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## Feature Story
Liquid AI has unveiled two new bidirectional encoders, the LFM2.5-Encoder-230M and LFM2.5-Encoder-350M, designed to maintain speed even with an 8,192-token context on a CPU. These models are built on the LFM2 hybrid architecture and are intended for tasks such as classification, natural language understanding, and token-level operations. They promise to match or exceed the performance of larger encoders while scaling more efficiently with longer input lengths. Encoders like these are crucial for applications that require continuous operation without the aid of a GPU, such as classifiers, intent routers, safety filters, and personally identifiable information (PII) detectors. The LFM2.5 models are particularly noteworthy because they offer a significant improvement in speed and efficiency over previous models like ModernBERT, especially when handling long-context inputs. The development of these encoders involved converting existing decoder backbones into encoders through three key modifications. First, the causal attention mask was replaced with a bidirectional one, allowing each token to attend to both preceding and following tokens. Second, the short convolutions were made non-causal using symmetric center padding, enabling each token's convolution to incorporate neighboring tokens from both sides. Finally, the models were trained with a masked language modeling objective at a 30% mask rate, which is denser than the 15% used by BERT, based on evidence that a higher mask rate is beneficial at this scale. The training process for these encoders occurs in two stages. The first stage establishes the foundational capabilities of the model, while the second stage fine-tunes it for specific tasks. This approach allows the encoders to be highly adaptable and efficient, making them suitable for a wide range of applications. One of the standout features of the LFM2.5 encoders is their ability to handle document-scale workloads quickly, even on standard hardware. This is achieved by ensuring that latency grows slowly as input lengths increase, making them about 3.7 times faster than ModernBERT-base at processing long contexts. This efficiency is particularly beneficial for enterprises looking to deploy AI solutions that require minimal infrastructure investment while maintaining high performance. Liquid AI's release of these encoders is part of a broader trend in the AI industry to reduce the infrastructure demands of AI systems and increase throughput at a lower cost. By providing models that can operate efficiently on CPUs, Liquid AI is enabling more organizations to implement advanced AI capabilities without the need for expensive hardware upgrades. For developers and businesses, the implications are clear: these encoders offer a cost-effective solution for building and deploying AI applications that require fast, long-context processing. Whether it's for intent routing, policy linting, PII detection, or text classification, the LFM2.5 encoders provide a robust and scalable option that can be integrated into existing systems with ease. Looking ahead, the release of the LFM2.5 encoders sets a new benchmark for what can be achieved with compact, efficient AI models. As the demand for AI solutions continues to grow, innovations like these will play a crucial role in making advanced AI capabilities accessible to a wider range of users and applications. In summary, Liquid AI's LFM2.5-Encoder-230M and LFM2.5-Encoder-350M models represent a significant advancement in the field of AI encoders. By offering high performance with minimal infrastructure requirements, they provide a practical and scalable solution for a variety of AI tasks, paving the way for more widespread adoption of AI technologies.
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