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Design Patterns in Python for AI and LLM Engineers: A Practical Guide

Unite.AI

For AI and large language model (LLM) engineers , design patterns help build robust, scalable, and maintainable systems that handle complex workflows efficiently. This article dives into design patterns in Python, focusing on their relevance in AI and LLM -based systems. model inference, real-time updates).

Python 147
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The Best Inference APIs for Open LLMs to Enhance Your AI App

Unite.AI

Groq groq Groq is renowned for its high-performance AI inference technology. Their standout product, the Language Processing Units (LPU) Inference Engine , combines specialized hardware and optimized software to deliver exceptional compute speed, quality, and energy efficiency. per million tokens.

LLM 278
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SGLang: An Open-Source Inference Engine Transforming LLM Deployment through CPU Scheduling, Cache-Aware Load Balancing, and Rapid Structured Output Generation

Marktechpost

SGLang is an open-source inference engine designed by the SGLang team to address these challenges. It optimizes CPU and GPU resources during inference, achieving significantly higher throughput than many competitive solutions. RadixAttention is central to SGLang, which reuses shared prompt prefixes across multiple requests.

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LightLLM: A Lightweight, Scalable, and High-Speed Python Framework for LLM Inference and Serving

Marktechpost

Researchers developed an efficient, scalable, and lightweight framework for LLM inference, LightLLM, to address the challenge of efficiently deploying LLMs in environments with limited computational resources, such as mobile devices, edge computing, and resource-constrained environments.

LLM 67
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SGLang: Efficient Execution of Structured Language Model Programs

Unite.AI

higher throughput compared to state-of-the-art inference systems on various large language and multimodal models, tackling tasks such as agent control, logical reasoning, few-shot learning benchmarks, JSON decoding, retrieval-augmented generation pipelines, and multi-turn chat. Experiments demonstrate that SGLang achieves up to 6.4×

LLM 130
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CMU Researchers Introduce ReLM: An AI System For Validating And Querying LLMs Using Standard Regular Expressions

Marktechpost

For the ever-growing challenge of LLM validation, ReLM provides a competitive and generalized starting point. ReLM is the first solution that allows practitioners to directly measure LLM behavior over collections too vast to enumerate by describing a query as the whole set of test patterns.

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SGLang: A Structured Generation Language for Efficient Execution of Complex Language Model Programs

Marktechpost

Recent advancements in LLM capabilities have increased their usability by enabling them to do a broader range of general activities autonomously. There are two main obstacles to effective LM program utilization: The non-deterministic character of LLMs makes programming LM programs tedious and complex.