Innovation Technology Institute TII Sortes Falcon-H1: models of hybrid transformer-SSM for an evolutionary, multilingual and long context understanding

Approach architectural compromises in language models

As language models are evolving, the balance between expressiveness, efficiency and adaptability becomes more and more difficult. Transformers' architectures dominate due to their strong performance through a wide range of tasks, but they are costly in calculation, in particular for long -context scenarios – to the quadratic complexity of self -attenuated. On the other hand, the structured state space models (SSM) offer improved efficiency and linear scaling, but often do not have the modeling of nuanced sequence required for a complex understanding of language. A combined architecture that exploits the forces of the two approaches is necessary to support various applications in the environments.

Presentation of Falcon-H1: a hybrid architecture

The Falcon-H1 series, published by the Technology Innovation Institute (TII), presents a hybrid family of language models that combine the attention mechanisms of transformers with SSM components based in MAMBA2. This architecture is designed to improve computer efficiency while maintaining competitive performance between tasks requiring deep contextual understanding.

Falcon -H1 covers a wide range of parameters – from 0.5b to 34b – occurring in use cases, deployments linked to resources with large -scale inference. The design aims to approach current bottlenecks in LLM deployment: memory efficiency, scalability, multilingual support and possibility of managing extensive entry sequences.

Architectural details and design objectives

Falcon-H1 adopts a parallel structure where attention heads and SSM MAMBA2 work side by side. This design allows each mechanism to contribute independently to the modeling of sequences: attention heads specialize in the capture of dependencies at the level of tokens, while SSM components support effective retention of long -term information.

The series supports a context duration of up to 256k tokens, which is particularly useful for applications in the summary of documents, generation of recovery and multi-tours dialogue systems. Model training incorporates a personalized microparameterization recipe (μp) and optimized data pipelines, allowing stable and effective formation between model sizes.

The models are formed by emphasizing multilingual capabilities. The architecture is natively equipped to manage 18 languages, with a cover including English, Chinese, Arabic, Hindi, French and others. The framework is expandable to more than 100 languages, supporting the location and adaptation of the model specific to the region.

Empirical results and comparative evaluation

Despite the number of relatively modest parameters, the Falcon-H1 models demonstrate strong empirical performance:

• Falcon-H1-0.5B obtains results comparable to parameter 7B models published in 2025.
• Falcon-H1-1.5B-DEEP behaves equally with the main models of 7B transformers to 10b.
• Falcon-H1-34B corresponds or exceeds the performance of models such as QWEN3-32B, LLAMA4-SCOUT-17B / 109B and GEMMA3-27B through several landmarks.

The evaluations emphasize the understanding of language for general use and multilingual benchmarks. In particular, the models obtain solid performance in high resources and low -resources languages ​​without requiring layers of adaptation to fine or excessive additional adaptation.

Deployment and inference are supported by integration with open-source tools such as embraced facial transformers. Flashattement-2 compatibility further reduces the use of memory during inference, offering an attractive performance-performer balance for the use of the company.

Conclusion

Falcon -H1 represents a methodical effort to refine the architecture of the language model by integrating complementary mechanisms – attention and SSM – in a unified setting. In doing so, it addresses the key limitations of long -context treatment and scale efficiency. The family of models offers a range of options for practitioners, from light variants adapted to the deployment of edges to high -capacity configurations for server on the server applications.

Thanks to its multilingual coverage, its long-term context capacities and its architectural flexibility, Falcon-H1 offers a technically solid basis for cases of research and production that require performance without compromising efficiency or accessibility.

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