Introduction
The Falcon-H1 series developed by the Institute of Technology Innovation (TII) marks a significant progress in the development of large language models (LLM). In a hybrid parallel configuration, by integrating transformer-based attention with Mamba-based state space model (SSMS), Falcon-H 1 receives extraordinary performance, memory efficiency and scalability. Refill the trade-closure between many sizes (0.5B to 34B parameters) and released in versions (base, instruction-tune, and volume), Falcon-H1 models re-define the trading between compute budget and output quality, many contemporary models such as Qwen2.5-72B and llama3.3-70B offer parameter efficiency.
Major architectural innovation
technical Report It is said how Falcon-H 1 adopts a novel Parallel hybrid architecture Where both meditation and SSM modules function concurrently, and their output is covered before projection. This design is distracted by traditional sequential integration and provides flexibility to tune freely and tune the number of SSM channels. The default configuration uses 2: 1: 5 ratio for SSM, attention and MLP channels, adapting both efficiency and learning dynamics.
To further refine the model, Falcon-H 1 explore:
- Channel allocation: Ablements show that meditation channel deteriorates by increasing performance, while balanced by SSM and MLP has strong benefits.
- Block configuration: SA_M configuration (semi-secular and SSM moves together with attention, followed by MLP) Best performs in training loss and computational efficiency.
- Rope base frequency: Unusually high base frequency of 10^11 in rotary positional embeding (rope) proved to be optimal by improving generalization during long -term reference training.
- Width and deepening trade: Experiments show that deep models perform widely better under fixed parameters budget. Falcon-H 1–1.5 B-Dip (66 layers) improve several 3B and 7B models.
Toknerizer strategy
Falcon-e 1 uses a customized byte pair encoding (BPE) tookar suit with a terminology size ranging from 32K to 261k. The main design options include:
- Numerals and punctuation division: Code and multilingual settings improve display empirically.
- Latex token injection: Mathematics on the benchmark increases accuracy.
- Multilingual support: Using customized fertility and bytes/token matrix, covers 18 languages and scales up to 100+.
Pretraning Corpus and Data Strategy
The Falcon-H1 model is trained from a carefully curate 20 te token corpus to 18t token, including:
- High quality web data (Filted FineWeb)
- Multilingual dataset: Common Call, Wikipedia, Arxiv, OpenSubtitles, and Curated Resources for 17 languages
- Code core: 67 languages, Minhash Diduplication, Codbert Quality Filters, and PII scrubbing were processed through scrubbing
- Math dataset: Mathematics, GSM8K, and In-House Latex-Wide Crroll
- Synthetic data: 30k Wikipedia-based subjects diverse LLMS, Plus textbook-style QA was re-written from Raw Corpora
- Long reference sequence: Fil-in-a-media, extended up to 256K tokens through re-ordering and synthetic regional tokens
Training infrastructure and functioning
Use of training optimized maximal updated paramirization () p), supporting smooth scaling in model sizes. Models employ advanced equalism strategies:
- Mixer Equality (MP) And Reference parallism: Increase the throwput for long -term reference processing
- Permanentation: To facilitate the age deployment, released in BFLOT16 and 4-bit variants
Evaluation and performance
Falcon-H 1 per parameter receives unprecedented performance:
- Falcon-H 1-34 B-Intection Qwen2.5-72B and llama3.3-70B like arguments, mathematics, instructions, and multilingual functions such as 70B-Scale models or matches or matches.
- Falcon-H 1-1.5 B-Dip Rival 7 B -10B model
- Falcon-H 1-0.5 B 2024-Eugory 7B protects performance
Benchmarks expand the mmlu, gsm8k, humaneval, and long-contemporary functions. Models display strong alignment through SFT and direct preference adaptation (DPO).
conclusion
Falcon-H1 sets a new standard for open-weet LLM by integrating parallel hybrid architecture, flexible tokens, efficient training dynamics and strong multilingual abilities. Its strategic combination of SSM and meditation allows for practical calculations and unmatched performance within the memory budget, making it ideal for both research and deployment in diverse environments.
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Michal Sutter is a data science professional, with Master of Science in Data Science from the University of Padova. With a concrete foundation in statistical analysis, machine learning, and data engineering, Mishhala excelled when converting the complex dataset into actionable insights.
