Transformers

2026-10-21

MODELS · 1 SOURCE · Hugging Face

IBM updates Granite Speech suite with granite-speech-5.0-2b and two revised models

IBM released three updates to its Granite Speech collection: a new speech-language model, granite-speech-5.0-2b, and revised granite-speech-5.0-470m-turboctc and granite-speech-4.1-2b-plus. The 5.0-2b model (trained on ~390,000 hours of public audio) expands multilingual transcription and translation and improves English ASR robustness; the Turbo CTC 470M revision adds multilingual support, native punctuation, and faster runtime (~19,870 RTFx); the 4.1-2b-plus revision improves speaker attribution, timestamps, long-form WER, and keyword biasing. All models ship under Apache 2.0 with integrations for Transformers, vLLM, llama.cpp and mlx-audio.

7.0

2026-10-20

CODING · 1 SOURCE · Hugging Face

Hugging Face launches TorchNeuron — native PyTorch backend for AWS Trainium (public beta Oct 20, 2026)

TorchNeuron is a new PyTorch plugin from Hugging Face that exposes Neuron as a native PyTorch device for AWS Trainium, enabling eager execution, torch.compile and native distributed APIs (DTensor, FSDP, TP). It brings Transformers, Diffusers and other Hugging Face libraries to Trainium without Optimum Neuron-specific forks, increases out-of-the-box text-generation model compatibility (89% of top 100 vs 46% with Optimum Neuron v0.4.5), and will be available as a public beta starting Oct 20, 2026 (pip install torch-neuronx).

8.0

2026-09-25

MODELS · 1 SOURCE · InfoQ AI, ML & Data Engineering

InfoQ podcast panel on enterprise AI adoption and open-source sovereignty

InfoQ hosted a podcast panel with Meryem Arik (DoubleWord), Clara Higuera Cabañes (BBVA) and Jeff Smith (C Proof) that surveyed enterprise AI adoption, covering inference/token cost reduction, tensions between proprietary models (e.g., Claude) and high-quality open-source alternatives, governance and ethics, the shift to non-deterministic "generative code," and speculative future architectures such as Kolmogorov-Arnold Networks.

3.0

2026-09-22

COMPANIES · 1 SOURCE · Hugging Face

Hugging Face hires oMLX creator Jun Kim to support MLX local-AI ecosystem

Hugging Face has added Jun Kim, the creator and maintainer of oMLX, to its team to support and further develop the MLX local-AI ecosystem. oMLX will remain Apache 2.0-licensed, Jun will continue to lead the project, and Hugging Face says it will focus on improving stability and streamlining conversion from Transformers model definitions to MLX implementations.

6.0

2026-09-18

RESEARCH · 1 SOURCE · arXiv cs.AI

TranSGrid: reasoning-centered benchmark shows Transformers struggle with inductive and abductive aspects of systematic generalization

The paper (arXiv:2609.19212v1) introduces TranSGrid, a reasoning-centered testbed that unifies deductive, inductive, and abductive reasoning to more rigorously evaluate systematic generalization. Experiments on 4,800 instances with seven Transformer models show a substantial performance gap versus a held-out test set (largest model: 79.6% on the test set, 55.3% on TranSGrid, 15.8% on the hardest subset), and variants that reintroduce linear action composition or action-explicit goals recover test-set-level performance.

6.0

2026-03-16

MODELS · 1 SOURCE · Mistral AI

Mistral releases Small 4: 119B MoE multimodal model with 256k context (Apache 2.0)

Mistral announced Mistral Small 4, a 119B-parameter hybrid Mixture-of-Experts model (128 experts, 4 active) that accepts text and image inputs, offers a 256k context window, and includes a configurable reasoning_effort parameter; it is released under the Apache 2.0 license. The company says Small 4 unifies capabilities from its Magistral, Pixtral, and Devstral lines, targets chat, coding/agentic, and complex-reasoning use cases, claims substantial latency and throughput gains versus Mistral Small 3, and is available across vLLM, llama.cpp, SGLang, Transformers and other runtimes.

9.0