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This AI Paper from Meta AI Unveils Dualformer: Controllable Fast and Slow Thinking with Randomized Reasoning Traces, Revolutionizing AI Decision-Making

Marktechpost

On the other hand, models relying on slow and complete reasoning traces, such as Searchformer, provide better accuracy but underperform due to longer steps of reasoning and its high computational cost. Besides that, when in auto mode, the model selects its strategy, it still stays high, with a high optimal rate of 96.6%

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SGLang: Efficient Execution of Structured Language Model Programs

Unite.AI

These new use cases necessitate multiple, often dependent, LLM generation calls, indicating a trend of using multi-call structures to complete complex tasks. State-of-the-art inference engines, optimized to reduce latency and improve throughput, lack direct knowledge of the workload, resulting in significant inefficiencies.

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Host ML models on Amazon SageMaker using Triton: TensorRT models

AWS Machine Learning Blog

With kernel auto-tuning, the engine selects the best algorithm for the target GPU, maximizing hardware utilization. Overall, TensorRT’s combination of techniques results in faster inference and lower latency compared to other inference engines. Note that the cell takes around 30 minutes to complete. !docker

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Improved ML model deployment using Amazon SageMaker Inference Recommender

AWS Machine Learning Blog

With advancements in hardware design, a wide range of CPU- and GPU-based infrastructures are available to help you speed up inference performance. Analyze the default and advanced Inference Recommender job results, which include ML instance type recommendation latency, performance, and cost metrics. sm_client = boto3.client("sagemaker",

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No More Paid Endpoints: How to Create Your Own Free Text Generation Endpoints with Ease

Mlearning.ai

The following libraries are included in the file: requirements.txt datasets transformers accelerate einops safetensors The complete example can be viewed at: Falcon 7B HuggingFace Spaces. LLM from a CPU-Optimized (GGML) format: LLaMA.cpp is a C++ library that provides a high-performance inference engine for large language models (LLMs).

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Build a personalized avatar with generative AI using Amazon SageMaker

AWS Machine Learning Blog

It also provides a built-in queuing mechanism for queuing up requests, and a task completion notification mechanism via Amazon SNS, in addition to other native features of SageMaker hosting such as auto scaling. To host the asynchronous endpoint, we must complete several steps. amazonaws.com/djl-inference:0.21.0-deepspeed0.8.3-cu117"