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2026-11-09

Hugging Face 官方博客20:34

Your Experts Have Partners. There's HOPE for Keeping Them Together

HOPE, a second-order expert pruning method for Mixture-of-Experts (MoE) language models, accounts for pairwise expert interactions by optimizing a quadratic program based on an interaction matrix derived from calibration data. Unlike first-order methods that prune experts independently, HOPE preserves cooperative expert groups, leading to better performance—especially at high pruning rates like 50%. On benchmarks such as SWE-Bench Pro, HOPE outperforms state-of-the-art first-order methods like REAP by up to +6.1%, maintaining model quality while significantly reducing memory footprint. The method requires only a single calibration pass and efficient quadratic programming, with negligible overhead. It has been validated on Qwen3.5 and GLM-4.5 models, showing consistent improvements across diverse tasks and calibration sets.

2026-11-05

Hugging Face 官方博客08:00

A Closer Look at Phonon TTS. Launching High-Quality On-Device Speech That Tops the Benchmarks.

Phonon, a small text-to-speech model that runs entirely on the user's device without requiring internet access, has been released with support for English, French, German, Spanish, and Portuguese. Built on the Pocket TTS lineage using flow matching, Phonon achieves high-quality audio in compact sizes (65 MB and 75 MB), with the 31m version licensed under Apache 2.0 for commercial use. It excels in intelligibility and speed, ranking first in German and Spanish on Hugging Face's Open TTS Leaderboard and leading in CPU streaming benchmarks. The model supports voice cloning, custom voices via Gradium Studio, and integrates with Hugging Face Transformers. It is available in multiple formats and backends, including WebAssembly, GPU, and NPU, enabling deployment across phones, laptops, single-board computers, and browsers. Over 200,000 downloads were recorded in its first two weeks, with active developer adoption through tools like ExecuTorch, Micdrop, and Spaces. Future developments include support for additional languages, smaller models, and custom vocabulary fine-tuning.

Hugging Face Blog01:50

Dharma Field Extractor: Any Document In, Structured Fields in JSON Out

The Dharma Field Extractor, a 2-billion-parameter model, specializes in extracting specific fields from diverse document types—such as invoices, IDs, and medical forms—into structured JSON format. It outperforms general-purpose frontier LLMs in accuracy and reduces cost by up to 147 times when self-hosted, demonstrating that domain-specialized small models can surpass large general-purpose models on well-defined tasks. The model supports both open extraction (detecting fields without a schema) and schema-directed extraction (returning exact field names and types), with training data spanning English, Brazilian Portuguese, and multilingual documents. Benchmarks show superior F1 scores and valid JSON output across real-world datasets like SROIE and FUNSD, reinforcing the thesis that specialization beats scale for enterprise document processing.

2026-10-28

Hugging Face 官方博客08:00

NVIDIA Nemotron 3.5 Transcribe and Granary v2: Intelligent Multilingual Transcription You Can Prompt and Adapt

Timeline and related coverage

MarkTechPost05:08

Liquid AI Releases LFM3: 6 Open-Weight 2B-Class Multimodal Models

Liquid AI released LFM3, 6 open-weight 2B-class models for text, vision, audio, omni and decisions. LFM3-1.7B uses a tri-hybrid backbone, a 256K context window and fits in about 1 GB at 4-bit. The post Liquid AI Releases LFM3: 6 Open-Weight 2B-Class Multimodal Models appeared first on MarkTechPost.

Timeline and related coverage

2026-10-27

Hugging Face 官方博客08:00

Build specialized multimodal models with LFM3

Timeline and related coverage

2026-10-21

NVIDIA Blog18:00

NVIDIA releases a portfolio of open-source models across multiple domains, driving the development of sovereign AI in European industries.

NVIDIA announced the expansion of its open-source model family to encompass areas such as language, robotics, bioinformatics, and quantum computing, unveiling the Nemotron 3.5 series, the Kumo structured-data model, the Cosmos world model, the Alpamayo 2 Edge autonomous-driving model, the BioNeMo RFD4-Proteina protein-design model, and the Ising quantum-computing model. All these models are now available to developers and enterprises, with access to weights and tools, supporting localized customization and independent deployment.

Official

Timeline and related coverage

2024-09-19

Qwen00:00

Qwen2.5-Coder: Code More, Learn More!

GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction In early April, we introduced CodeQwen1.5, which garnered significant attention from the community. Since then, we have been working to enhance the coding model. Today, we are excited to announce the release of the next generation of open-source coding models, Qwen2.5-Coder, and officially rename CodeQwen to Qwen-Coder. We think “Coder” is more human-like and agile, reflecting our vision of it becoming a true coding partner in the future.