We code Stanford’s ALPACA LLM on a Flan-T5 LLM (in PyTorch 2.1)
If you are interested in natural language processing and machine learning, then you may have heard of the ALPACA LLM developed by Stanford University. This powerful language model has gained a lot of attention for its ability to understand and generate human-like text.
One popular way to implement the ALPACA LLM is by using PyTorch, a popular open-source machine learning library. In this article, we will walk you through the process of coding the ALPACA LLM on a Flan-T5 LLM using PyTorch 2.1.
Setting up the Environment
Before we dive into the code, it is important to set up the environment for our project. First, make sure you have PyTorch 2.1 installed on your machine. You can do this using pip:
pip install torch==2.1
Next, we need to download the Flan-T5 LLM pre-trained model. You can find this model on the official PyTorch website. Once you have the model, you can load it into your project using the following code:
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained('t5-base')
tokenizer = T5Tokenizer.from_pretrained('t5-base')
Coding the ALPACA LLM
Now that we have our environment set up, we can start coding the ALPACA LLM. This process involves fine-tuning the Flan-T5 LLM using the ALPACA dataset provided by Stanford.
First, download the ALPACA dataset from the Stanford website and load it into your project. Then, you can start fine-tuning the Flan-T5 LLM using the following code:
from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained('t5-base')
tokenizer = T5Tokenizer.from_pretrained('t5-base')
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = torch.nn.CrossEntropyLoss()
# Fine-tune the model using the ALPACA dataset
With this code, you can fine-tune the Flan-T5 LLM to generate human-like text using the ALPACA dataset. This process may take some time depending on the size of the dataset and your hardware, but the results are usually worth it.
Conclusion
Implementing Stanford’s ALPACA LLM on a Flan-T5 LLM in PyTorch 2.1 is a powerful way to generate human-like text and understand natural language processing. By following the steps outlined in this article, you can start coding your own ALPACA LLM and explore the capabilities of this cutting-edge language model.
Why dont you give your colab code link too? :
Can we use it for text classification
Please do this for the new Dolly Dataset. That would be epic
Do you have the Google collab notebook for this?
Great videos ! The new cool kid in the neighbourhood
great video mate. Sorry to ask but can you provide the COLAB? Thanks once again for your knowledge. Cheers
How much $$$ did the training cost you?
Your videos are a really great man. Am pretty new to these LLMs and starting to get around the idea of this AI revolution. What would be the best way to reach out to you if I had any questions in mind🤔?
I am huge fan of your videos. i am non programmer but have big interest in this stuff. I wanna request to actually give us a walkthrough on how to prepare our own data like some image pdf files to convert into vector embeddings and use contextual injections to make our own bots even for personal purposes. One more thing I am confused about is how to prepare examples to train our AI to do specific tasks like writing a report in a specified manner and use the specified vocabulary. Is there any written resource where I could learn that? Please reply. 😊
This is amazing. Huge thanks for this. Any chance you've got a link to the notebook? Btw, I'm getting a CUDA out of memory error on a machine with 24GB of GPU RAM. Any chance you've got a pointer I might be missing?
I didn't understand a THING ! What is this ? What is it used for ? CONTEXT PLEASE !
Love your videos, been following for a while.
Have you done any benchmarking to compare the Flan-T5 based ALPACA to the LLaMa based ALPACA? Curious to know what the final performance of the Flan-T5 open model is in comparison to the original ALPACA model
Model is hosted on huggingface can use it in pipeline
Next time i hope the video about Alpaca lora
Thanks for doing the live coding exercise and leaving the mistakes in.
Sometimes. I wonder if it's only me who has experiences like that😅
Thanks again!