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8Hugging Face Blog·1mo ago

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.

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Related events (8)

6Hugging Face Blog·1mo ago·source ↗

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.

6Hugging Face Blog·1mo ago·source ↗

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.

6Hugging Face Blog·1mo ago·source ↗

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.

6Hugging Face Blog·1mo ago·source ↗

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.

6Hugging Face Blog·1mo ago·source ↗

Falcon-Edge: 1.58-bit Quantized Language Model Series from TII

Technology Innovation Institute (TII) has released Falcon-Edge, a series of language models operating at 1.58-bit precision, targeting edge deployment scenarios. The models are designed to be fine-tunable despite extreme quantization, positioning them as practical options for resource-constrained environments. This release extends the Falcon model family into the ultra-low-bit regime, following broader industry interest in BitNet-style ternary weight models.

5Hugging Face Blog·1mo ago·source ↗

Falcon-Arabic: A Breakthrough in Arabic Language Models

TII UAE has released Falcon-Arabic, a language model specifically designed for Arabic. The announcement highlights it as a significant advancement in Arabic NLP capabilities. As a tier-2 source with minimal body content, specific technical details about model size, training data, or benchmark performance are not available from this item.

8Qwen Research·1mo ago·source ↗

Qwen2.5-LLM: Alibaba releases open-weight language models from 0.5B to 72B

Alibaba's Qwen team releases the Qwen2.5 series of decoder-only dense language models, open-sourcing seven variants spanning 0.5B to 72B parameters. The release targets production use cases in the 10-30B range and mobile deployments at 3B scale. This represents a significant expansion of the open-weights frontier from a Tier 1 Chinese AI lab.

9Openai Blog·1mo ago·source ↗

OpenAI Releases gpt-oss-120b and gpt-oss-20b Open-Weight Models Under Apache 2.0

OpenAI is releasing two open-weight language models, gpt-oss-120b and gpt-oss-20b, under the Apache 2.0 license. The models are claimed to outperform similarly sized open models on reasoning tasks and feature strong tool use capabilities. They are optimized for efficient deployment on consumer hardware, positioning them as cost-effective alternatives in the open-weights ecosystem.