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只是生成描述应该发生什么的文本，而是会实际执行这些操作。 这一能力在很大程度上取决于底层语言模型。根据伯克利函数调用排行榜（该榜单按模型将自然语言请求转化为可执行代码和系统命令的能力进行排名），Anthropic 的 Claude 4 模型目前在工具调用方面表现最佳。 但较新的开源模型正在快速追赶。Goose 的文档重点介绍了几个具有强大工具调用支持的选项：Meta 的 Llama 系列、阿里巴巴的 Qwen 模型、Google 的 Gemma 变体，以及 DeepSeek 以推理为核心的架构。 该工具还与模型上下文协议（Model Context Protocol，简称 MCP）集成，这是一种将 AI 智能体连接到外部服务的新兴标准。通过 MCP，Goose 可以访问数据库、搜索引擎、文件系统和第三方 API——将其能力远远扩展到基础语言模型所能提供的范围之外。 对于有兴趣搭建完全免费、保护隐私的配置的开发者来说，这个过程涉及三个主要组件：Goose 本身、Ollama（一款用于在本地运行开源模型的工具）以及一个兼容的语言模型。 第 1 步：安装 Ollama Ollama 是一个开

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servers. Images can be split across multiple encoders for load balancing; Vision Encoding: Encoder servers process images through ViT, generating vision embeddings and image grid metadata. Results are cached if enabled; Embedding Transfer: Vision embeddings are transferred back to the prefill server using the configured transfer backend (ZMQ, Mooncake, etc.); LLM Computation: The prefill server combines vision embeddings with text tokens to form mm inputs containing pre-computed tensors. The LL

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us give Claude these new abilities. At its simplest, a skill is a directory that contains a SKILL.md file . This file must start with YAML frontmatter that contains some required metadata: name and description . At startup, the agent pre-loads the name and description of every installed skill into its system prompt. This metadata is the first level of progressive disclosure : it provides just enough information for Claude to know when each skill should be used without loading all of it into con

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ng Huang for their invaluable guidance throughout this work. Special thanks as well to Nicolo Lucchesi, Alexander Matveev, Akshat Tripathi, and Saheli Bhattacharjee, for being an integral part of the V1 integration and the push for Cloud Next. Related Posts Announcing vllm-metal: Concurrent Serving on Apple Silicon vllm-metal brings vLLM's paged, continuously batched serving stack to Apple Silicon, with flatter TTFT under concurrent agent load, batched MTP, and automatic M5 prefill acceleration.

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subagents because the context and state management gets complex quickly. Sonnet 4.5 seems more aware of the right types of tasks to delegate, which could make this more practical; Meta-agent prompting. We're particularly interested in how this model handles meta-level reasoning about agent workflows. Early experiments suggest it works well with verification systems — letting the model reason about its own development process rather than just executing tasks; Context-management models. Sonnet 4.5

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and CUDA graph optimizations; Red Hat, including Tyler Michael Smith, Doug Smith, Tarun Kumar, and Elvir Crncevic for testing the model and tuning MoE kernels; Community partners: Meta, Roblox, etc. for testing, feedback, and scaling insights vLLM team members who contributed to this effort include: Jie Li, Kaichao You, Chen Zhang, Simon Mo. 👉 Qwen3-Next is now available in vLLM. Try it out today and experience ultra-efficient long-context inference with the latest hybrid MoE architecture. Relat

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latency, and sustainability. One-Sentence Summary GPT-5: enterprise routing for smarter inference; vLLM Semantic Router: technical-first routing for open-source LLMs; Edge future: context-aware, minimal-compute inference that works seamlessly Related Posts Announcing vllm-metal: Concurrent Serving on Apple Silicon vllm-metal brings vLLM's paged, continuously batched serving stack to Apple Silicon, with flatter TTFT under concurrent agent load, batched MTP, and automatic M5 prefill acceleration.

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integration focuses on geospatial foundation models, a class of convolutional or vision transformer models that requires data beyond RGB channels (e.g. multispectral or radar) and metadata (e.g. geolocation, date of image acquisition) used for, but not limited to, tasks like disaster response or land use classification from satellite imagery. However, the changes are generic and pave the way for serving a wide variety of non text-generating models. As a concrete example, we've integrated all geo

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loyees adopt these features at home, expectations for AI-assisted productivity at work will rise, influencing IT support and training plans. Source: TechRadar Summary: A review of Meta’s AI app finds inconsistent results months after launch, highlighting reliability gaps between marketing promises and real use. The report underscores the challenge of making consumer AI assistants dependable for daily tasks. Why it matters: Consumer trust influences adoption at work, so inconsistent quality can s

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as a manifest.json , which describes everything Claude Desktop and other apps supporting desktop extensions need to know. extension.mcpb (ZIP archive) ├── manifest.json Extension metadata and configuration ├── server/ MCP server implementation │ └── [server files] ├── dependencies/ All required packages/libraries └── icon.png Optional: Extension icon Example: Node.js Extension extension.mcpb ├── manifest.json Required: Extension metadata and configuration ├── server/ Server files │ └── index.js

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l. MPI programs are designed to run on a single supercomputer in a single datacenter within a single network. Thus, many MPI implementations assume local reachability. E.g. to use Meta’s Gloo across the public internet, a common - but theoretically unnecessary and practically slower - solution is to use a VPN. This was a necessary work around during the training of INTELLECT-1, which reduced throughput. Peers in an MPI program are fixed at the start of the program. Naively, if one process fails,

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Yannick Schnider from the Spyre team on vLLM for pluggable scheduler design and implementation, and other contributors, including yancong for extendable quantization method design and implementation, Aviv Keshet for extendable SamplingParams. Related Posts Announcing vllm-metal: Concurrent Serving on Apple Silicon vllm-metal brings vLLM's paged, continuously batched serving stack to Apple Silicon, with flatter TTFT under concurrent agent load, batched MTP, and automatic M5 prefill acceleration.

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un Llama 4 on AMD MI300X GPUs by building vLLM from source and using the same commands as above Inference Accuracy Validation: We validated inference accuracy against the official Meta report using lm-eval-harness. Here are the results for meta-llama/Llama-4-Maverick-17B-128E-Instruct: Llama 4’s model architecture is particularly well-suited for efficient long-context inference, thanks to features like: Mixture of Experts (MoE): Scout uses 16 experts (17B activated parameters), and Maverick uses

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ng model behaviors and corrupted tasks easily go unnoticed. In the next sections, we’ll describe how Docent solves this problem. Docent analyzes agent transcripts and accompanying metadata (e.g. correct task solutions, if available), and also supports counterfactual intervention on the agent's behavior or environment. Summarization provides a bird's-eye view of key steps the agent took, as well as interesting moments where the agent made mistakes, did unexpected things, or made important progres

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tracts away most distributed logic, enabling workers to operate the same way for both single-GPU and multi-GPU setups. 5. Efficient Input Preparation In vLLM V0, input tensors and metadata for the model are recreated at each step, often leading to significant CPU overhead. To optimize this, V1 implements the Persistent Batch technique, which caches the input tensors and only applies the diffs to them at each step. Additionally, V1 minimizes the CPU overheads in updating the tensors by extensivel

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Introduction; FP8; Pipeline Parallelism; Acknowledgements Today, the vLLM team is excited to partner with Meta to announce the support for the Llama 3.1 model series. Llama 3.1 comes with exciting new features with longer context length (up to 128K tokens), larger model size (up to 405B parameters), and more advanced model capabilities. The vLLM community has added many enhancements to make sure the longer, larger Llamas run smoothly on vLLM, which includes chunked prefill, FP8 quantization, and

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of the Hivemind library. Instead of using torch.distributed for the worker communication, Hivemind utilizes a distributed hash table (DHT) spread across each worker to communicate metadata and synchronize them. This DHT is implemented using the open-source libp2p project. We utilize Hivemind for inter-node communication between DiLoCo workers and PyTorch FSDP for intra-node communication within DiLoCo Workers. Our integration with Hivemind enables a real-world decentralized training setup for Di

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