Get started with HuggingFace Transformers - Pipeline, Custom Pipeline, Tokenizer, Model, Hub
Вставка
- Опубліковано 3 сер 2024
- The Transformers architecture is fundamental to almost all the Large Language Models (LLMs) out there like Chat-GPT, LLAMA, Vicuna and you name it. The transformers is the main building block of these models.
If you want to get hands-on and train or fine-tune LLMs you cannot escape working with the Transformers Python library from HuggingFace.
So In this video, I will walk you through the Transformers library so that you can get up to speed working with it.
⌚️ ⌚️ ⌚️ TIMESTAMPS ⌚️ ⌚️ ⌚️
0:00 - Intro
1:05 - Installation
1:25 - Pipeline
3:46 - Create Custom Pipeline
5:59 - AutoClass
8:28 - Tokenizer
11:30 - Model
13:39 - Saving the Model
14:55 - HuggingFace Hub
15:22 - Extro
RELATED LINKS
Notebook for this video: github.com/ai-bites/generativ...
HuggingFace Transformers: huggingface.co/docs/transform...
Transformers github repo: github.com/huggingface/transf...
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hugging face transformers = spaghetti code :)
hah.. thanks for your comment. like it or not, most people are using it. Would you recommend any other library to say fine-tune an LLM?
@@AIBites maybe lighting stuff idk I worked on hf and man the code is radioactive - at some point some else will take over and make a better framework. But the user facing APIs are great, cannot say I'd want to trave place with the people working there now
I have worked with Lighting on the other hand and it's a debugging nightmare. For me, implementing everything in PyTorch wins over anything else!
@@AIBites oh really? I've been using lighting for a long time, what was your issue? Curious to hear it, I also have a lot of contacts there so I'd be happy to share it with them and hopefully they can come up with something better
looks like the community is moving fast towards HF now. For example, Google releasing its gemma models on HF hub and also collaborating with HF.
I am just making videos on whichever could get more viewership :)