Developing an LLM: Building, Training, Finetuning
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- čas přidán 23. 07. 2024
- REFERENCES:
1. Build an LLM from Scratch book: mng.bz/M96o
2. Build an LLM from Scratch repo: github.com/rasbt/LLMs-from-sc...
3. Slides: sebastianraschka.com/pdf/slid...
4. LitGPT: github.com/Lightning-AI/litgpt
5. TinyLlama pretraining: lightning.ai/lightning-ai/stu...
DESCRIPTION:
This video provides an overview of the three stages of developing an LLM: Building, Training, and Finetuning. The focus is on explaining how LLMs work by describing how each step works.
OUTLINE:
00:00 - Using LLMs
02:50 - The stages of developing an LLM
05:26 - The dataset
10:15 - Generating multi-word outputs
12:30 - Tokenization
15:35 - Pretraining datasets
21:53 - LLM architecture
27:20 - Pretraining
35:21 - Classification finetuning
39:48 - Instruction finetuning
43:06 - Preference finetuning
46:04 - Evaluating LLMs
53:59 - Pretraining & finetuning rules of thumb - Věda a technologie
I am your fan, I have most of your books, thanks for this excellent video ! Another evaluation metric that I found interesting in another channel was to make the LLMs to play chess against each other 10 times.
Hah nice, that's a fun one. How do you evaluate who's the winner, do you use a third LLM for that?
Your articles and videos have been extremely helpful in understanding how LLMs are built. Building LLM from Scratch and Q and AI are resources that I am presently reading and they provide a hands-on discourse on the conceptual understanding of LLMs. You, Andrej Karpathy and Jay Alammar are shining examples of how learning should be enabled. Thank you!
Thanks for the kind comment!
What wonderful Tech Minds : { Sebastian Raschka, Yann LeCun, Andrej Karpathy, ...} who share their works and beautiful ideations for Mere mortal like me... Sebastian's teachings are so, so fundamental that takes fear off my clogged mind... 🙏
Although I am struggling to build LLMs for specific & niche areas, I am confidant of cracking them with great resources like : Build a Large Language Model (From Scratch)!!!
The hero of open source
Haha, thanks! I've learned so much thanks to all the amazing people in open source, and I'm very flattered by your comment to potentially be counted as one of them :)
Thank you Sebastian for your awesome contributions. You're a big inspiration.
Very nice video, I liked it so much that I preordered your new book directly after watching it (to be fair I have read your blog for some time now).
Thanks! I hope you are going to like the book, too!
Thanks for the great knowledge You are sharing
great content! love it ❤
the greatest Liverpool fan ever! ⚽
Haha nice, at least one person watched it until that part :D
Oh, my lord, my favourite machine learning author is a Liverpool fan.😎
Haha, nice that people make it that far into the video 😊
@@SebastianRaschka You'll never walk alone 🙂
Big fan ❤
Hi, nice videos! One question for my understanding. When talking about embedding dimensions such as 1280 in "gpt2-large" do you mean the size of the number vector encoding the context of a single token or the number of input tokens? When comparing gpt2-large and Lama2 the number is the same for the ".. embeddings with 1280 tokens".
Good question, the term is often used very broadly and may refer to the input embeddings or the hidden layer sizes in the MLP layer. Here, I meant the size of the tokens that are embedded.
That's great Dr. Hope you will be doing good.
I wish if i could download your deep learning book which is published recently. If there is any open source link to download it please mention in comments.
Thanks and regards,
Researcher at Texas
When is your whole book coming out ? Eagerly waiting 😅
Thanks for your interest in this! It's already available for preorder (both on the publisher's website and Amazon) and if the production stage goes smoothly, it should be out by the end of of August
Would
you make videos about time series and trannsformer?
in the instruction fine tuning we propagate loss only on output text tokens? or for all tokens from start to EOS?
That's a good question. You can do both. By default all tokens, but more commonly you'd mask the tokens. In my book, I include the token masking as a reader exercise (it's super easy to do). There was also a new research paper a few weeks ago that I discussed in my monthly research write-ups here: magazine.sebastianraschka.com/p/llm-research-insights-instruction
@@SebastianRaschka
Thanks for the reply
I just have a general question: do we use masking? For example, was masking used during the instruction fine-tuning of LLaMA 3 or mistral any Open source LLMs? Also, does your book include any chapters on the parallelization of training large language models?
@@bashamsk1288 Masking is commonly used, yes. We implement it as the default strategy in LitGPT. In my book we do both. I can't speak about Llama 3 and Mistral regarding masking, because while these are open-weight models they are not open source. So there's no training code we can look at. My book explains DDP training in the PyTorch appendix, but it's not used in the main chapters because as a requirement all chapters should also work on a laptop to make them accessible to most readers.
I'm interested in SLM RAG with Knowledge graph traversal/search for RAG dataset collection and vector-JIT semantic match for hybrid search. Any repos you think I would be interested in?
bookmarked, clear and concise.
Unfortunately I don't have a good recommendation here. I have only implemented standard RAGs without knowledge graph traversal.
I've heard about instruct learning, and it sounds similar to how you define preference learning. I have also heard about transfer learning. How would you compare/define those?
Transfer learning is basically involved in everything you do when you start out with a pretrained model. We don't really name or call it out explicitly anymore because it's so common. In instruction finetuning, the loss function is different from preference tuning mainly. Instruction finetuning trains the model to answer queries, and preference finetuning is basically more about the nuance of how these get answered. All preference tuning methods that are used today (DPO, RLHF+PPO, KTO), etc. expect you to have done instruction finetuning on your model before you preference finetune.
@@SebastianRaschka Thanks for explaining it. I need to do a lot more research :p
Great Video. Now that LLM is so powerful , will regular machine learning & deep learning slowly vanish?
Great question. I do think that special purpose ML solutions still have and will continue to have their place. The same way ML didn't make certain more traditional statistics based models obsolete. Regarding deep learning ... I'd say LLM is a deep learning model itself. But yeah, almost everything in deep learning is nowadays either a diffusion model, transformer-based model (vision transformer and most LLMs), or state space model
Should we give prompt to LLM when fine tuning for classification with last layer modification or directly pass the input to the LLM like in deberta?
Thanks for the comment, could you explain a bit more what you mean by passing the input directly?
@@SebastianRaschka Hey, sorry for the bad language. I meant should the chat formats like alpaca etc be applied or we give the text as it is to LLM for classification.
@@ArbaazBeg Oh I see now. And yes, you can. I wanted to create an example and performance comparison for that to the GitHub repo (github.com/rasbt/LLMs-from-scratch) some time. For that I wanted to first instruction-finetune the model on a few more spam classification instructions and examples though.
Im sold , im buying your book .. would love to chat with you sometime if possible
Thanks, hope you are liking it! Are you going to SciPy in July by chance, or maybe Neurips end of the year?
@@SebastianRaschka unfortunately not,but I'd like to have a zoom/google meet chat with you if possible
What we are seeking not only fuzzing code
Glad that's useful
Hello sir, please also make with javascript
🤌
I take that as a compliment!? 😅😊
@@SebastianRaschka Yes yes! It was supposed to be a compliment only. You are doing great work with our teaching materials :).
Easier said than done unless u got a GPU super computer lying around lol
ha, I should mention that all chapters in my book run on laptops, too. It was a personal goal for me that everything should work even without a GPU. The instruction finetuning takes about ~30 min on a CPU to get reasonable results (granted, the same code takes 1.24 min on an A100)