Falcon-Edge: A series of powerful, universal, fine-tunable 1.58bit language models.

Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance

Falcon 2: An 11B parameter pretrained language model and VLM, trained on over 5000B tokens and 11 languages

Large Language Models: A New Moore's Law?

Falcon-Arabic: A Breakthrough in Arabic Language Models

BuzzASR: A Swarm of 100+ Monolingual Speech Recognition Models

arXiv:2609.09554v1 Announce Type: new Abstract: We introduce BuzzASR, a collection of language-specialized fine-tuned Whisper models adapted for automatic speech recognition (ASR) in 102 languages. L...

AMD + 🤗: Large Language Models Out-of-the-Box Acceleration with AMD GPU

Accelerating Vision-Language Models: BridgeTower on Habana Gaudi2

BuzzASR: A Swarm of 100+ Monolingual Speech Recognition Models

We introduce BuzzASR, a collection of language-specialized fine-tuned Whisper models adapted for automatic speech recognition (ASR) in 102 languages. Large end-to-end Transformer-based ASR models such...

The State Of LLMs 2025: Progress, Problems, and Predictions

A 2025 review of large language models, from DeepSeek R1 and RLVR to inference-time scaling, benchmarks, architectures, and predictions for 2026.

Subtitle-Aligned Fine-Tuning of Whisper for Swiss German ASR: Benchmark Contamination, Convention Mismatch, and an Honest Baseline at 25.6% WER (13.8% cWER)

arXiv:2606. 07608v1 Announce Type: cross Abstract: We present a systematic study of fine-tuning OpenAI's Whisper large-v3 for Swiss German ASR, using 1,367 hours of broadcast speech paired with Standard German subtitles as weak supervision.

UltraVoice: Scaling Fine-Grained Style-Controlled Speech Conversations for Spoken Dialogue Models

arXiv:2510.22588v2 Announce Type: replace-cross Abstract: Spoken dialogue models currently lack the ability for fine-grained speech style control, a critical capability for human-like interaction tha...

Do Audio Language Models Hear and Read Distinctive Features Alike?

The paper investigates whether audio language models encode phonetic features similarly when processing spoken versus written input. By comparing mean representations of minimal phoneme pairs across six models, seven features, and 15 languages, the study finds that only voicing in two Qwen2.5-Omni models shows a significant shared direction, and that the model family—not size—determines feature representation. The analysis uses cosine similarity against a random-pair reference to assess alignment across modalities.