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Donate arxiv logo > cs > arXiv:2507.22448 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Computation and Language arXiv:2507.22448 (cs) [Submitted on 30 Jul 2025] Title:Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance Authors:Jingwei Zuo, Maksim Velikanov, Ilyas Chahed, Younes Belkada, Dhia Eddine Rhayem, Guillaume Kunsch, Hakim Hacid, Hamza Yous, Brahim Farhat, Ibrahim Khadraoui, Mugariya Farooq, Giulia Campesan, Ruxandra Cojocaru, Yasser Djilali, Shi Hu, Iheb Chaabane, Puneesh Khanna, Mohamed El Amine Seddik, Ngoc Dung Huynh, Phuc Le Khac, Leen AlQadi, Billel Mokeddem, Mohamed Chami, Abdalgader Abubaker, Mikhail Lubinets , Kacper Piskorski, Slim Frikha View a PDF of the paper titled Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance, by Jingwei Zuo and 26 other authors View PDF HTML (experimental) Abstract:In this report, we introduce Falcon-H1, a new series of large language models (LLMs) featuring hybrid architecture designs optimized for both high performance and efficiency across diverse use cases. Unlike earlier Falcon models built solely on Transformer or Mamba architectures, Falcon-H1 adopts a parallel hybrid approach that combines Transformer-based attention with State Space Models (SSMs), known for superior long-context memory and computational efficiency. We systematically revisited model design, data strategy, and training dynamics, challenging conventional practices in the field. Falcon-H1 is released in multiple configurations, including base and instruction-tuned variants at 0.5B, 1.5B, 1.5B-deep, 3B, 7B, and 34B parameters. Quantized instruction-tuned models are also available, totaling over 30 checkpoints on Hugging Face Hub. Falcon-H1 models demonstrate state-of-the-art performance and exceptional parameter and training efficiency. The flagship Falcon-H1-34B matches or outperforms models up to 70B scale, such as Qwen3-32B, Qwen2.5-72B, and Llama3.3-70B, while using fewer parameters and less data. Smaller models show similar trends: the Falcon-H1-1.5B-Deep rivals current leading 7B-10B models, and Falcon-H1-0.5B performs comparably to typical 7B models from 2024. These models excel across reasoning, mathematics, multilingual tasks, instruction following, and scientific knowledge. With support for up to 256K context tokens and 18 languages, Falcon-H1 is suitable for a wide range of applications. All models are released under a permissive open-source license, underscoring our commitment to accessible and impactful AI research. Comments: Technical report of Falcon-H1 model series Subjects: Computation and Language (cs.CL) Cite as: arXiv:2507.22448 [cs.CL] (or arXiv:2507.22448v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2507.22448 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jingwei Zuo [view email] [v1] Wed, 30 Jul 2025 07:55:33 UTC (9,843 KB) Full-text links: Access Paper: View a PDF of the paper titled Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance, by Jingwei Zuo and 26 other authors * View PDF * HTML (experimental) * TeX Source * Other Formats license icon view license Current browse context: cs.CL < prev | next > new | recent | 2025-07 Change to browse by: cs References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... 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