Falcon-H1-34B-Instruct-GGUF

Falcon-H1 is a new series of large language models (LLMs) featuring a novel hybrid architecture that combines Transformer-based attention with State Space Models (SSMs) for superior long-context memory and computational efficiency. Released in multiple configurations (0.5B to 34B parameters), 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, while smaller models also show strong performance relative to their size. These models excel across reasoning, mathematics, multilingual tasks, instruction following, and scientific knowledge, supporting up to 256K context tokens and 18 languages. All models are released under a permissive open-source license. For more details about the training protocol of this model, please refer to the Falcon-H1 technical blogpost and Technical Report. Currently to use this model you can either rely on Hugging Face `transformers`, `vLLM` or our custom fork of `llama.cpp` library.

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