2024-07-12
한어Русский языкEnglishFrançaisIndonesianSanskrit日本語DeutschPortuguêsΕλληνικάespañolItalianoSuomalainenLatina
In today's rapidly developing artificial intelligence, the pursuit of efficient and high-performance language models has prompted the Google DeepMind team to develop the breakthrough model RecurrentGemma. This new model is detailed in the paper "RecurrentGemma: An Efficient Open Language Model Beyond Transformers", which promises to redefine the standard of language processing by combining linear recurrence and local attention mechanisms.
The architecture of the RecurrentGemma model is the core of its high performance. It is based on the Griffin architecture proposed by Google DeepMind, which provides new possibilities for processing language tasks by combining linear recursion and local attention mechanisms. When exploring the model architecture of RecurrentGemma in depth, we first need to understand the foundation of the Griffin architecture and how RecurrentGemma innovates and optimizes on its basis.
RecurrentGemma makes a key modification to the Griffin architecture that involves the processing of input embeddings. The input embeddings of the model are multiplied by a constant that is equal to the square root of the model width. This processing adjusts the input side of the model but does not change the output side because the output embeddings do not have this multiplication factor applied. This adjustment allows the model to process information more efficiently while keeping the model width consistent. This modification plays an important role in the mathematical representation and information flow of the model. Not only does it optimize the model's initial processing of the input data, but by adjusting the scale of the embeddings, it helps the model better capture and represent the characteristics of language.
The performance and efficiency of the RecurrentGemma model are largely determined by its hyperparameters. These hyperparameters are a key part of the model definition, and they include but are not limited to the following aspects:
Table 1 provides a summary of these key hyperparameters, and a more detailed model definition can be found in the Griffin paper by De et al. Together, these hyperparameters form the basis of the RecurrentGemma model, enabling it to efficiently process long sequences while maintaining a small memory footprint.
Through careful modification of the Griffin architecture and meticulous adjustment of hyperparameters, the Recurrent Gemma model not only demonstrated its advancement in theory, but also proved its efficiency and powerful language processing capabilities in practical applications.
RecurrentGemma-2B’s pre-training uses 2 trillion tokens. Although this amount of data is smaller than the 3 trillion tokens used by Gemma-2B, it still constitutes a huge dataset, providing rich language information for the model.
The data sources for pre-training are mainly web documents, mathematics, and code in English. These data not only cover a wide range of topics and fields, but are also carefully screened and cleaned to reduce unwanted or unsafe content and exclude personal or sensitive data. In addition, to ensure the fairness of the evaluation, all evaluation sets are excluded from the pre-training dataset.
RecurrentGemma-2B first uses a large mixture of general data in pre-training, and then turns to a smaller but higher-quality dataset for further training. This phased training approach helps the model learn general language representations on a wide range of data, which are then refined and optimized through more specialized data.
After pre-training, RecurrentGemma-2B was fine-tuned through instruction adjustment and the RLHF algorithm. This process aims to optimize the model so that it can better follow instructions and generate responses with high rewards.
Instruction Tuning is a training method that enables the model to understand and respond to specific instruction formats. RecurrentGemma-2B is trained to follow a specific dialogue format, which is defined by specific control tags, such as user input and model output are marked with different tags.
The RLHF algorithm is an advanced fine-tuning technique that optimizes the model's output through a reinforcement learning framework. In RLHF, the model's output is evaluated based on human feedback and adjusted based on the evaluation results to improve the quality and reward of the output. This algorithm enables the model to learn how to generate more appropriate responses in different contexts.
Through instruction adjustment and RLHF fine-tuning, RecurrentGemma-2B is not only able to generate high-quality language output, but also performs well in conversation and following instructions. This training method provides flexibility and adaptability for the model, enabling it to play a role in a variety of application scenarios.
In this way, RecurrentGemma-2B becomes a powerful language model that can provide efficient and accurate language processing capabilities in a variety of tasks and environments.
Automated benchmarks are the first step in evaluating the performance of RecurrentGemma-2B. These tests cover a variety of popular downstream tasks, including but not limited to question answering, text summarization, language reasoning, etc. The performance of RecurrentGemma-2B on these tasks is compared with Gemma-2B, and the results show that although RecurrentGemma-2B is trained with a smaller number of tokens, its performance is comparable to Gemma-2B.
RecurrentGemma-2B performs similarly to Gemma-2B in multiple academic benchmarks such as MMLU 5-shot, HellaSwag 0-shot, and PIQA 0-shot, demonstrating its versatility and effectiveness in different tasks. These test results not only demonstrate the model's deep understanding of language, but also reflect its potential in practical applications.
In addition to automated benchmarks, RecurrentGemma-2B was also tested with human evaluation, a critical step in assessing whether a language model can generate responses that meet human expectations. In this process, the instruction-adjusted variant of RecurrentGemma-2B (RecurrentGemma-2B-IT) was compared with the Mistral 7B v0.2 Instruct model.
The human evaluation used a collection of approximately 1,000 instruction-following prompts for creative writing and coding tasks. RecurrentGemma-2B-IT performed impressively on this collection, achieving a win rate of 43.7%, only slightly lower than Gemma-1.1-2B-IT's 45.0%. This result suggests that RecurrentGemma-2B's ability to understand and execute complex instructions is comparable to existing state-of-the-art models.
RecurrentGemma-2B-IT was also evaluated on a collection of approximately 400 prompts testing basic safety protocols, achieving a win rate of 59.8%, demonstrating the model’s strength in following safety guidelines.
The performance of RecurrentGemma-2B has been thoroughly tested by combining automated benchmarks and human evaluation. Automated testing provides a quantitative assessment of the model's performance on various language tasks, while human evaluation provides a qualitative understanding of the quality of the model's output. This comprehensive evaluation approach ensures that RecurrentGemma-2B not only performs well in theory, but also provides high-quality language generation and comprehension capabilities in practical applications.
Inference speed is one of the key indicators to measure the practicality of language models, especially when processing long sequence data. The optimization of RecurrentGemma-2B in inference speed is a major highlight that distinguishes it from the traditional Transformer model. In the traditional Transformer model, in order to perform effective sequence processing, the model needs to retrieve and load the key-value (KV) cache into the device memory. As the sequence length increases, the size of the KV cache also grows linearly, which not only increases memory usage, but also limits the model's ability to process long sequences. Although the cache size can be reduced through a local attention mechanism, this usually comes at the expense of certain performance.
RecurrentGemma-2B solves the above problems through its innovative architecture design. It compresses the input sequence into a fixed-size state instead of relying on a KV cache that grows with the length of the sequence. This design significantly reduces memory usage and enables the model to maintain efficient inference speed when processing long sequences.
In the benchmark, RecurrentGemma-2B showed a significant throughput advantage. As shown in Figure 1a, on a single TPUv5e device, when sampling sequences of different lengths from a 2k token prompt, RecurrentGemma-2B is able to achieve a throughput of up to 6k tokens per second, while the Gemma model's throughput decreases as the cache grows.
The fixed state size of RecurrentGemma-2B is the key to its efficient inference. Compared with the Gemma model, the state of RecurrentGemma-2B does not grow as the sequence length increases, which means that it can generate sequences of arbitrary length without restriction, regardless of the host memory size. This is particularly important in long sequence processing, as it allows the model to process longer text data while maintaining high performance.
The improvement in inference speed is not only of great significance in theory, but also shows its value in practical applications. In resource-constrained environments, such as mobile devices or edge computing devices, the high throughput and low memory usage of RecurrentGemma-2B make it an ideal choice. In addition, the efficient inference speed also enables the model to respond to user requests faster and provide a smoother interactive experience.
In the field of artificial intelligence, model deployment is not only about implementing technology, but also about taking on safety and ethical responsibilities. The deployment strategy of RecurrentGemma-2B fully reflects the importance attached to these key factors.
Before the model was deployed, RecurrentGemma-2B was tested on a series of standard academic security benchmarks designed to assess possible misbehavior or bias in the model. Through these tests, the development team was able to identify and mitigate potential risks, ensuring the safety of the model when used publicly.
In addition to automated safety benchmarks, RecurrentGemma-2B also underwent an independent team’s ethical and safety assessment. This process involved a comprehensive review of the model, including but not limited to its fairness to specific groups, its ability to avoid harmful outputs, and its protection of user privacy.
Despite rigorous testing and evaluation, the development team emphasizes that it is impossible to cover all possible use cases, given that RecurrentGemma-2B may be used in a variety of different scenarios. Therefore, they recommend that all users perform additional security testing based on their specific use cases before deploying the model. This recommendation reflects the emphasis on user responsibility to ensure that each deployment is well thought out and customized.
Responsible deployment also includes transparency into the model's performance and limitations. The development team provides detailed model architecture and training details, allowing users and researchers to understand how the model works and its potential limitations. In addition, the team commits to continuous monitoring and improvement of the model to address emerging risks and challenges.
Responsible deployment also involves collaboration with the broader AI community and multiple stakeholders. By sharing research results, participating in public discussions, and accepting external feedback, the RecurrentGemma development team has demonstrated its commitment to open science and collaboration.
As the field of artificial intelligence continues to expand, RecurrentGemma serves as an example of combining innovative architectural design concepts with rigorous training and evaluation processes, demonstrating the potential to push the boundaries of what is possible in language understanding and generation.
Paper link: https://arxiv.org/abs/2404.07839
I have devoted myself to the research of technology for more than 30 years. I am proficient in various languages such as Java, Linux, JavaScript, PHP, CSS, etc. I have made many contributions in the field of open source. I have established a developer documentation site to share some problems in technology development for everyone to read.
