搜索“RAG”

tial; Commercial; Forest; Airport; Bridge; Park; Farmland; SparseResidential; BaseballField; School; Playground; Square; Stadium; Meadow; Beach; Resort; DenseResidential; Port; StorageTanks; Pond; Viaduct; Industrial; Center. - Output your result using exclusively the following schema: {'image description': FieldInfo(annotation=str, required=True), 'label': FieldInfo(annotation=str, required=True)} - Put your results between a json tag json Some classes can be quite challenging to differentiate

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, a technique for managing attention key-value caches more efficiently. Instead of storing the kv-cache contiguously, vLLM divides it into multiple memory pages, greatly reducing fragmentation and over-allocation. This allows vLLM to minimize memory waste to under 4%, compared to 60%-80% with traditional approaches. Such efficient memory handling is crucial for models like MiniMax-M1 that support ultra-long context lengths, ensuring smooth and stable inference without running into memory bottlen

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AI agent companies in 2025, and then returning a list of companies to the lead agent so it can compile a final answer. Traditional approaches using Retrieval Augmented Generation (RAG) use static retrieval. That is, they fetch some set of chunks that are most similar to an input query and use these chunks to generate a response. In contrast, our architecture uses a multi-step search that dynamically finds relevant information, adapts to new findings, and analyzes results to formulate high-qualit

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re anything; it tells the LLM what variable names to use when referencing these values extracted from the porting plan metadata later, similar to how variable assignments work. Storage happens implicitly in the LLM's context. The "in parallel" instruction tells the LLM to use multiple tools simultaneously rather than sequentially, speeding up execution. Step 2a first tries to read the conventions file using ${targetRuntime}-conventions.md - this is like string interpolation, where the LLM insert

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ed for high-throughput inference, pulling models from the Hugging Face Hub and optimizing them for production-ready performance. A recent addition to the vLLM codebase enables leveraging Transformers as a backend for model implementations. vLLM will therefore optimize throughput/latency on top of existing Transformers architectures. In this post, we’ll explore how vLLM leverages the Transformers modeling backend to combine flexibility with efficiency, enabling you to deploy state-of-the-art mode

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vironment database The primary evaluation metric used in τ-bench is pass^ k , which measures the probability that all k independent task trials are successful for a given task, averaged across all tasks. Unlike the pass@ k metric that is common for other LLM evaluations (which measures if at least one of k trials succeeds), pass^ k evaluates consistency and reliability—critical qualities for customer service applications where consistent adherence to policies is essential. Performance Analysis O

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tion ensures teams can move from discussion to development with minimal manual intervention. Figure 2: TranscriptToPRDTicket agentic workflow At its core, our agentic solution leverages two key components: PRDAgent and TicketCreationAgent, both powered by Mistral Large 2, a state-of-the-art LLM. The workflow follows a clear progression: Meeting transcripts serve as the input, capturing raw stakeholder discussions. PRDAgent processes these transcripts to generate comprehensive PRDs. TicketCreatio

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e models efficiently requires a combination of Tensor Parallelism, Pipeline Parallelism, and performance optimizations like Chunked Prefill. vLLM enables scalable inference by leveraging these techniques while ensuring adaptability across different hardware accelerators. As we continue to enhance vLLM, staying informed about new developments such as expert parallelism for Mixture of Experts (MoE) and expanded quantization support will be crucial for optimizing AI workloads. Come to the Bi-weekly

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es backends: In vLLM’s v0 architecture, we've implemented XGrammar as a logit processor, optimizing it with caching for tokenizer data. While the performance improvements are encouraging, we believe there's still significant room for optimization. There are still a few usability concerns in XGrammar v0 integration to match feature parity with all use cases: It is yet to support grammars other than GBNF format (PR on vLLM: github); It is yet to support regex; It is yet to support complex JSON tha

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h. When and how to use frameworks There are many frameworks that make agentic systems easier to implement, including: The Claude Agent SDK ; Strands Agents SDK by AWS ; Rivet , a drag and drop GUI LLM workflow builder; and Vellum , another GUI tool for building and testing complex workflows. We suggest that developers start by using LLM APIs directly: many patterns can be implemented in a few lines of code. If you do use a framework, ensure you understand the underlying code. Incorrect assumptio

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5B. It also achieves 1.8x higher throughput and 5.1x faster TTFT than TGI for Llama 3.1 70B. This guide explores 8 key vLLM settings to maximize efficiency, showing you how to leverage the power of open-source LLM inference on AMD. If you just want to see the optimal parameters, jump to the Quick Start Guide. Introduction Meta recently announced they're running 100% of their live Llama 3.1 405B model traffic on AMD MI300X GPUs, showcasing the power and readiness of AMD's ROCm platform for large

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Speculative decoding in vLLM is a powerful technique that accelerates token generation by leveraging both small and large models in tandem. In this blog, we’ll break down speculative decoding in vLLM, how it works, and the performance improvements it brings. This content is based on a session from our bi-weekly vLLM Office Hours, where we discuss techniques and updates to optimize vLLM performance. You can view the session slides here. If you prefer watching, you can view the full recording on Y

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rt-term memory of the model to learn; Long-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval; Tool use The agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more A compl

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e }s(v)=\rho^{- \vert \{\ell \in \mathcal{L} \vert (v, \ell)\in E \}\vert},\quad\rho 1 $$ such that $s(v)$ is low if many of $v$’s neighbors are selected and thus the scoring encourages to pick diverse samples; Rubin et al. (2022) proposed to train embeddings via contrastive learning specific to one training dataset for in-context learning sample selection. Given each training pair $(x, y)$, the quality of one example $e i$ (formatted input-output pair) can be measured by a conditioned probabili

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