Ya if u see training the model from scratch for your dataset might look better and optimal but the energy is used in training a model from scratch is too much so finetuning a pretrained model is considered a better option than training model for specific data everytime....
Summary of the course. Course Overview: This crash course by Krish Naak covers theoretical concepts and practical implementation of fine-tuning large language models (LLMs), including techniques such as quantization, LoRA, and CLA PFT. Fine-Tuning Techniques: The course discusses different fine-tuning methods like quantization-aware training, matrix decomposition, and domain-specific fine-tuning for various applications like chatbots. Technical Concepts: Explains floating-point precision (FP32, FP16), tensor data types in TensorFlow, and quantization methods (e.g., 4-bit normal float) used to optimize model performance and memory usage. Implementation Steps: Demonstrates the process of preparing datasets, configuring training parameters (like optimizer, learning rate), and using the LoRA configuration for fine-tuning models such as LLaMA 2. Practical Application: Provides a hands-on example of loading datasets, setting up the training environment, and fine-tuning a model using custom data, with plans to push the fine-tuned model to platforms like Hugging Face.
After the fine tuning process in this video, isn't it the same old model that is used here test the queries? We should have tested the queries with the "new_model" isn't it?
Hi Krish, the video is really good and more understanding. but I have one reason how to you choose this the right dataset and why? why you choosing that format_func function to format the dataset into the some kind of format. if you have any tutorial or blog please share the link.
Anyone getting stuck at the import it is "from transformers import AutoModelForCausalLM" I kept getting error for reading it "casual" instead of "causal"😭
Hello Sir, Hello, could you create a tutorial on fine-tuning vision-language models like LLaVA or Multimodal LLMs like IDEFICS for Visual Question Answering on datasets like VQA-RAD, including evaluation metrics? Please make a full step by step tutorial. Thanks in Advance!
actually sir this step cant able to run !pip install -q datasets !huggingface-cli login due to this dataset cant be load nd getting error in other step so is thier is any solution for this ?????
Krish, most of the fine tuning done by the existing dataset from HF. however converting the dataset as per the format its a challenging for any domain dataset. How we can train our own data to finetune the model so that accuracy ll be even better. Any thoughts?
Hi Krish, i Have seen entire video. i am confused with 2terms. some times you said its possible to train with my own data (own data refers from a url , pdfs , simple text etc) but when you are trying to train the llm model you are giving inputs as in certain format like### question : ans. Now if i want to train my llm in real life scenario i don't have my data in this instruction format right in that case what to do. And its not possible to transform my raw text to into that format right how to handle that situation . is it a only way to fine tune in specific format or i can train given in raw text format i know a process where i need to convert my text to chunks then pass to llm. those are really confusing can you clear those things
Krish...yet again!! I was just looking for your finetuning video here and you uploaded this..I cant thank you enough..really 👍😀
Can we connect brother. I am new into generative AI and wanted to know the basics .
Awesome presentation Krish !!!! You are a superstar!!!
just getting your video at the right time !! Cudos brother
Krish Naik respect Button❤
Thanks Krish it's very helpful
can we apply Lora for bert please reply
Thanks man!
i dont know why i feel training a whole model from scratch is much more easier for me than to fine tune it ..............
Ya if u see training the model from scratch for your dataset might look better and optimal but the energy is used in training a model from scratch is too much so finetuning a pretrained model is considered a better option than training model for specific data everytime....
Hi @krishnaik06,
Thank you again for anther Crash Course.
may I know which tools/software are you using for presentation?
Summary of the course.
Course Overview: This crash course by Krish Naak covers theoretical concepts and practical implementation of fine-tuning large language models (LLMs), including techniques such as quantization, LoRA, and CLA PFT.
Fine-Tuning Techniques: The course discusses different fine-tuning methods like quantization-aware training, matrix decomposition, and domain-specific fine-tuning for various applications like chatbots.
Technical Concepts: Explains floating-point precision (FP32, FP16), tensor data types in TensorFlow, and quantization methods (e.g., 4-bit normal float) used to optimize model performance and memory usage.
Implementation Steps: Demonstrates the process of preparing datasets, configuring training parameters (like optimizer, learning rate), and using the LoRA configuration for fine-tuning models such as LLaMA 2.
Practical Application: Provides a hands-on example of loading datasets, setting up the training environment, and fine-tuning a model using custom data, with plans to push the fine-tuned model to platforms like Hugging Face.
Can anybody tell me how to fine-tune llm for multiple tasks?
GREAT WORK SIR! Love from ghaziabad
After the fine tuning process in this video, isn't it the same old model that is used here test the queries? We should have tested the queries with the "new_model" isn't it?
Can you make a good video around how to decide hyper parameters when training gpt 3.5
Krish bro ❤
Hi Krish, the video is really good and more understanding. but I have one reason how to you choose this the right dataset and why? why you choosing that format_func function to format the dataset into the some kind of format. if you have any tutorial or blog please share the link.
i am unable to make gradient ai account it says not allowed while signing up
Please make a complete playlist to secure a job in the field of Ai
Thank you krish
You are awesome ❤
Anyone getting stuck at the import it is
"from transformers import AutoModelForCausalLM"
I kept getting error for reading it "casual" instead of "causal"😭
Krish, how to retrieve data from an API, like OpenWheatherData, instead of retrieving from Google and Wikipedia?
Hello Sir, Hello, could you create a tutorial on fine-tuning vision-language models like LLaVA or Multimodal LLMs like IDEFICS for Visual Question Answering on datasets like VQA-RAD, including evaluation metrics?
Please make a full step by step tutorial. Thanks in Advance!
Hi krish, one question from my side..can I fine tune gpt2 for text classification
Hi sir, I have tried your llama finetuning notebook to run on colab with free T4 gpu but it is throwing OOM error. So could you please guide
Thank you for an amazing course as always. Can we please get these notes as well. they are really good for quick revision.
actually sir this step cant able to run
!pip install -q datasets
!huggingface-cli login
due to this dataset cant be load nd getting error in other step
so is thier is any solution for this ?????
we want more video on fine tuning projects
hey krish , can you by any chance share the notes used in the video. would be really helpful. thanks !!
Krish, most of the fine tuning done by the existing dataset from HF. however converting the dataset as per the format its a challenging for any domain dataset. How we can train our own data to finetune the model so that accuracy ll be even better. Any thoughts?
hey could you tell me what are the pre req to follow this crash course? it would be greatly beneficial!!
If i would like to join data science community group where i can get the access, please let me know.
Please also provide the source. Research paper/Blog you might have referred for this video.
RAG or fine-tuning? How should one decide?
Hi Krish. What device do you use to write on...like a board
Great TH-cam ssshhhaaaaanel for LLM
how to finetune and quantize the phi3 mini model ,
Hi Krish, i Have seen entire video. i am confused with 2terms. some times you said its possible to train with my own data (own data refers from a url , pdfs , simple text etc) but when you are trying to train the llm model you are giving inputs as in certain format like### question : ans.
Now if i want to train my llm in real life scenario i don't have my data in this instruction format right in that case what to do. And its not possible to transform my raw text to into that format right how to handle that situation . is it a only way to fine tune in specific format or i can train given in raw text format i know a process where i need to convert my text to chunks then pass to llm. those are really confusing can you clear those things
full respect bro , from morocco MA.
What documentation did you refer to in this video?
hello krish sir thank you for amazing lecture can please share the notes of session
Amazing as always! So great tutorials and clear explanations! Thank you!
Full Respect to you Krish, Great video !!
Amazing content, big fan of you :) Much love from Hawaii
Prerequisite ?
@krishnaik06 WANDB_Disabled is for disabling weights and Bais of the current model
EK HI DIL HAI
KITNE BAAR JITOGE SIR?
Pre-requisites?
How to deploy these?...I have seen deployment of custom LLM models...how to do this?
Thanks you very much sir🎉🎉🎉
Thank you very much Krish for uploading this.
Thank you so much for such an comprehensive tutorial. Really love your teaching style. Could you also refer some books on LLM fine tuning.
Generative AI on AWS by Chris Fregly and Shelbee Eigenbrode
its a good one
Can anyone suggest how to analyze audio for soft skills in speech using Python and llm models?
🙏💯💯
Brilliant brilliant 🙌
Big salute!
Hello Krishna sir ,
Please make a playlist for genai and lanchain
Already made please check
@@krishnaik06 Thank you for replying me
I understand this video just like your hairs sometime nothing some time something ❤🫠