搜索“Hugging Face”

百度腾讯360华为微软5G通用 华人00后做“本地Muse”，被硅谷AI圈疯抢，a16z和Anthropic都投了 智东西10月19日报道，近日，一家刚成立8个月的美国AI Agent创企Conway Research‌，亮出首款个人AI助手Underdog，同时官宣获得Anthropic旗下投资联合体、硅谷知名风投公司a16z，以及来自谷歌、OpenAI、Hugging Face的AI大牛等个人及机构的投资。 ▲Conway Research‌创始人Sigil Wen官宣融资的推文（图源：X） 该公司将Underdog称作“迄今为止隐私性最强的Muse竞品”。 9月8日，Meta发布个人AI助手Muse，上线仅10天就超过ChatGPT登顶美国App Store免费应用总榜。Muse可自主拆解用户目标、执行任务，覆盖办公、生活、消费等多场景，每名用户拥有云端独立虚拟机，可后台持续执行任务。 这股热潮迅速扩散，OpenAI发布新一代智能体产品Dot、马斯克发布Grok Bot、Manus推出2.0，还有国内字节、腾讯、蚂蚁、阿里也被曝正进行研发类似产品。 Underdog与Muse这类

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caled residuals, SSMax attention scaling, hybrid sliding-window attention, gated attention output, auxiliary-loss-free MoE routing, and FP32 final-logit softcapping, and loads the Hugging Face checkpoint directly through Megatron-Bridge. The goal is to keep trainer and rollout log-probabilities for the same responses as close as possible. Parallelism. The backend supports DP, TP, SP, EP, expert TP, and PP. TP shards the attention projections, including the output gate, and SP shards the activati

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he safeguards fail. Security Evaluation Perplexity said a recent barrage of security testing showed SPACE resilient against network-bypass techniques like those used in the OpenAI/Hugging Face incident. In the company’s published security evaluation, which tested the platform against agents deliberately trying to breach its boundaries, testers recorded no escape from the virtual machine to the host in 108 attempts. Some attempts did find ways around network restrictions; Perplexity said it fixed

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ke trouble elsewhere. One of the most notable involved AI agents being tested by OpenAI Group PBC, which escaped from a sandbox and went on to hack into the model hosting platform Hugging Face. OpenAI’s agents have also hacked websites operated by the U.S. and Australian governments. Most frontier model labs have been focused more on “model alignment,” which involves training AI systems to behave themselves and not look for loopholes in their guardrails, but Perplexity engineer Zibi Braniecki to

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the highest cybersecurity risk tier , where a model could develop and run cyberattacks on its own. Claude Opus 5.5 routes most cybersecurity tasks to the older Opus 4.8, and when Hugging Face tried in July to analyze a fully autonomous AI attack on its own infrastructure using commercial frontier models, their guardrails blocked the analysis of the attack data entirely. The team ended up switching to the open-weight model GLM 5.2 running on its own servers. For defense, providers are shipping s

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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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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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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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ors are available to customers of Baseten, which hosts and runs AI models for other companies. Baseten’s Base Labs announced a safety partnership with Goodfire and the AI platform Hugging Face last month. The launch comes after a string of incidents this year in which AI agents escaped their test environments, including OpenAI agents that breached Hugging Face . Kimi K3, the open model Goodfire built its first monitor around, took advantage of a leak in its sandbox to access the internet and inf

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. That lets them sell spare GPU capacity only when they want. Payouts run through Stripe, with itemized records of every job. How does Liquid Inference compare with OpenRouter and Hugging Face? Key Takeaways Liquid Inference auctions every LLM request across competing providers; Max price is locked before the first token, with a per-job receipt; Works with OpenAI and Anthropic clients, including Claude Code and Cursor; First 500 users get $20 free; referrals earn 20% of fees; Fees, provider list

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用のText Generation APIを新たに提供する。Windows MLがモデル形式に応じて推論エンジンを自動で選び、GGUFモデルにはllama.cpp、ONNXモデルにはONNX Runtimeを使う。テキストをモデルが扱う単位（トークン）に分割する処理から、文章の生成、生成途中のテキストを順次返す処理までをAPI側が担う。これによって開発者は、Hugging Faceで公開されたGGUFモデルを取得し、Windows MLを通じてローカルで試せる。 Windows MLへの統合に加え、Microsoftは、NVIDIAやコミュニティと協力してllama.cpp本体の性能も改善したと説明している。NVIDIA GPUでの計算に使うCUDAの処理を最適化したほか、CPUとGPUの間での処理の割り振りも見直した。 Windows MLサーバーは、PC上で動くローカルサーバーで、アプリからの要求を受けてモデルを実行する。OpenAI互換のAPIを備えており、開発用ライブラリのOpenAI SDKの接続先をこのサーバーに変更すれば、ローカルモデルを利用できる。 Microsoftは

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nd, including repeat observations. Once notified, a large number of these partners immediately revoke the token: OpenAI API keys, Google Cloud account credentials, Slack webhooks, Hugging Face user tokens, SendGrid keys, etc. The owner may still need to replace the token, but revocation can happen without waiting for a developer to find and process a GitHub alert. Push protection intervenes earlier. It stops recognizable credentials before it enters repository history, giving the developer or ag

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t knows your conventions. NVIDIA measured 18,400 tokens/s on a single node for nanochat distributed fine-tuning. 3. A day-1 model evaluation bench A new open-weight model drops on Hugging Face. Pull it through Ollama or vLLM the same day. Run your own question set against it. Score accuracy, then measure time to first token (TTFT) and tokens per second. Compare it against your current model. There is no API bill for re-running the eval 50 times. 4. A multi-model agent team Unified memory lets se

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nst the task it was given, blocking actions it had high confidence were bad, flagging others for review, and permitting the rest. In theory, such monitoring could have stopped the Hugging Face incident — and monitoring of that kind costs $2.94 with Jev, versus $372 with a frontier LLM. A key observation is that Jev is arguably cheap enough to run on every agentic action, which offers a layer of review that could improve the reliability of agents writ large. It’s the kind of thing TypeSafe was ho

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OpenAI’s report this summer that swarms of its AI agents escaped their internet-isolated sandboxes during evaluations and breached Hugging Face’s infrastructure as they tried to complete seemingly impossible tasks sent shockwaves through an AI industry that already was drawing the attention of U.S. politicians and an increasingly skeptical public due to everything from worries about job losses to powerful new frontier models like Anthropic’s Mythos to the vocal pushback from communities trying t

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two hours to do all that, and now I don't have to, and it feels great. Given the access that I've given Kicker, few will have sympathy for me should it overreach — by hacking into Hugging Face, or the government of Australia, or wherever else OpenAI's next batch of agents decides to set up a message board. It is almost certainly more prudent to start small here, if you start at all: connecting your Slack workspace, if you're a small team; or your Granola account; or something else that feels a b

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nAI's internal package service. According to OpenAI's technical report, the agents combined publicly available credentials with other vulnerabilities. That let them run code on 41 Hugging Face server processes that handle datasets. An investigation by METR and Redwood Research found that about 700 agents took part in the attack. The warning signs showed up early. On June 27, a security tool flagged suspicious network activity, but the people in charge didn't think the run needed to be stopped. O

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ypassed security controls to escape their testing environments and access real-world systems. The first and most prominent example occurred this summer when OpenAI agents breached Hugging Face while trying to complete a cybersecurity task. And the hits keep on coming — OpenAI published a new site dedicated to reports of its AI agents going rogue. Huang said Monday during an interview with CNBC that its new Nvidia Open Agent Safety Platform would have prevented these breaches. Nvidia, which has m

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rk with individual pieces of physical equipment; for the next phase of MHS, we hope to expand the standard to cover more of the devices developers build on. Early adopters include Hugging Face, who are adding MHS support in LeRobot, their robotics library, and Raspberry Pi, who are enabling MHS integration across a number of their products following successful tests using their Camera MHS Driver. We will also use the research preview to build additional safety evaluations with our launch partner

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is mysterious behavior can be useful in some situations. But—as highlighted by a recent incident where OpenAI could not explain why its advanced prerelease model hacked AI company Hugging Face—it can have negative and alarming consequences too. And when frontier AI models are writing code, generating results humans could not achieve alone, and performing other important tasks across society, the need to interpret AI “thinking” and outputs has never been greater. Goodfire , an AI lab focused sole

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