Meta's LLAMA 3 with Hugging Face - Hands-on Guide | Generative AI | LLAMA 3 | LLM
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- Опубликовано: 20 апр 2024
- Dive into the future of generative AI with our detailed guide on how to access Meta's LLAMA 3 using Hugging Face. This video provides a step-by-step walkthrough to help you harness the power of one of the most advanced language models available today.
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My first hands-on, programming tutorial that I’ve walked through in 20 years. Very well done (followed on LinkedIN). I had trouble with the Pipeline, max tokenizer return. I’ll troubleshoot more later. Ran after I removed that Pipeline attribute. Well done - thanks for your time making these videos. Excellent introduction/hands on to AI programming.
Bro. In machine learning by using svm or lr, if i give any input it shows reults as 1. It doesn't show 0.And it shows some warning messages like you reached maximum iteration . Limit reached. What can I do for this bro..
Very neat explanation. Great job 👏
I have faced an issue: RuntimeError: No GPU found. A GPU is needed for quantization.
How to use Few-shots in meta-llama/Meta-Llama-3-8B and Dataset in .csv file?
What if I want to add functional calling and retrieval?
Will the pipeline stay text generator?
Thank you very much!
Hi, thank you very much and really appreciate the knowledge you are sharing through this video. Would it possible to know, how to fine-tune a LLM in q&a format dataset please?
is llama3 have filters too ? like nfsw conversasion or any thing crime
is it possible to do this without having colab pro?
Hello @Siddhardhan
It was a very informative video and helped me a lot to understand the entire code. Can you please make a video on How can we fine tune the LLama 3 using RAG while the code structure will be the same as this video (It will help to understand the entire process at a time).
Sir can u upload any project for genai other than RAG ... Or any idea for any other project than RAG
text summarization, email auto-response can be good use cases to start with.
First off, Great content. Please make a new tutorial with gradio and explain how can we integrate it api .. !!!
Can you make a video on fine tuning using hugging face models on custom set or kaggle dataset ? Either inage or nlp
did you find a way to finetune?
Showing invalid upi id😔
"Your request to access this repo has been successfully submitted, and is pending a review from the repo's authors."
How much time will it take??
edit: 10 minutes :D
I'm sorry, can you further explain the role of tokenizer.pad_token = tokenizer.eos_token? Script only runs when this line is removed.
same