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#Open source

All collected and published items, ordered by time. Picks are a selected subset.
First-party

Direct announcements from model vendors, companies and research institutions on their official sites, blogs and accounts.

10/8Thu
  1. Hugging Face Blog40

    TII launches Falcon-ASR, a 1.6B-parameter Arabic speech recognition model focused on Emirati dialects

    Abu Dhabi's Technology Innovation Institute (TII) released Falcon-ASR, a 1.6B-parameter speech recognition model focused on Arabic and Emirati dialects, with support for English, French, Spanish, and Portuguese as well. Its average WER across six Arabic test sets is 20.92%, 2.25 percentage points below the best public result of 23.17% in the leaderboard snapshot used. In an internal Emirati dialect evaluation, it achieved a 22.73% WER and 10.19% CER, outperforming Qwen3-Omni.

10/6Tue
  1. Hugging Face Blog26

    Falcon-Emirati-7B: teaching LLMs the Emirati dialect, culture, and feel for language

    Falcon-Emirati-7B is a specialized model for Emirati dialects built on Falcon-H1-Arabic. It aims to understand and generate Emirati Arabic like a native speaker. It uses the Falcon-H1 hybrid architecture, running Mamba alongside Transformer attention. The base model family comes in 3B, 7B, and 34B sizes, with a context window of up to 256K tokens.

10/3Sat
  1. Hugging Face Blog62

    Ai2 open-sources AstaBrief 8B, a scientific report-generation model

    Ai2 open-sourced AstaBrief 8B, a model that turns research questions and retrieved literature excerpts into reports with citations. It was trained from Qwen3-8B using SFT and DPO and is now available as Asta's Fast mode.

    Why it matters: The source describes AstaBrief's training-data construction and citation-filtering details, which can be adapted to deploy other long-form generation models.

9/24Thu
9/21Mon
  1. Microsoft Research40

    Microsoft open-sources RetroChimera retrosynthesis model in a paper published in Nature

    Microsoft has published RetroChimera, a retrosynthesis-prediction framework, in Nature and open-sourced its implementation and weights. It combines the Transformer model R-SMILES 2 with the GNN model NeuralLoc, using a learned ensemble to rerank their predictions and performing strongly on both common and rare reaction types. In blind tests, chemists preferred its single-step reaction predictions. It found complete routes for nine of ten challenging targets, compared with five for a de novo model, four for an editing model, and two for NeuralSym.

  2. Hugging Face Blog48

    Hugging Face releases tokenizers v1: benchmarks show major encoding and decoding speedups

    Hugging Face has released tokenizers v1, producing exactly the same token IDs as v0.23 while running up to dozens of times faster. The project splits a single crate into a workspace and introduces bitcannon, which replaces regex tokenization with SIMD bit operations, along with an allocation-free merge loop, thread-local word cache, and native multithreaded parallelism.

9/19Sat
9/3Thu
9/1Tue
8/26Wed
8/21Fri
  1. Microsoft Research38

    Microsoft releases Skala 1.1 with 2.5× more training data; now integrated into CP2K

    Microsoft Research has released Skala 1.1, a deep-learning DFT functional trained on 2.5 times as much data as the previous public version. It ranks first in 32 of the 55 GMTKN55 benchmark categories, with a weighted mean error of 2.8 kcal/mol. The release is available in CP2K and is being integrated into Psi4, FHI-aims, ORCA, and VASP. Microsoft has also introduced a living benchmark that tracks computational performance across versions.

8/13Thu
  1. Hugging Face Blog34

    Hugging Face OlmoEarth Studio adds custom embedding export

    Hugging Face's OlmoEarth Studio can now compute and export embeddings; its code and model weights are public. Through the UI or API, users can select a region, time range, encoder variant (Nano: 128 dimensions and 1.4M parameters; Tiny: 192 dimensions and 6.2M parameters; Base: 768 dimensions and 89M parameters), resolution, and imagery source, then export an int8 Cloud-Optimized GeoTIFF.

8/10Mon
  1. Hugging Face Blog48

    Hugging Face proposes efficient knowledge distillation with offline top-K logits and fused, chunked KL loss

    A new Hugging Face paper proposes two systems improvements that sharply reduce the training cost of distilling LLMs: cache the teacher model's top-100 logits offline so teacher and student do not need to reside in GPU memory simultaneously, and use a fused, chunked KL loss to avoid generating a full vocabulary-by-sequence-length matrix.

7/16Thu
7/6Mon
7/1Wed
6/30Tue
5/27Wed
  1. Mistral AI38

    After acquiring Emmi AI, Mistral advances physical-AI research with industrial-simulation results

    After acquiring Emmi AI, Mistral is building foundational physical AI for aerospace, automotive, semiconductors, and energy. Its announced results include AB-UPT, which processes raw geometry with 9M surfaces and 140M volume cells on a single GPU without remeshing, and NeuralDEM, an end-to-end deep-learning surrogate for large multiphysics processes.

4/16Thu
  1. Google Research39

    Google Research uses AI-generated neurons to speed brain mapping, reducing reconstruction errors by 4.4%

    Google Research has proposed MoGen (Neuronal Morphology Generation), a model that generates synthetic neuron shapes with point-cloud flow matching. The data supplements training for the PATHFINDER reconstruction model, reducing mouse-axon reconstruction error by 4.4%, mainly by decreasing merge errors. At whole-mouse-brain scale, the work is equivalent to saving 157 person-years of manual proofreading. MoGen is available as an open-source model.