搜索“Meta”

, and the VAE component must be the 4-channel AutoencoderKLQwenImage (argmax reference encoding). Missing, partial, or unknown values are errors; the loader never infers padding behavior from a directory name or silently falls back to another mode. Legacy packages that predate the component metadata are loaded strictly from a root inference profile.json during the compatibility window; when both exist, the component configs are authoritative and any disagreement with the legacy file is an error.

匹配字段正文

ning and inference engines Architecture The Awex weight exchange framework consists primarily of three components: WeightWriter: Runs within each training process, responsible for metadata collection and reporting of weight shards for the current training process, weight convert, resharding transfer plan construction, weight transmission, and other functions; WeightReader: Runs on the control process of each inference instance, which starts a WorkerWeightsReader on each GPU managed by the infere

匹配字段正文

, AMD GPUs, Intel GPUs, and x86/ARM/PowerPC CPUs. Additionally, diverse hardware plugins such as Google TPUs, Intel Gaudi, IBM Spyre, Huawei Ascend, Rebellions NPU, Apple Silicon, MetaX GPU, and more vLLM seamlessly supports 200+ model architectures on Hugging Face, including: Decoder-only LLMs (e.g., Llama, Qwen, Gemma); Mixture-of-Expert LLMs (e.g., Mixtral, DeepSeek-V3, Qwen-MoE, GPT-OSS); Hybrid attention and state-space models (e.g., Mamba, Qwen3.5); Multi-modal models (e.g., LLaVA, Qwen-VL

匹配字段正文

l (SM90); Fused KDA prefill kernel (SM10X); MTP support; More aggressive fusion of small neighboring kernels like cumsum for inference scenarios Acknowledgements This project is inspired by flash-linear-attention, CUTLASS, CuTe DSL, FlashInfer, Flash-Attention, and FlashMLA. We thank FLA-org and NVIDIA for their great work. Citation If you find cuLA useful, please cite it using the metadata in our CITATION.cff file: CUDA kernels for linear attention variants, written in CuTe DSL and CUTLASS C++.

匹配字段正文

enyi Zhuang, Jinjie Gu; Don’t Just Fine-tune the Agent, Tune the Environment arxiv, 2025. paper Siyuan Lu, Zechuan Wang, Hongxuan Zhang, Qintong Wu, Leilei Gan, Chenyi Zhuang, etc Meta Learning Profile-Aware Maneuvering: A Dynamic Multi-Agent System for Robust GAIA Problem Solving by AWorld. arxiv, 2025. paper, code Zhitian Xie, Qintong Wu, Chengyue Yu, Chenyi Zhuang, Jinjie Gu; Recon-Act: A Self-Evolving Multi-Agent Browser-Use System via Web Reconnaissance, Tool Generation, and Task Execution.

匹配字段正文

adapter. Responsibilities Start, wait for, inspect, measure, and delete sandboxes; Prepare local, OCI, Nydus, and S3-backed rootfs and mounts; Extract and inject OCI image startup metadata for in-sandbox runtimes; Allocate cgroups and pooled TAP or ephemeral veth endpoints, and configure iptables or eBPF for NAT; Discover, validate, and lease scheduler-selected accelerator devices Architecture API / CLI The public protobuf contract is api/runtime/v1/sandbox-api.proto. Runtime isolation, host req

匹配字段正文

leaderboard sprints to surface emerging research directions 🤝 Contributing We welcome issues & PRs! Please check each project’s CONTRIBUTING.md and adhere to its license terms. 📄 Citation If you use any component of the ecosystem, please cite the corresponding paper listed in that project’s README. 🛡️ License This meta‑repository is released under Apache‑2.0. Individual projects may apply different licenses—see their respective READMEs. Maintained by the FlagEval team · Last updated: 2025‑04‑23

匹配字段正文

peline 🤗 Model Zoo 🎲 General Visual Reasoning Tasks Then, your local directory should be like: Note: If you want to implement training process for our three main tasks, only three Meta-Json files -- 'A1-Spatial-Transformation-train-60k-cot.json', 'A2-Structure-Perception-train-4k5-cot.json' and 'A3-Visual-Counting-train-35k-cot.json' -- are needed respectively. Then, all the training-used json files can be constructed by these three Meta-Json files above. At this step, you should write the simpl

匹配字段正文

L, and LeRobot formats to meet diverse training needs. Dataset ingest: author dataset yaml files (tasks, device models, scenes, actions, etc.), which are validated to produce info metadata for ingestion; Format conversion: write conversion configs, align pose files and check frame counts, then run the official conversion to produce standard LeRobot datasets (including parquet, mp4, and meta); Tech overview: More updates coming soon. DataForge DataForge is a toolkit for embodied data filtering an

匹配字段正文

ion toolkit for the [RoboCOIN Dataset], this project is built upon the LeRobot repository. It maintains full compatibility with LeRobot’s data format while adding support for rich metadata—including subtasks, scene descriptions, and motion descriptions. RoboCOIN provides an end-to-end pipeline for dataset discovery, download, and standardized loading, along with model deployment capabilities across multiple robotic platforms. Key Features: Dataset Management: Seamless retrieval, downloading, and

匹配字段正文

ith our provided data processing scripts. Step 2. Process Your Own Dataset Here, we use dataset/example raw data as an example. Step 3. Fine-Tune GRM with Your Own Dataset Add the meta-info of your own dataset to train/qwenvl/data/ init .py Modify the path of training script train/scripts/finetune grm.sh Launch the training script Before you launch training, make sure that you have installed the packages below: Then, run the script below: 📑 Citation If you find our work helpful, feel free to cit

匹配字段正文

pendent, we’ve continued to invest in and refine our provenance tools. For general availability, our provenance safety tooling for our First-party (1P) products will include: C2PA metadata on all assets, providing verifiable origin through an industry standard; Visible moving watermark on videos downloaded from sora.com or the Sora app; Internal detection tools to help assess whether a certain video or audio was created by our products We recognize that there is not a single solution to provenan

匹配字段正文

istry workflows, evaluating multimodal chemical structure understanding; structure-activity relationship (SAR); prediction of drug potency, toxicity, and absorption, distribution, metabolism, excretion (ADME); multiparameter lead-optimization decision-making, and retrosynthesis. Table 1 3.2 Genomics and quantitative biology To evaluate GPT-Rosalind-5.5 on genomics understanding and quantitative biology, we designed an agentic evaluation on long horizon, end-to-end genomics analysis tasks. This e

匹配字段正文

e interpreted alongside remaining failures, evaluation awareness findings, and monitoring limitations; September 9, 2026: Naming and substance updates to the section on Verbalized Metagaming and Oversight Gaming. Verbalized Metagaming is when a model reasons in its Chain of Thought about how it will be graded, rewarded, or monitored, rather than only engaging the intended task. We revised this section to more prominently emphasize that we are currently measuring metagaming as verbalized in chain

匹配字段正文

rol (Figure 15) and mostly consistent across the nine CoT instruction types (Figure 16). Qualitatively, while GPT-5.5 frequently thought about the instruction at length, including meta-discussion of whether to obey constraints on the analysis channel expressed in the prompt, GPT-5.6 Sol more often either follows the constraint directly or fails in a much terser way. We are investigating the causes of increased CoT controllability of GPT-5.6 Sol. Update on June 27th: we updated some plots in the

匹配字段正文

rol (Figure 15) and mostly consistent across the nine CoT instruction types (Figure 16). Qualitatively, while GPT-5.5 frequently thought about the instruction at length, including meta-discussion of whether to obey constraints on the analysis channel expressed in the prompt, GPT-5.6 Sol more often either follows the constraint directly or fails in a much terser way. We are investigating the causes of increased CoT controllability of GPT-5.6 Sol. Metagaming is when a model reasons about how it wi

匹配字段正文

Provenance We have continued to prioritize enhancing our provenance tools. For ChatGPT Images 2.5, our expanded provenance safety tooling includes: A continued commitment to C2PA metadata, an industry-standard framework that enables automated disclosure of provenance information, through the C2PA Conformance Program; To make provenance more resilient, we are taking a multi-layered approach and incorporating watermarking through Google DeepMind’s SynthID⁠ through ChatGPT⁠, Codex, and the OpenAI

匹配字段正文

Provenance We have continued to prioritize enhancing our provenance tools. For ChatGPT Images 2.0, our expanded provenance safety tooling includes: A continued commitment to C2PA metadata, an industry-standard framework that enables automated disclosure of provenance information, through the C2PA Conformance Program; Integrating an imperceptible, robust, and content-specific watermark alongside internal tooling to help assess whether a certain image was created by our products We recognize that

匹配字段正文

DALL·E and Sora, we have continued to prioritize enhancing our provenance tools. For general availability of 4o image generation, our provenance safety tooling will include: C2PA metadata on all assets (verifiable origin, industry standard); Internal tooling to help assess whether a certain image is created by our products We recognize that there is no single solution to provenance, but are committed to improving the provenance ecosystem, continuing to collaborate on this issue across industry

匹配字段正文

rs express thoughts of harming themselves, such as directing them to professional helplines.) At least one AI company is thinking about monetizing its users’ mortality. Last year, Meta was granted a patent for an AI system that could keep a user account active well after death. In the words of the patent, “the language model may be used for simulating the user when the user is absent from the social networking system, for example, when the user takes a long break or if the user is deceased”. It’

匹配字段正文