Gemma 3n: On-Device AI with RAG and Function Calling

Gemma 3n: Unveiling the Power Within

Gemma 3n is offered in two distinct parameter variants: Gemma 3n 2B and Gemma 3n 4B. Both iterations are equipped to handle text and image inputs, with audio support slated to be integrated in the near future, according to Google’s projections. This signifies a substantial leap in scale compared to its predecessor, the non-multimodal Gemma 3 1B, which debuted earlier this year and demanded a mere 529MB to manage an impressive 2,585 tokens per second on a mobile GPU.

According to Google’s technical specifications, Gemma 3n leverages selective parameter activation, an innovative technique designed for efficient parameter management. This implies that the two models encompass a greater number of parameters than the 2B or 4B that are actively engaged during inference. This strategic approach optimizes resource utilization and enhances performance.

Fine-Tuning and Quantization: Unleashing Customization

Google underscores the capability for developers to fine-tune the base model and subsequently convert and quantize it using cutting-edge quantization tools accessible through Google AI Edge. This empowers developers to tailor the model to specific applications and optimize its performance characteristics. Fine-tuning, in this context, refers to the process of taking a pre-trained language model and further training it on a smaller, more specific dataset. This allows the model to specialize in a particular task or domain, leading to improved accuracy and performance. For example, a developer might fine-tune Gemma 3n on a dataset of medical texts to create a model that is specifically designed for medical diagnosis or research.

Quantization, on the other hand, is a technique for reducing the size and computational requirements of a neural network. This is achieved by representing the parameters of the network using fewer bits. For example, a 32-bit floating-point number might be quantized to an 8-bit integer. This can significantly reduce the memory footprint and inference time of the model, making it more suitable for on-device deployment. Google AI Edge provides tools for performing quantization on Gemma 3n models, allowing developers to optimize their models for specific hardware platforms. The combination of fine-tuning and quantization allows developers to create highly customized and efficient language models that can be deployed on a wide range of devices.

RAG Integration: Enriching Language Models with Contextual Data

As an alternative to fine-tuning, Gemma 3n models can be deployed for on-device Retrieval Augmented Generation (RAG), a methodology that enriches a language model with application-specific data. This augmentation is facilitated by the AI Edge RAG library, currently exclusive to Android but with plans for expansion to other platforms in the pipeline. RAG represents a powerful paradigm for enhancing the capabilities of language models by integrating them with external knowledge sources. Instead of relying solely on the information encoded in its parameters, a RAG model can retrieve relevant information from a database or other knowledge source and use it to inform its responses. This allows the model to generate more accurate, informative, and contextually relevant responses.

The benefits of RAG are particularly pronounced in scenarios where the language model needs to access up-to-date or domain-specific information that is not already contained in its training data. For example, a RAG model could be used to answer questions about current events, scientific research, or company-specific policies. The AI Edge RAG library simplifies the process of implementing RAG with Gemma 3n models. The library provides a set of tools and APIs for indexing, retrieving, and integrating external data into the language model’s response generation process. The library supports a variety of data sources and retrieval strategies, allowing developers to customize the RAG pipeline to their specific needs.

The RAG library operates through a streamlined pipeline consisting of several key stages:

• Data Import: Ingesting relevant data into the system. This stage involves the process of acquiring data from various sources and preparing it for use in the RAG system. The data can come from a variety of sources, including databases, APIs, web pages, and documents. The data may need to be cleaned, transformed, and formatted before it can be ingested into the system.
• Chunking and Indexing: Segmenting and organizing the data for efficient retrieval. This stage involves dividing the data into smaller chunks and creating an index that allows for efficient retrieval of relevant chunks based on a user’s query. The chunking strategy can vary depending on the type of data and the specific requirements of the application. For example, text data might be chunked into sentences, paragraphs, or sections. The index can be implemented using a variety of data structures, such as inverted indexes, tree-based indexes, or vector indexes.
• Embeddings Generation: Creating vector representations of the data for semantic understanding. This stage involves generating vector embeddings for each chunk of data. The embeddings capture the semantic meaning of the data and allow for efficient similarity search. The embeddings can be generated using a variety of techniques, such as word embeddings, sentence embeddings, or document embeddings.
• Information Retrieval: Identifying and extracting pertinent information based on user queries. This stage involves retrieving the most relevant chunks of data based on a user’s query. The query is also converted into a vector embedding, and the similarity between the query embedding and the data embeddings is used to identify the most relevant chunks.
• Response Generation: Crafting coherent and contextually relevant responses using an LLM. This stage involves using the retrieved chunks of data to generate a coherent and contextually relevant response to the user’s query. The language model uses the retrieved information to augment its own knowledge and generate a response that is both accurate and informative.

This robust framework enables comprehensive customization of the RAG pipeline, encompassing support for custom databases, chunking strategies, and retrieval functions. This allows developers to tailor the RAG pipeline to their specific needs and optimize its performance for their particular application.

AI Edge On-device Function Calling SDK: Bridging the Gap Between Models and Real-World Actions

Concurrently with the unveiling of Gemma 3n, Google introduced the AI Edge On-device Function Calling SDK, initially available solely on Android. This SDK empowers models to invoke specific functions, thereby executing real-world actions. The AI Edge On-device Function Calling SDK represents an important step towards enabling language models to interact with the real world. By allowing models to invoke external functions, the SDK unlocks a wide range of possibilities for creating intelligent and context-aware applications.

To seamlessly integrate an LLM with an external function, the function must be meticulously described by specifying its name, a descriptive narrative elucidating when the LLM should utilize it, and the requisite parameters. This metadata is encapsulated within a Tool object, which is subsequently passed to the large language model via the GenerativeModel constructor. The function calling SDK incorporates support for receiving function calls from the LLM based on the provided description and transmitting execution results back to the LLM. The detailed description of the function is crucial for the language model to understand when and how to use the function effectively. The description should be clear, concise, and informative, providing the model with all the necessary information to make informed decisions about whether to invoke the function.

Exploring the Potential: The Google AI Edge Gallery

For those eager to delve deeper into these groundbreaking tools, the Google AI Edge Gallery stands as an invaluable resource. This experimental application showcases a diverse array of models and facilitates text, image, and audio processing. The Google AI Edge Gallery provides a hands-on experience with Gemma 3n and its associated tools, allowing developers to explore the potential of these technologies and experiment with different use cases.

Diving Deeper: The Nuances of Gemma 3n and its Ecosystem

The advent of Gemma 3n marks a significant stride in the evolution of on-device machine learning, offering a potent combination of efficiency, adaptability, and functionality. Its multimodal capabilities, coupled with support for RAG and function calling, unlock a myriad of possibilities for developers seeking to create intelligent and context-aware applications.

Selective Parameter Activation: A Deep Dive

The selective parameter activation technique employed by Gemma 3n warrants closer scrutiny. This innovative approach allows the model to dynamically activate only the parameters necessary for a given task, thereby minimizing computational overhead and maximizing efficiency. This is particularly crucial for on-device deployment, where resources are often constrained. Selective parameter activation is a key technique for enabling efficient on-device inference with large language models. By only activating the parameters that are relevant to a specific task, the model can reduce its computational footprint and memory requirements, making it possible to run on resource-constrained devices.

The underlying principle behind selective parameter activation lies in the observation that not all parameters in a neural network are equally important for all tasks. By selectively activating only the most relevant parameters, the model can achieve comparable performance with significantly reduced computational cost. This is based on the idea that different parts of a neural network learn to represent different aspects of the input data. For example, in a language model, some parameters might be responsible for processing syntax, while others might be responsible for processing semantics. When processing a specific input, only the parameters that are relevant to the input need to be activated.

The implementation of selective parameter activation typically involves a mechanism for determining which parameters to activate for a given input. This can be achieved through various techniques, such as:

• Attention Mechanisms: Attending to the most relevant parts of the input and activating the corresponding parameters. Attention mechanisms allow the model to focus on the most important parts of the input when making predictions. By attending to the relevant parts of the input, the model can selectively activate the parameters that are responsible for processing those parts.
• Gating Mechanisms: Using a gating function to control the flow of information through different parts of the network. Gating mechanisms allow the model to control the flow of information through different parts of the network. By using a gating function, the model can selectively activate or deactivate different parts of the network based on the input.
• Sparse Training: Training the network to learn sparse connections, so that only a subset of the parameters are active during inference. Sparse training encourages the network to learn sparse connections, meaning that only a subset of the parameters are active during inference. This can be achieved by adding a regularization term to the loss function that penalizes the use of dense connections.

The choice of technique depends on the specific architecture of the model and the characteristics of the task. However, the overarching goal is to identify and activate only the parameters that are most relevant for the given input, thereby reducing computational cost and improving efficiency.

RAG: Augmenting Knowledge and Context

Retrieval Augmented Generation (RAG) represents a paradigm shift in the way language models are used. By integrating external knowledge sources, RAG enables language models to generate more informed, accurate, and contextually relevant responses. RAG addresses the limitations of traditional language models by allowing them to access and incorporate information from external knowledge sources, such as databases, APIs, and web pages. This allows the models to generate more accurate, informative, and up-to-date responses.

The RAG pipeline consists of several key stages:

1. Data Indexing: In this stage, the external knowledge source is indexed to enable efficient retrieval of relevant information. This typically involves creating a vector representation of each document in the knowledge source, which can then be used to quickly identify documents that are similar to a given query. The indexing process involves converting the data into a format that can be efficiently searched and retrieved. This often involves creating a vector representation of each document or chunk of data.
2. Information Retrieval: When a query is received, the RAG system retrieves the most relevant documents from the indexed knowledge source. This is typically done using a similarity search algorithm, which compares the vector representation of the query to the vector representations of the documents in the knowledge source. The retrieval process involves using the index to identify the documents or chunks of data that are most relevant to the user’s query. This is typically done using a similarity search algorithm, which compares the query vector to the document vectors and retrieves the documents with the highest similarity scores.
3. Contextualization: The retrieved documents are then used to augment the context of the query. This can be done by simply concatenating the retrieved documents to the query, or by using a more sophisticated technique to integrate the information from the retrieved documents into the query representation. The contextualization process involves integrating the retrieved information into the context of the query. This can be done by simply concatenating the retrieved documents to the query, or by using a more sophisticated technique to integrate the information from the retrieved documents into the query representation.
4. Response Generation: Finally, the augmented query is fed into a language model, which generates a response based on the combined information from the query and the retrieved documents. The response generation process involves using the language model to generate a response based on the augmented query. The language model uses its internal knowledge and the information from the retrieved documents to generate a response that is both accurate and informative.

RAG offers several advantages over traditional language models:

• Increased Accuracy: By incorporating external knowledge, RAG models can generate more accurate and factual responses. RAG models can access and incorporate information from external knowledge sources, which allows them to generate more accurate and factual responses.
• Improved Contextual Understanding: RAG models can better understand the context of a query by leveraging the information inthe retrieved documents. RAG models can leverage the information in the retrieved documents to better understand the context of the user’squery. This allows them to generate responses that are more relevant and informative.
• Reduced Hallucinations: RAG models are less likely to hallucinate or generate nonsensical responses, as they are grounded in external knowledge. RAG models are less likely to hallucinate or generate nonsensical responses because they are grounded in external knowledge.
• Adaptability to New Information: RAG models can easily adapt to new information by simply updating the indexed knowledge source. RAG models can easily adapt to new information by simply updating the indexed knowledge source. This makes them more robust and adaptable than traditional language models.

Function Calling: Interacting with the Real World

The AI Edge On-device Function Calling SDK represents a significant step towards enabling language models to interact with the real world. By allowing models to invoke external functions, the SDK unlocks a wide range of possibilities for creating intelligent and context-aware applications. Function calling is a powerful mechanism that allows language models to interact with external systems and services. This enables the models to perform real-world actions, such as retrieving information from databases, controlling devices, and sending messages.

The function calling process typically involves the following steps:

1. Function Definition: The developer defines the functions that the language model can invoke. This includes specifying the name of the function, a description of what the function does, and the parameters that the function accepts. The function definition provides the language model with the necessary information to understand how to use the function.
2. Tool Object Creation: The developer creates a Tool object that encapsulates the function definition. This object is then passed to the language model. The Tool object serves as a container for the function definition and allows the language model to access the function’s metadata.
3. Function Call Generation: When the language model needs to perform a real-world action, it generates a function call. This call includes the name of the function to be invoked and the values of the parameters to be passed to the function. The function call is generated by the language model based on its understanding of the user’s query and the available functions.
4. Function Execution: The function call is then executed by the system. This typically involves invoking the corresponding API or service. The function execution is performed by the system, which typically involves invoking the corresponding API or service.
5. Result Transmission: The results of the function execution are then transmitted back to the language model. The results of the function execution are transmitted back to the language model, allowing it to incorporate the information into its response.
6. Response Generation: Finally, the language model uses the results of the function execution to generate a response. The language model uses the results of the function execution to generate a response that is both accurate and informative.

The function calling SDK enables language models to perform a wide range of tasks, such as:

• Accessing Information from External Sources: The model can call functions to retrieve information from databases, APIs, and other external sources. This allows the model to access up-to-date and domain-specific information that is not already contained in its training data.
• Controlling Devices and Appliances: The model can call functions to control smart home devices, such as lights, thermostats, and appliances. This enables the model to automate tasks and create a more seamless and intuitive user experience.
• Performing Transactions: The model can call functions to perform financial transactions, such as making payments and transferring funds. This allows the model to facilitate commerce and provide a more convenient way for users to manage their finances.
• Automating Tasks: The model can call functions to automate complex tasks, such as scheduling appointments and sending emails. This can save users time and effort and improve their productivity.

The Google AI Edge Gallery: A Showcase of Innovation

The Google AI Edge Gallery serves as a vital platform for showcasing the capabilities of Gemma 3n and its associated tools. By providing an interactive environment where developers can experiment with these technologies, the gallery fosters innovation and accelerates the development of new applications. The Google AI Edge Gallery provides a hands-on experience with Gemma 3n and its associated tools, allowing developers to explore the potential of these technologies and experiment with different use cases.

The gallery features a diverse array of models and demos, showcasing the potential of Gemma 3n for various tasks, such as:

• Image Recognition: Identifying objects and scenes in images.
• Natural Language Processing: Understanding and generating human language.
• Speech Recognition: Transcribing spoken language into text.
• Audio Processing: Analyzing and manipulating audio signals.

The gallery also provides access to the AI Edge SDKs, enabling developers to integrate these technologies into their own applications. This allows developers to build their own applications that leverage the power of Gemma 3n and its associated tools.

The Future of On-Device Machine Learning

The emergence of Gemma 3n and its accompanying ecosystem heralds a new era for on-device machine learning. By combining efficiency, adaptability, and functionality, Gemma 3n empowers developers to create intelligent and context-aware applications that can run directly on devices, without the need for a constant internet connection. On-device machine learning offers several advantages over cloud-based machine learning, including increased privacy, reduced latency, and improved reliability.

This has profound implications for various industries, including:

• Mobile: Enabling more intelligent and responsive mobile applications. On-device machine learning can enable more intelligent and responsive mobile applications by allowing them to process data locally, without the need for a constant internet connection.
• IoT: Powering smart devices that can operate independently and autonomously. On-device machine learning can power smart devices that can operate independently and autonomously, without relying on a cloud connection.
• Automotive: Enhancing the safety and convenience of autonomous vehicles. On-device machine learning can enhance the safety and convenience of autonomous vehicles by allowing them to process sensor data in real time, without the need for a cloud connection.
• Healthcare: Improving the accuracy and efficiency of medical diagnosis and treatment. On-device machine learning can improve the accuracy and efficiency of medical diagnosis and treatment by allowing doctors to analyze patient data locally, without the need to send it to the cloud.

As on-device machine learning technologies continue to evolve, we can expect to see even more innovative and impactful applications emerge in the years to come. Gemma 3n represents a significant step in this journey, paving the way for a future where intelligence is seamlessly integrated into our everyday lives.