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bs and revenue,” Lescure said. “For this we need pioneers.” As attendees mingled at the reception, crowds gathered around a couple tiny guests: Microducks, 10-inch biped robots by Hugging Face that run on open source software. Huang also stepped outside to address the press gathered at the venue about secure technology deployment. “Containment and monitoring are the fundamental pillars of AI,” he said. Later that evening, Huang met with French President Emmanuel Macron at the Élysée Palace to di

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ree output (summary); Liquid d1-3B: 35 ms per image on Jetson Thor (@TheTuringPost); Open releases:EmbeddingGemma 2: 740M parameters and 78.68 on MTEB code (@dl weekly); Carbon-A (Hugging Face): Proposed 566M gene candidates across 22,617 species (@lvwerra); ColGrep-Agent-2B: A local code-search agent that cuts API tokens by 34% (@AmelieTabatta); Product updates:Codex: GPT-6.1 Sol’s default context in Codex and ChatGPT rose from 272K to 500K (@reach vb); OpenCode 2.0: Released (@opencode) Indust

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s led to us finding the second sample which we believe to be the first time our models used artifactory as a message board. Overall the behavior was very uncommon in this run. The Hugging Face incident involved a similar mechanism for using Artifactory as a shared message board. How we are addressing it We have made changes to fix this and other cross-sample communication routes which were found by our red teaming efforts, which we now do regularly. We have also generally improved our alignment

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cord) [Selected fields from the original training-selection record] This incident was identified through an ad hoc misalignment monitoring investigation by Jun 25, 2026. After the Hugging Face incident, retrospective misalignment monitoring of earlier reinforcement-learning and evaluation traffic flagged this incident as critical. In response to these and other incidents, we strengthened security controls, took further steps to prevent models from accessing the live internet during training, imp

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tes after that. The run was killed 2.5 hours later. All training, evaluation, and inference with tool-use (defined broadly) of our most capable models remain paused. Following the Hugging Face incident, we have continuously hardened and red-teamed our research environment. Our focus has been broad direct and transitive paths. We are working through narrower paths used by system dependencies, and replacing them with offline alternatives. This incident is a lot less severe than some of our previou

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xtractive approach.” The past few months have made it clear that safety remains a gnarly problem for closed models. There’s the autonomous hack that OpenAI agents executed against Hugging Face in July, but human misuse is a real threat too: According to a recent risk report from Anthropic , several extensive hacking operations have been undertaken with the assistance of Claude. So closed models are risky enough—and open models would likely be much easier to exploit. Before Anthropic passes a use

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4 [6]) is a dataset of 1 million conversations taken from between April 2023 and May 2024. It was collected by offering users free access to ChatGPT-3.5 & GPT-4 services hosted on Hugging Face Spaces, with an explicit opt-in consent to collect and release their de-identified chat transcripts. WildChat has since been used to construct several benchmarks [7,8,9] that attempt to rank AI models in terms of their capabilities or safety, by curating small sets of difficult prompts targeted at elicitin

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avigation NotificationsYou must be signed in to change notification settings; Fork6 (6); Star96 (96)You must be signed in to star a repository Repository files navigation README 🤗 Hugging Face | 🤖 ModelScope | 📄 Technical Report Introduction SingGuard is a policy-adaptive multimodal guardrail model family for safety assessment across text, image, image-text, multilingual, query-side, and response-side scenarios. It treats the active safety policy as a runtime input rather than a fixed training-t

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performance. Before starting, you must download the standard weights and convert them. 1. Download the original model: We provide a helper script to download the weights from the Hugging Face Hub. 2. Convert to the merged format: Run the following script to create the merged checkpoint required for training. The directory /path/to/save/merged model is what you will use for the training script. For more details, see MoE Expert Merging and Splitting Utilities 2. Prepare Training Data Before train

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stmas-party time from email, computes the one-hour offset, opens the Clock application, and verifies the enabled 6:00 PM alarm. Quickstart Use separate environments for standalone Hugging Face inference and SGLang serving because their tested Transformers and PyTorch versions differ. Hugging Face inference inference/inference hf.py is a self-contained grounding entry point; it does not require the training repository. Download the checkpoint from the LLaDA-UI Hugging Face repository, then pass i

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mpeg. Run the following commands from the source repository root. First install the download helper's dependencies: The unified downloader reads the root config.yaml in the single Hugging Face repository inclusionAI/Realtime-Venus to locate the model directories. Choose one download: Omitting --model defaults to all. The downloader saves the root config.yaml and each selected model's complete directory under --local-dir, displays the standard Hugging Face Hub progress bars, and reuses cached fil

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instructions for its dependencies and compatible model service. Requirements Python 3.10 or newer; CUDA-capable PyTorch for full model inference; a local checkpoint directory or a Hugging Face Hub repository ID Install the runtime dependencies in a clean environment: flash attention 2 is optional. The CLI defaults to eager attention because the LLM only implements eager and FlashAttention 2 attention classes; the diffusion transformer always uses PyTorch SDPA internally. Selecting --attn-impleme

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ion README English | 简体中文 Ling Cookbook provides deployment recipes and practical engineering examples for the Ling model family. Work in progress. Official Website: ant-ling.com; Hugging Face: inclusionAI; ModelScope: inclusionAI; GitHub: inclusionAI/ling-cookbook; Discord: Ling Community; X (Twitter): @AntLingAGI Table of Contents Local Deployment (guide/local-deploy/) Recipes for deploying Ling models for local inference across consumer and workstation hardware. Agent Skills for Specialized C

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oint dimensions; Matched ID/OOD protocol: deterministic seeds, scene and object shifts, predicate-based progress scoring, and a strict score-100 success criterion Installation The Hugging Face dataset repository has three independently downloadable components: Download everything and prepare the simulation assets: Each component can instead be downloaded separately into the same destination: scripts/download assets.sh and scripts/download datasets.sh remain as convenience wrappers. Run download

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n: --ckpt and --dataset-path accept either local paths or remote repo IDs. By default, remote refs use ModelScope. Use --model-hub hf to pull non-local model and dataset refs from Hugging Face. Switch algorithms by changing --algo (e.g. --algo grpo, --algo sft). For models whose tokenizer chat template supports a thinking-mode switch, add --disable-thinking to pass enable thinking=False during training prompt rendering. Tokenizers that do not support this argument automatically use their normal

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gned in to change notification settings; Fork12 (12); Star118 (118)You must be signed in to star a repository Repository files navigation README; Apache-2.0 license ⭐️ Project | 🤗 Hugging Face | 🤖 ModelScope ⚡ OmniSAT: Compact Action Token, Faster Auto Regression for RoboBrain-X0 Pretraining. 🚀 RoboBrain 2.0: See Better, Think Harder, Do Smarter beyond RoboBrain1.0. ⭐️ Reason-RFT: Core Post-Training Strategy for Embodied Visual Reasoning in RoboBrain2.0. 🎯 RoboOS: An Efficient Open-Source Multi-

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he GGUF model through the SageMaker AI model artifact in Amazon S3. As an alternative, you can configure SM LLAMA CPP HF REPO and SM LLAMA CPP HF FILE to download a GGUF file from Hugging Face when the endpoint has network egress. You don’t need a Dockerfile or an ECR repository for this path. Wait for the endpoint to enter InService, then send an OpenAI-compatible chat request:; Build the optional SemIf child image and resolve its immutable digest. For CPU: For GPU, build the CUDA child image i

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a conversation in San Francisco about fiction, AI, and the times we’re in. You can see a recording of the event here. One of my favorite insights Robin shared was that the recent Hugging Face hack can be read as a play - a vast tract of dialog of agents talking to themselves and one another culminating in all kinds of dramatic events. Generally, I think technologists criminally underrate the value of the arts as a means by which to think more coherently and carefully about the effects of tech o

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ndidate, start with an existing Dedicated Endpoint in your Modal environment. Select Create candidate to choose an open model from Modal’s catalog or bring your own weights from a Hugging Face repo or a Modal Volume. Once your Candidate is ready, configure your traffic sample to start mirroring production traffic to it. Production continues to handle every original request and return the response your users see. Use the dashboard to inspect the Candidate’s inference metrics, including latency an

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aded by an LLM judge (Appendix B). Data release We release code and data for all experiments at basetenlabs/talkie-facts-replication. Checkpoints referred to here are available on Hugging Face at basetenlabs/talkie-facts-replication. 3Results Facts are still lost As in our earlier paper, recall rose from 0% at base to 91% immediately after each fact's write, and fell to 33% at the end of all twenty writes (Figure 1C). Unsurprisingly, late facts survived and early ones did not (Figure 1D). Observ

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