai
#explanation
#finetuning
By far one of the best intro and explanations I’ve seen in months!!!
Had to come back to the final part, but i wanted to give a very heart-felt thank you for producing this video. The picture of Pre-training, Fine-tuning, Peft-Lora, and RAG really solidified all of the information i've been learning as a beginner. Thank you!
thank you, all your videos are very useful and informative
very nice and smooth explanation love your content, keep it up
Thank you for one more very quality video. How can you produce so many quality content? Are you a machine 😊?
Dude, you are the single most slept on YT channel today without exceptions regardless of subject matter. I'm not super smart or anything, so maybe I'm punching above my class here, but sometimes I have to do something mindless for roughly 30 minutes before I watch your videos... Perhaps if I continue to watch them faithfully one day your videos will become small morsels, but for now your videos are like a Thanksgiving meal with the family... Afterward you are stuffed, it takes days to digest fully, and then you a week's worth of left overs to work through. Seems like there are a lot of people who are mentally starving while there is a feast all the time on your channel.
Golden Retriever
Heyy, do you have twitter ? I'm making a Twitter bot to track all the paper study content on TH-cam, I want to tag you
I wonder if we'll see an incremental method to adaptive perf... So that every new video or paper that comes out could be incorporated into the llm's knowledge. Will make for a great expert ai that is always caught upto latest research
Humm, wrong reply ... But I think this is already in project ;)
one big downside of PEFT over RAG is that you can not quickly verify if the answer is a hallucination or not. Because the connection between input data and llm output is lost. if you use RAG with keyword search this is still available. With fine tuning this connection is thrown away. Even though it is very important. If you take the reductive view of RAG as embeddings I would agree with you. Then PEFT is definitely better.
The screen shot failed to download, could you kindly reload it? Thanks.
The chat is gone, anyone have the chat exported?
thank you sir
First of all, I hugely appreciate your videos, I could not get this type of depth and insights anywhere else. About the reference to how important is RAG (~ min 28) I get that RAG is a drop in the ocean when compared to the training efforts and even the fine-tuning efforts. To me PEFT is just a light technique to fine tune the model, I understand that the technique used could make them "different" but the purpose is the same in essence, no? (you fine-tune to train on a skill/task or in a domain) and you can do this with PEFT, specially for domain training. The way I see it, RAG might be smaller in what the dataset size might be, from this perspective I would get the picture, even if RAG could be larger than PEFT as well... what is so important about RAG, therefore the marketing, is that it would enable access to knowledge and frequent updates on your "enterprise specific datasets". This could be very small in size indeed (when compared to other layers) but the importance of this data might be of critical importance to the service that is being built. As a second point, I am in fact a bit confused about all this area of fine-tuning the embeddings, I understand those "fine-tunings" would be rather of the PEFT type, but still, I am not sure about how this would complexify the need for frequent updates of the knowledge base (say weekly). With RAG there are techniques that allow updates of the deltas without having to re-run the whole RAG datasets again and again, but I am not sure if we can run these PEFT fine-tunings only on small updates, updates include new data (probably ok for the PEFT) but also corrections or changes to existing knowledge, and for those I am wondering how PEFT would work...
Here for another code_your_own_AI classic.
If someone were to train a LoRA using the data retrieved by RAG, what format should be employed? Training LoRA with raw, unformatted retrieved data might lead to the augmented model not adhering to the previously fine-tuned chat format correctly. Have I overlooked anything, or is there a solution to address this issue?
Thank you for this video. Diagrams you've used in it is very nice, easy for me to understand the concept.
I wonder if we'll see an incremental method to adaptive perf... So that every new video or paper that comes out could be incorporated into the llm's knowledge. Will make for a great expert ai that is always caught upto latest research
Detailed explanation. Another problem trying to solve is reading 10K code file with continuous updates. Looking for help, can you please share your contact