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Modern Natural Language Processing: From Understanding the Transformer Architecture to Practical LLM Fine-Tuning

Do you know the basics but feel overwhelmed when it comes to applying them in practice? Drawing on real-world experience, we make complex LLM architectures easy to understand.

(4.9) 31 reviews

605 learners

Level Intermediate

Course period Unlimited

Deep Learning(DL)
Deep Learning(DL)
NLP
NLP
AI
AI
LLM
LLM
Deep Learning(DL)
Deep Learning(DL)
NLP
NLP
AI
AI
LLM
LLM

130_Transformer Practice Code Troubleshooting

It seems that Google has recently released the latest version of Tensorflow in an incomplete state that has not been completely backtested with the existing source. I have solved the problem by downgrading tensorflow from the current 2.17.0 to the previous stable 2.15.0 version as follows. I have also updated the github source.

 

# Delete the current tensorflow version

!pip uninstall --y tensorflow

# tensorflow version downgrade

!pip install tensorflow==2.15.0

# Check if downgrade was done

import tensorflow as tf

print(tf.__version__)

 

# Install package matching the downgraded version of TensorFlow

!apt install --allow-change-held-packages libcudnn8=8.1.0.77-1+cuda11.2

# Install the compatible cuDNN version for CUDA 11.8

!apt install --allow-change-held-packages libcudnn8=8.1.0.77-1+cuda11.2

# Uninstall existing TensorFlow and related packages

!pip uninstall -y -q tensorflow keras tensorflow-estimator tensorflow-text

# Install the required protobuf version

!pip install protobuf~=3.20.3

# Install TensorFlow Datasets

!pip install -q tensorflow_datasets

# Install TensorFlow 2.15.0 and TensorFlow Text 2.15.0

!pip install -q -U tensorflow-text==2.15.0

 

By doing this, you can complete the training task in Colab within 1 hour based on T4 GPU.

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