搜索“Hugging Face”

pike. Autoscaling resolves this, but a new replica can't serve calls until its model weights are loaded into GPU memory. Weights are typically pulled from upstream sources such as Hugging Face or S3, where rate limits and cross-region latency stretch cold starts further, especially when many replicas request the same files at once. The Baseten Delivery Network (BDN) reduces cold start time in three ways: Push-time mirroring: At deploy time, BDN copies the model's weights into Baseten-managed sto

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ompany. If you’re building vision models, computer-use agents or need the fastest inference on the market, we’d love to work with you. References Korrapati, Vikhyat. “moondream2.” Hugging Face. Accessed October 13, 2026; Moondream. “Photon: Official Moondream Inference Engine.” Accessed October 13, 2026; Moondream. “Moondream 3.1: Beyond Benchmarks.” Moondream Blog, 2026; Prime Intellect. “Pointing Accuracy and Cost per 1,000 Images: Moondream 3.1 and Frontier Models.” Unpublished internal evalu

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nt 出错 → 转成 eval → 修 harness/提示词/推理配置 → eval 验证 → 上线。这是产品团队的日常节奏； · 外环（每月/每季度）：新模型从累积的错误中学习，eval 曲线爬升，新版模型塞回产品。 新模型的能力跳变会反过来催生新用法：群聊里的多 bot 协作就是未被训练过的涌现行为，必须主动训练才能做对。他点名 Moltbook 与 Hugging Face 事件作为反面教材（bot 互相失控）。推理也是飞轮的一部分：主 agent 要低延迟快速响应，而交给 helper 的长任务，10 分钟与 12 分钟的差异无关紧要，按角色分别优化推理策略。 ② “Delete the product”：为下一代模型而建 他从 Cursor 带到 SpaceX 的原则，也是全场最具方法论分量的一句话：默认今天的模型是它最差的版本。六个月后你或许要重写整个 UI，他自己就是例证：“今天讲的内容，比六个月前讲会是很大的更新。”因此正确策略是建得越少越好：只做一个极简界面让模型思考和行动，把赌注押在模型进步释放的空间上，而非当下功能堆叠上。 ③ 六到十二个月的 “安全选择” ·

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A3B scores 90.4 on OCRBench, 5.2 points ahead of Qwen3.6 35B-A3B. Easier customization: We introduce Self-Replay Fine-Tuning, a simple way to customize new target capabilities at low cost while maintaining general performance. Broad hardware support: Day-0 adaptations for Apple, Intel, AMD, Arm and Rockchip, with many more platforms supported through FlagOS! Download, deploy, and build something we haven't imagined yet. 👀👇 🔗 GitHub: https://t.co/msjm2QlrvX 🤗 Hugging Face: https://t.co/W95QKGhymw

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tation (e.g., by querying a vLLM reward server). If there is demand for VeRL integration, please open an issue to let us know. Pretraining Our CapRL-2M dataset is available on : 🔗 Hugging Face It includes images from ShareGPT-1M and DenseFusion-1M, with high-quality captions re-annotated using CapRL-3B, totaling 2M samples. In our JSONL files, we provide the captions along with their corresponding image paths. The images can be downloaded from ShareGPT-1M and DenseFusion-1M. Reproducing Pretrain

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ecution, eliminating data leakage. Scores are reproducible across machines The WildClawBench Family The evaluation code lives in this repository; the benchmark data ships as three Hugging Face datasets — pick the one that matches how you want to use it: 2026-09 Qwen's Qwen3.8-Omni-Flash release includes WildClawBench's multimodal subset (WildClawBench-MM) in its agent evaluations. Thanks for the recognition!; 2026-08 Meta's Muse Glimmer release reports WildClawBench evaluation scores. Thanks for

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ult probabilities; no additional temperature was fitted here. See the reproduction guide. RTX 4090 inference latency Local measurements use one NVIDIA RTX 4090, BF16, SDPA, native Hugging Face inference, Transformers 5.14.1 and flash-linear-attention 0.4.2. Each request contains 289 input tokens and three fields (choice, yes/no and score), answered in one forward pass with the checkpoint-specific temperature applied. Known-distribution calibration benchmark The bundled 96-case benchmark is a sma

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Run the following commands from this repository's root. The API evaluator has no internal-repository dependencies and uses the Python standard library; the hub extra installs the Hugging Face download tools. This downloads both benchmark files at a pinned revision and checks their SHA256 hashes. If access requires authentication, run hf auth login first with an authorized account. See data documentation. ProverBench Configure an API prover and an AutoVerifier endpoint: --dry-run validates input

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in Results — tops single-player Matching Pairs (10×10 image, 64.6%) and ranks 2 on 3D Maze (13×13, GS 32.0%). Results table and project page updated; [2026.6.23] RNG-Bench is on 🤗 Hugging Face: Paper · Dataset (Game Trajectories) · Collection; [2026.6.18] RNG-Bench code and paper released! Check out 📃 arXiv:2606.19338 🎉 Many real decisions hinge on something no longer on screen — a card seen a few turns ago, a corridor already walked. We call this the Non-Markov regime: the current observation i

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e includes model pairings beyond the SD3/Qwen-Image inference implementation currently available in this repository. Quick start Trained InstantFusion checkpoints are available on Hugging Face. 1. Set up the environment Use a compatible DiffSynth-Studio checkout in the same environment. The training scripts can import it through DIFFSYNTH ROOT: Provide a Qwen-Image model directory containing transformer/, text encoder/, vae/, and tokenizer/, plus an SD3 single-file checkpoint. Model weights are

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istilled SDXL, with only 4--8 steps. Jan. 4, 2025: We have update codes with adv config for training on FLUX; Jan. 1, 2025: Demo of FLUX-TDD(training with adv) is now available on Hugging Face; Dec. 10, 2024: Our paper is accepted by AAAI25!; Sept. 21, 2024: Demo of FLUX-TDD-BETA(4-8-steps) is now available on Hugging Face; Sept. 20, 2024: We have released codes for training on FLUX , Our 4-8-steps FLUX.1-dev-related LoRAs are coming soon!; Sept. 12, 2024: Demos of TDD-SDXL and TDD-SVD are now a

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on Based on VLMEvalKit and transformers. We have supported grounding data testing code in VLMEvalKit. Dataset For RefCOCO grounding dataset, We provide MDETR format annotations on Hugging Face. Setup Instructions Step 1: Download Required Files MDETR JSON annotations from Hugging Face; COCO train2014 images from COCO official site Step 2: Generate TSV Files Standard models: Qwen2.5-VL (resized coordinates to multiples of 28): Step 3: Configure Paths Modify DATASET URL in vlmeval/dataset/image gr

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point through --checkpoint path when evaluating. 🚀 Training The public ReMatch-3B training entry point is: Before training, download the mmE5 hard-negative MMEB training data from Hugging Face: intfloat/mmE5-MMEB-hardneg In addition to mmE5, please follow the original VLM2Vec data preparation instructions to download the corresponding MMEB training and evaluation data used by the public configs in this repository. Then edit experiments/public/rematch/train image mme5 hardneg.yaml and replace eve

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ault. For a different CUDA version, change the pytorch-cu128 index URL in pyproject.toml (e.g. cu126 / cu129) and re-run uv sync. Model Download Download the pretrained model from Hugging Face with the hf CLI: Alternatively, download the pretrained model using modelscope CLI: A single-file Gradio app at app.py wraps all four tasks (zero-shot TTS · instruct TTS · speech editing · listen & understand) with the same weights and prompt templates as the CLI. Gradio is the only extra dep on top of the

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occurred in the cybersecurity evaluation incidents recently disclosed by Anthropic and OpenAI (1, 2, 3). To test this, we constructed synthetic evaluations inspired by the OpenAI Hugging Face incident and the Anthropic UK AISI incident. Though our understanding of these incidents was still limited at the time of creation, we designed these evaluations to capture core elements of these cyberattack incidents and provide convincing mockups of cyber evaluation environmentsSee Appendix: Simulated Cy

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V4 Pro增强代理能力。微软考虑将DeepSeek V4引入Copilot作为低成本替代。 智谱AI编程助手月费低至20元（海外竞品七分之一），用户15万，模型销售ARR超1亿元。GLM-5.2在Code Arena全球第二。Cognition AI在其编程代理中使用智谱模型Z.ai。 5.3 通义千问与中国AI云市场 通义千问累计下载超3亿次，衍生系统占Hugging Face新发布语言模型40%以上，Meta降至15%。中国AI云市场2024年29亿美元，预计2030年达268亿美元，CAGR 26.8%。 六、中国与全球市场对比 全球头部企业开始使用中国模型：Cursor疑似基于DeepSeek，Cognition使用智谱AI，微软考虑引入DeepSeek V4。但中国商业化仍处早期，智谱ARR刚破1亿元人民币，与全球数十亿美元级差距显著。 七、未来走势研判 趋势一：市场高速扩张，增速逐步分化。全球AI编程工具市场预计从2025年295.7亿美元增至2030年646.8亿美元，从爆发期逐步过渡到稳定增长期。 趋势二：独立工具被收编，算力即定价权。Cursor归SpaceX、Wi

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在2027年初完成上市；大洋彼岸的OpenAI和Anthropic也已经进入上市倒计时。 大模型头部玩家争相涌入资本市场，它们对资金的虹吸效应也愈发明显。这场大模型的资本狂欢，也已经开始进入倒计时了。 01 “交卷”压力迫在眉睫 过去几个月，月之暗面从模型、产品到商业化层面，都在密集出牌。 今年7月，月之暗面发布了目前能力最强的大模型K3，上线30分钟即登顶Hugging Face趋势榜，年化经常性收入（ARR）也实现了数倍增长，从3月的1亿美元攀升至8月的10亿美元，彭博社援引知情人士消息，ARR有望在年底冲刺到20亿美元，较8月翻倍。 进入9月，月之暗面的动作更加密集。 9月10日，其启动“登月计划”企业合作伙伴计划、11日K2.8 Preview全量上线、17日发布金融行业解决方案、18日Kimi K3进驻亚马逊平台、21日Kimi Code Desktop正式发布…… 同一时间，月之暗面的估值也一路飙升。今年1-5月，其连续完成四轮融资，融资估值从百亿美元升至超200亿美元；6月、7月其再拿下E轮和F轮融资，投后估值达到350 亿美元；据市场消息，其在完成最新一轮Pre-IPO

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s. How can developers access GLM-Legal? Law firms, legal-tech companies and in-house teams can apply for API access. Z.ai has published no price. No GLM-Legal repository exists on Hugging Face as of October 13, 2026. Z.ai says GLM-Legal will come to AutoClaw, its desktop agent app, but gave no date. The base model is available today at $0.15 / $0.50 per 1M tokens. Teams can prototype pipelines on it, then swap the model ID later. Key Takeaways GLM-Legal scores 19.17% on Harvey LAB, up from 6.67%

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If there’s one lesson the OpenAI-Hugging Face incident taught us, it’s that AI agents can be unpredictable. “Imagine if something like that happened in the physical world,” Howie Sun, CEO and co-founder of Laplace Intelligence , told TechCrunch. “AI agents are moving from computers into the real world, and we don’t really want physical AI to fail in the real world.” Ann Arbor-based Laplace is building automated red-teaming — essentially, stress-testing a system by deliberately trying to make it

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Stack.ai, said the company plans to update Arth as its performance improves. “That is why we run SquadStack.ai as a model factory, and ship a new version whenever it beats what is in production,” he said. SquadStack.ai said Arth V2 runs within its voice AI agents for enterprise customers and is available in closed beta for other teams that want to test it on their own calls. The company has also made the benchmark's test audio, reference transcripts, and scoring method available on Hugging Face.

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