Gemma Fine Tune: A Comprehensive Guide to Fine-Tuning LLMs

Fine-tuning LLMs can be a complex process, but with Gemma Fine Tune, you can simplify it. Gemma Fine Tune is a comprehensive guide that provides a structured approach to fine-tuning Large Language Models (LLMs).

To get started, you need to choose a pre-trained model, which is where Gemma Fine Tune comes in. It offers a range of pre-trained models, each with its own strengths and weaknesses, such as the BERT and RoBERTa models.

The key to fine-tuning is to identify the specific tasks you want to perform, such as sentiment analysis or text classification. By doing so, you can tailor your model to meet your needs.

With Gemma Fine Tune, you can fine-tune your model in a matter of hours, not days or weeks. This is because it provides a streamlined workflow that minimizes the number of parameters to tune.

Gemma fine tune is a powerful tool for developers, and here are some key features that make it stand out:

Optimal performance and compatibility on iOS devices is ensured through platform-specific adaptations, including new ops, quantization, caching, and weight sharing.

The MediaPipe LLM Inference API supports running large language models like Gemma 2B fully on-device across platforms, including iOS.

By leveraging the power of CoreML, developers can integrate Gemma 2B into iOS apps with ease, using the MediaPipeTasksGenai library installed via CocoaPods.

Metal Performance Shaders play a crucial role in accelerating LLM inference on iOS, relying on custom Metal operations to optimize performance.

Techniques like operator fusions and pseudo-dynamism enable efficient execution of attention blocks and dynamic operations on the GPU.

The model's compact size, achieved through advanced compression and distillation techniques, allows it to fit within the device's memory limitations.

iOS developers can take advantage of 8-bit and 4-bit quantization to further reduce memory requirements while maintaining model quality.

Gemma 2B stands out for its balance between size and efficiency compared to other models in the Gemma family. This balance is crucial for maintaining a competitive edge in the market.

The Gemma family has variants with 9 billion and 27 billion parameters, but Gemma 2B is notable for its unique combination of size and efficiency.

As newer models like Meta's Llama 3.1 and OpenAI's GPT-4o are released, Google must focus on refining the Gemma series to stay competitive.

In comparison to other models in the Gemma family, Gemma 2B stands out for its balance between size and efficiency. This is particularly notable when considering the 9 billion and 27 billion parameter variants, which don't quite match Gemma 2B's unique blend.

Google must focus on refining the Gemma series to maintain its competitive edge, especially with newer models like Meta's Llama 3.1 and OpenAI's GPT-4o being released.

You can now load any fine-tuned weights for supported models on both Kaggle and Hugging Face. This means you have access to dozens of Gemma fine-tunes uploaded by Hugging Face users, directly from KerasNLP.

Keras models are available for download and user uploads on Kaggle and Hugging Face. This integration allows for seamless conversion of weights on the fly.

Gemma and Llama3 models are the first to support this feature, but it will eventually work for any Hugging Face Transformers model that has a corresponding KerasNLP implementation.

Try out the Hermes-2-Pro-Llama-3-8B fine-tune using this Colab to see the new feature in action.

LoRA is a technique that reduces the number of trainable parameters in a model, such as Gemma 9B, from 9 billion to 14.5 million by freezing model weights and replacing them with low-rank adapters.

Direct Preference Optimization (DPO) simplifies the alignment process by eliminating the need for an additional reward model, and instead directly fine-tunes the language model on preference data. DPO reformulates the RL policy as a supervised learning task that optimizes the following loss: (πθ - πref)^2 + β * (πref - πθ)^2.

To optimize hyperparameters, DPO training includes several parameters worth experimenting with, such as β and learning rate. A grid search method implemented in Determined can be used to explore how these parameters affect model training, with a total of 9 trials initiated with such settings.

Before we dive into the coding part, let's make sure we have all the necessary libraries installed and configured. We'll be using the KerasHub library, so let's install it first. To reduce memory usage and training time, we'll set the precision to bfloat16.

To access the Gemma model, ensure that your KAGGLE_USERNAME and KAGGLE_KEY are correctly configured. This will allow us to load the pre-trained Gemma-2b model from Google.

Here are the libraries we'll be using:

These libraries will help us automate the training loop, resource provisioning, distributed training, and metrics visualization.

Pre-training is the first step in preparing a Large Language Model (LLM) for use. It involves training the model on a massive dataset of text to minimize the next-token-prediction loss.

This process turns a randomly initialized LLM into something useful, giving it strong text completion capabilities and general language pattern understanding. It can be adapted to more specific tasks via supervised fine-tuning.

The scale of a massive dataset is staggering, with Llama2-7B being trained on 3 trillion tokens. This requires a significant amount of computational power, taking about 3.3 million GPU hours on A100-80GB GPUs.

Typically, we adapt existing high-quality pre-trained models to our use cases, rather than pre-training our own models from scratch. This is because pre-training a model on such a large scale is not an easy feat for a regular user.

To fine-tune Gemma for enhanced performance, we need to meticulously structure the training data.

Our approach involves integrating UBIAI data, specifically focusing on tables and their summarization capabilities.

The dataset's structure plays a crucial role in shaping Gemma's adaptability to this unique domain.

The training data is curated to feature conversational turns within a well-defined format.

This format is characterized by markers that distinctly delineate between user inputs and model responses.

We draw inspiration from UBIAI's rich dataset, ensuring that the integration aligns seamlessly with Gemma's objective of table summarization.

Each exchange is framed by and markers, demarcating user and model inputs.

We would need a CSV file containing a text column for the fine-tuning with Hugging Face AutoTrain.

However, we would use a different text format for the base and instruction models during the fine-tuning.

The fine-tuning process involves using the –train flag to initiate the fine-tuning process for Gemma.

If the fine-tuning process succeeds, we will have a new directory of our fine-tuned model.

Fine-tuning Gemma 2B on iPhones can be achieved through various advanced strategies. LoRA (Low-Rank Adaptation) reduces the computational cost of fine-tuning large language models like Gemma 2B.

Prompt engineering plays a crucial role in optimizing Gemma 2B's performance on iPhones. Carefully crafted prompts can guide the model to generate more accurate and relevant outputs.

Few-shot learning is a technique that can significantly improve Gemma 2B's performance on specific tasks. By providing the model with a small number of examples to learn from.

Continuous learning is another powerful approach for fine-tuning Gemma 2B on mobile devices. By allowing the model to learn from user interactions and feedback, it can adapt and improve over time.

To unlock the full potential of Gemma 2B on iPhones, developers can combine these advanced fine-tuning strategies. This enables the creation of powerful and efficient language model applications.

Fine-tuning Gemma stands as a crucial gateway to unleashing the true power and adaptability of this revolutionary AI model. The ability to fine-tune Gemma opens avenues for customization, enabling developers to address domain-specific challenges and improve model outcomes.

To fine-tune the LLM with Python API, developers need to install the Python package. This can be run using the following code: –peft: Conditionally includes PEFT for additional training robustness.

Gemma's architecture is a key factor in its impressive performance, thanks to its massive vocabulary size of 256,000 words, which dwarfs competitors like Llama 2 with a 32,000-word vocabulary.

The model's training on a colossal 6 trillion token dataset sets it apart from its counterparts, requiring substantial resources. This training data is a crucial aspect of Gemma's development, allowing it to learn and adapt in ways that smaller models can't.

Gemma's architecture also showcases a resemblance to the Gemini models, particularly in its use of Nvidia GPUs for optimization, ensuring industry-leading performance from data centers to the cloud. This collaboration with Nvidia is a testament to Google's commitment to accessibility in AI.

Gemma's architecture is a significant departure from its competitors, boasting a vocabulary size of 256,000 words, dwarfing Llama 2's 32,000-word vocabulary.

Its training on a colossal 6 trillion token dataset sets it apart from its counterparts in terms of resource requirements.

Gemma's resemblance to the Gemini models is evident in its utilization of Nvidia GPUs for optimization, a collaboration that ensures industry-leading performance.

Quantization is a key technique used in Gemma, enabling a reduction in model size and an improvement in inference speed.

Using LoRA, we can significantly reduce the number of trainable parameters in a model. For instance, with 768 parameters and a rank of 4, the dense layer goes from 589,824 trainable parameters to 6,144 trainable parameters.

This reduction in parameters is a major advantage of LoRA, especially for models with a large number of parameters like Gemma 9B. By freezing model weights and replacing them with low-rank adapters, LoRA brings the number of trainable parameters down to 14.5 million.

With fewer trainable parameters, models become more efficient and easier to train, especially on limited hardware. This makes LoRA a valuable technique for developers working with resource-constrained environments.

The training process for Gemma fine-tuning involves several key steps.

To start, we need to load the SFT dataset, which is a crucial part of the process. This dataset is used to fine-tune the model for enhanced performance.

Next, we define the instruction template, which is used to format the user instructions and expected outputs. This template is essential for the model to understand the user's intent.

Optional steps include refining the training data structure, which involves integrating UBIAI data and curating the dataset to feature conversational turns within a well-defined format.

The SFTTrainer builds upon the standard HuggingFace Trainer, adding new parameters that simplify the initiation of training. One such parameter is formatting_func, which processes a batch of examples into a list of strings used for next token prediction training.

Here are the high-level steps involved in creating the SFTTrainer:

The SFTTrainer accepts a TrainingArguments object that holds essential hyperparameters like batch size and learning rate. These hyperparameters are configured in the yaml config file, which can be viewed here.

After creating the SFTTrainer, we need to add the DetCallback that is responsible for visualizing metrics and reporting checkpoints to Determined.