#Hugging Face

AIThree OpenAI safety researchers, Tomek Korbak, Jasmine Wang, and Mikita Balesni, say they were fired and that their terminations are scaring remaining employees. OpenAI says an investigation found they violated policies on handling sensitive information and denies firing anyone for raising safety concerns, without specifying the breach.

AIHugging Face's Merve Noyan gave an interview to Argentina's La Nacion newspaper about the OpenAI hack, Hugging Face's acquisition, and open-source AI. The post links to the interview and provides no further details on its content.

AINVIDIA has released a deployment model for Agile One S SSD pickup, based on GR00T N1.7 checkpoint 58000 and using three cameras: ego, left wrist, and right wrist. The repository republishes ONNX graphs, external tensor files, and two existing TensorRT BF16 engines without retraining or re-export, and the original export reported a numerical warning that full FP32, node, and BF16 parity did not pass all tolerances. The files are not a certified robot deployment or safety qualification.

AIJetBrains released Mellum2.1, a 12B mixture-of-experts coding model with 2.5B active parameters under Apache 2.0, emphasizing agentic programming. Under high load, its inference throughput in tokens is nearly twice that of Qwen3.5-9B in JetBrains' comparison, and multi-token prediction (MTP) speeds single-request responses by about 1.6x. The model is available on Hugging Face for local or private-infrastructure deployment, with GGUF and vLLM MTP support announced for later.

AIGoodfire has launched monitors that read a model's internal signals during agent work instead of reviewing its written output. The monitors are available to Baseten customers, who can choose risks to watch and set automated responses. In Goodfire's tests on Kimi K3, monitoring about 1,500 sessions cost roughly $51 versus about $10,000 for a top-tier AI judge, while catching 94% of malicious hacking sessions.

AIPrima Mente and AlzData are launching the Alzheimer's Translation Challenge, a global AI competition to discover new therapeutic hypotheses for Alzheimer's disease. The challenge centers on a 150M cell atlas of neurons, astrocytes, and microglia across different genetic backgrounds under combinatorial perturbations, with multi-modal readouts. Top teams will have their hypotheses tested in Prima Mente's wet lab, and the data will be available through the AD workbench, Hugging Face, and Prima Mente's modeling platform.

AIThe Alzheimer's Translation Challenge, led by Primamente and AlzData with NVIDIA, Hugging Face, Nebius, Talisman Therapeutics, Ultima Genomics, Cellanome, Prime Intellect, and Boltz, is now open for registration. The competition starts in spring 2027, according to the post.

AIHugging Face is asking users to try Paper Pages at to help keep scientific research open. The post provides no further technical details, figures, or feature specifics about the platform.

AICarbon-A is an open model that predicts gene locations directly from DNA, and it has been used to annotate genomes from over 22,000 species. The release includes a database of 566 million gene candidates, about 16 times the gene annotations in the RefSeq dataset. Wet-lab RNA experiments supported 239 candidates missing from RefSeq across cats, Syrian hamsters, chickens, and Arabidopsis.

Why it matters: The source ties an open gene-annotation model to specific wet-lab checks and gene counts, helping readers judge how far its predictions extend beyond well-studied genomes.

AIClément Delangue of Hugging Face points users to a Hugging Face Space from Pollen Robotics where they can create a dance for Reachy Mini. The post gives no further details about how the dance tool works.

AIHugging Face CEO Clément Delangue says the field urgently needs more public traces of AI agents attacking and defending systems, since defenders cannot learn from activity they cannot see. He invites anyone holding such traces who faces pressure to keep them private to contact him directly.

AIllama.cpp v0.6.0 adds support for multimodal Jev-like models and a performance upgrade for Metal, according to Merve Noyan. The post says models can be served with a single command, `llama serve -hf ggml-org/Clef-Flash-GGUF`, and points to a trending list of decision models on Hugging Face.

AINathan Benaich's 9th annual State of AI Report covers agents, robotics, AI for science, inference economics, and government control over frontier AI access. The report also records a 2025 prediction scorecard and lists nine predictions for the next 12 months. It cites an OpenAI cyber evaluation in which agents compromised Hugging Face's production infrastructure, and it says Anthropic and OpenAI's combined annualized revenue run rate reached $105B by late summer.

AIUnsloth Studio binds remote-code approval to a fingerprint of the scanned code, so changed code requires fresh consent before it runs. It also blocks weight files that Hugging Face has flagged for malware in the path the selected loader would deserialize. The article describes these checks as one layer among several, alongside package-content scans and OS sandboxes, and notes that the scanner is not a sandbox and cannot catch every evasion.

AIA Hugging Face blog author used the ML-Intern agent in HuggingChat to build six small models by writing detailed prompts that specify datasets, base models, baselines, smoke tests, and spending limits. The projects include a citrus disease vision-language model, a Huggy character LoRA, a camera-angle LoRA, a doodle-to-object LoRA, a 0.8B prompt rewriter, and a 4-step distilled Agate model, with total compute cost of about USD 103. Each project's prompts and public models are linked from the post.

Why it matters: The author shows how prompt structure, baselines, smoke tests, and budget caps shape an agent-driven training workflow, with per-project costs given.

AIGoogle released EmbeddingGemma 2 on Hugging Face, and Paige Bailey called it a step toward open models for open science. The post links the model and cites earlier EmbeddingGemma-based projects, including medical, geographic, oncology, and PubMed embedding models.

AILiquid AI released two open-weight decision models, d1-3B and d1-omni-600M (experimental), built on its Liquid Foundation Models and available on Hugging Face. d1-3B scores 48.57 on the Decision Index 0.2.1, the highest among decision models under 10B parameters, and answers a question in 16 ms on an NVIDIA Jetson AGX Thor and under 50 ms on a Jetson Orin Nano. The models support text and images (d1-3B) or text with image or audio (d1-omni-600M).

AIPerplexity has published both pplx-embed-v2 models on Hugging Face, with the collection available at the linked URL. The post provides no further details on model sizes, benchmarks, or capabilities.

AIPerplexity has released pplx-embed-v2-late, two late-interaction embedding models that retrieve text, images, and pages within a shared embedding space for cross-model querying. The company says both models achieve frontier performance and are publicly available on Hugging Face.

AILlamaIndex announced OpenDocRouter, a single API that routes document parsing requests to any of 10 frontier and open-source models at launch, including Claude Opus 5.5, Gemini 3.8 Flash, GPT-6 Luna, MinerU2.5-Pro, and PaddleOCR-VL-1.6. Users can switch models in one line with the same request and markdown output, and each model is scored on ParseBench for quality and cost. Pricing is per-token, failed pages are not charged, and the service costs $0.86 to $48.82 per 1,000 pages depending on the model.

AIAi2 converted Qwen 3 8B and Llama 3 8B into byte-level models named Bwen 8B and Blama 8B. Both come close to matching their source models' performance in Ai2's evaluations, and Bwen 8B outperforms Bolmo 7B across its aggregate evaluation suite.

AIThe Technology Innovation Institute introduces Falcon-ASR, a 1.6 billion parameter speech recognition model for Arabic with a focus on the Emirati dialect. On six Arabic test sets it reports an average word error rate of 20.92%, versus 23.17% for the best published leaderboard result it compared against. The model also transcribes English, French, Spanish and Portuguese with the same weights, and a demo Space is available while API access and native apps are planned.

AINVIDIA reports that fine-tuned Nemotron models reached gold-medal level at both IOI 2026, scoring 535.4 out of 600, and IMO 2026, scoring 30 out of 42. The IOI run was a live, unofficial, unsupervised benchmark, while IMO proofs were graded by official IMO graders. The post also releases checkpoints, datasets, a new 200-problem benchmark, and inference pipelines on Hugging Face and NeMo-Skills.

Why it matters: The post traces how SFT, RL, and a generate-verify-refine loop turned Nemotron into gold-level specialists for IOI and IMO, with the training and inference details shared.

AIRep. Lori Trahan is rolling out a proposal to clarify liability for AI developers when their agents cause harm. The plan responds directly to a rogue AI hack of Hugging Face earlier this year, according to the post.

AIPollen Robotics' Microduck now runs on its first fully custom single-board computer, built by Seeed around the same Rockchip CPU. The new board adds better memory, WiFi/BT, dual NFC antennas, status LEDs, an RGB flashlight, and improved cooling and boot speed, replacing the earlier Radxa-based prototype stack.

AIPhilipp Schmid shares a Hugging Face Space that runs Gemma embedding models in the browser using WebGPU. The demo, a webml-community project, lets users generate embeddings locally without server-side inference.

AIA Hugging Face Space lets users try EmbeddingGemma 2 directly in their browser using WebGPU. The post shares only a demo link, with no further details on model size, benchmarks, or pricing.

AIGoogle releases EmbeddingGemma 2, its first native multimodal embedding model, built on Gemma 4 under Apache 2.0. It embeds over 100 languages, code, images, audio, and video into one vector, with an 8,192-token context and four sizes from 270M to 740M parameters. Matryoshka output dimensions of 768, 512, 256, or 128 are supported, and the model is available in Sentence Transformers and LiteRT-LM, with a reported 14% gain on MTEB Code.

Why it matters: The release extends an embedding model to text, code, images, audio, and video in one vector, a useful option for retrieval systems that mix media types.

AIPerplexity says its pplx-decider-v1.1-27b model supports a 250k context window and accepts text and image input. The weights are available on Hugging Face, and the model can be accessed through the Perplexity Decisions API.

AIPerplexity says its open-weights multimodal decision model pplx-decider-v1.1-27b is now available and scores highest on Hugging Face's new Decision Index 0.3 benchmark. The model costs $0.02 per million input tokens, half the price of v1.

AIllama.cpp added day-0 support for EmbeddingGemma 2, with GGUF builds available on Hugging Face under ggml-org. The post links to the embeddinggemma-2-GGUF model page as the way to run it.

AIGoogle's EmbeddingGemma 2 is an open embeddings model for on-device use that covers code, image, video, audio, and text. It comes in modular sizes from 270M text/code to 740M full multimodal, supports Matryoshka truncation down to 128 dimensions, and reports a 14% gain on MTEB Code over v1 under an Apache 2.0 license. The author's post highlights the release and a Hugging Face demo, while the benchmark table compares it with several models.

AIGoogle has made EmbeddingGemma 2 available to download on Hugging Face and Kaggle. Availability in the Gemini Enterprise Agent Platform Model Garden is coming soon.

AIGoogle has released the weights for EmbeddingGemma 2 on Hugging Face and Kaggle. The release includes out-of-the-box support for LiteRT, MediaPipe, LangChain, and LlamaIndex.

AIMistral announced Mistral Large 4, a natively multimodal model with 1T parameters and 49B active, now available via API. Open weights are scheduled for release at the end of October, with a countdown page on Hugging Face showing October 31, 2026.

AIClément Delangue of Hugging Face shared a link to a Hugging Face model page for mistralai/Mistral-Large-4.0-1T05-A52B. The post itself gives no further details about the model's capabilities, release terms, or benchmarks.

AIJulien Chaumond reposted Mistral's announcement of Mistral Large 4, a 1T-parameter natively multimodal model with 49B active parameters. Mistral says it is available via API today, with open weights scheduled for release at the end of October, and is working privately with cybersecurity partners.

Why it matters: The post lays out Mistral Large 4's scale, multimodal design, and availability timeline, which helps readers gauge the open-weights landscape outside China.

AITencent has open-sourced a new video model called Prism, according to the post. The poster says the previews look solid, with a Hugging Face link provided for access.

AINVIDIA has published Nemotron-Labs-3-Competitive-Coding on Hugging Face, a competitive-programming specialist model built on Nemotron-3-Ultra. The model is available in the NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4 repository, indicating a 550B-parameter total size with 55B active parameters in NVFP4 format.