Falcon Perception: TII Announces Multimodal Perception Capabilities for Falcon
TII (Technology Innovation Institute) has published a blog post on Hugging Face introducing Falcon Perception, a multimodal extension of the Falcon model family. The post appears to detail perception capabilities added to the Falcon series, likely covering vision-language or other sensory modalities. As the body content is empty, specific technical details about architecture, benchmarks, or release scope are unavailable from this source.
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Falcon LLM Integrated into Hugging Face Ecosystem
Hugging Face announced the integration of the Falcon language models (Falcon-7B and Falcon-40B) into its ecosystem, including model hosting, inference APIs, and tooling support. Falcon, developed by the Technology Innovation Institute (TII), had recently topped the Open LLM Leaderboard at the time of release. The post covers usage patterns, fine-tuning guidance, and deployment options within the Hugging Face stack.
Welcome to the Falcon 3 Family of Open Models
The Technology Innovation Institute (TII) has released the Falcon 3 family of open-weights models, announced via the Hugging Face blog. The release continues TII's Falcon lineage of open models and is positioned as a significant update to the series. Details on model sizes, capabilities, and training methodology are covered in the announcement.
Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
TII UAE has released Falcon-H1, a new family of hybrid-head language models combining attention and state-space mechanisms to improve efficiency and performance. The models are published on Hugging Face and represent TII's latest iteration in the Falcon series. The hybrid architecture targets better inference economics and competitive benchmark results relative to model size.
Falcon 2: 11B Parameter Pretrained LLM and VLM Trained on 5T+ Tokens Across 11 Languages
Technology Innovation Institute (TII) has released Falcon 2, an 11B parameter language model pretrained on over 5 trillion tokens spanning 11 languages. The release includes both a base language model and a vision-language model (VLM) variant. This represents a significant update to the Falcon model family, expanding multilingual and multimodal capabilities.
Perceiver IO: a scalable, fully-attentional model that works on any modality
Hugging Face published a blog post introducing Perceiver IO, a general-purpose transformer-based architecture designed to handle arbitrary input and output modalities by using a small latent array to avoid quadratic attention scaling. The model decouples input size from the attention bottleneck, enabling it to process images, audio, video, text, and multimodal data within a single unified framework. The post covers the architecture's design principles and its integration into the Hugging Face ecosystem.
Falcon 180B Released: New Open-Weights Frontier Model
Technology Innovation Institute (TII) has released Falcon 180B, a 180-billion parameter open-weights language model announced via Hugging Face. At the time of release, it was positioned as the largest publicly available open-weights model, trained on 3.5 trillion tokens. The model is available on Hugging Face Hub for research and commercial use under a custom license.
Fine-tuning Florence-2 - Microsoft's Cutting-edge Vision Language Models
This Hugging Face blog post provides a technical guide for fine-tuning Microsoft's Florence-2 vision-language models. Florence-2 is a compact yet capable multimodal model supporting tasks like captioning, object detection, and OCR. The post covers practical implementation details for adapting the model to custom datasets using the Hugging Face ecosystem.
Llama 3.2 Multimodal and Edge Models Launch on Hugging Face
Meta released Llama 3.2, introducing vision-capable multimodal models alongside lightweight models optimized for on-device inference. Hugging Face published a blog post covering integration support, model availability, and deployment options across the ecosystem. The release marks Meta's first open-weights multimodal Llama models, adding image understanding to the Llama family. Smaller 1B and 3B parameter variants target edge and mobile deployment scenarios.