The next generation of Gemma models is now available in KerasNLP.
Overview
The article discusses the release of the Gemma 2 model with 27 billion parameters, highlighting its capabilities in Keras and integration with JAX for efficient model training. It covers fine-tuning techniques, including LoRA for reduced parameter training, and updates on Hugging Face integration for accessing fine-tuned models.
What You'll Learn
1
How to fine-tune the Gemma 2 model using Keras and JAX
2
Why using LoRA can significantly reduce the number of trainable parameters in large models
3
How to implement distributed fine-tuning using Keras' ModelParallel API
Prerequisites & Requirements
- Understanding of machine learning model training and fine-tuning techniques
- Familiarity with Keras and JAX frameworks
Key Questions Answered
What are the main features of the Gemma 2 model in Keras?
Gemma 2 is available in two sizes, 9B and 27B parameters, with both standard and instruction-tuned variants. It leverages Keras and JAX for efficient training and fine-tuning, making it suitable for large-scale machine learning tasks.
How does distributed fine-tuning work with Keras and JAX?
Distributed fine-tuning in Keras using JAX involves partitioning model weights across multiple accelerators. The ModelParallel API allows users to specify how weights are sharded, enabling the training of large models that exceed single-device memory limits.
What is LoRA and how does it affect model training?
LoRA (Low Rank Adaptation) is a technique that freezes model weights and replaces them with low-rank adapters, significantly reducing the number of trainable parameters. For example, it reduces the trainable parameters in the Gemma 9B model from 9 billion to just 14.5 million.
How can Keras models be integrated with Hugging Face?
Keras models can now load fine-tuned weights from Hugging Face, allowing users to access various models directly. This integration means that weights are converted on the fly, making it easier to utilize fine-tuned models like Gemma and Llama3.
Key Statistics & Figures
Number of parameters in Gemma 9B model
9 billion
This is the original size of the model before applying LoRA, which reduces trainable parameters significantly.
Number of trainable parameters after applying LoRA
14.5 million
This reduction allows for efficient training on limited hardware.
Technologies & Tools
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Framework
Keras
Used for building and training the Gemma 2 model.
Framework
Jax
Provides numerical computing capabilities for scaling model training.
Platform
Hugging Face
Integration allows loading fine-tuned models and weights directly into Keras.
Key Actionable Insights
1Utilize the ModelParallel API in Keras for distributed training of large models.This approach allows you to efficiently train models that are too large for a single device, leveraging multiple accelerators to improve performance and reduce training time.
2Implement LoRA to reduce the number of trainable parameters in your models.By using LoRA, you can significantly decrease the computational resources required for training, making it feasible to fine-tune large models on limited hardware.
3Explore the integration of Keras with Hugging Face for accessing a wider range of models.This integration enables you to leverage community-contributed fine-tunes, enhancing your model's capabilities without starting from scratch.
Common Pitfalls
1
Failing to properly configure weight partitioning can lead to inefficient training.
Without correct partitioning, models may not utilize available hardware effectively, resulting in longer training times and potential out-of-memory errors.
Related Concepts
Distributed Training Techniques
Fine-tuning Large Language Models
Integration Of Machine Learning Frameworks