Remove Benchmark Remove Engineering Remove Scripts
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Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning

Using its enterprise software, FloTorch conducted an extensive comparison between Amazon Nova models and OpenAIs GPT-4o models with the Comprehensive Retrieval Augmented Generation (CRAG) benchmark dataset. FloTorch used these queries and their ground truth answers to create a subset benchmark dataset.

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Accelerate NLP inference with ONNX Runtime on AWS Graviton processors

AWS Machine Learning

ONNX Runtime is the runtime engine used for model inference and training with ONNX. We also demonstrate the resulting speedup through benchmarking. Benchmark setup We used an AWS Graviton3-based c7g.4xl 1014-aws kernel) The ONNX Runtime repo provides inference benchmarking scripts for transformers-based language models.

Benchmark 133
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Achieve ~2x speed-up in LLM inference with Medusa-1 on Amazon SageMaker AI

AWS Machine Learning

We also included a data exploration script to analyze the length of input and output tokens. As a next step, you can explore fine-tuning your own LLM with Medusa heads on your own dataset and benchmark the results for your specific use case, using the provided GitHub repository.

Scripts 77
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Unlocking Innovation: AWS and Anthropic push the boundaries of generative AI together

AWS Machine Learning

Current evaluations from Anthropic suggest that the Claude 3 model family outperforms comparable models in math word problem solving (MATH) and multilingual math (MGSM) benchmarks, critical benchmarks used today for LLMs. Media organizations can generate image captions or video scripts automatically.

Benchmark 145
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Generate training data and cost-effectively train categorical models with Amazon Bedrock

AWS Machine Learning

This requirement translates into time and effort investment of trained personnel, who could be support engineers or other technical staff, to review tens of thousands of support cases to arrive at an even distribution of 3,000 per category. Sonnet prediction accuracy through prompt engineering. We expect to release version 4.2.2

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Boost inference performance for LLMs with new Amazon SageMaker containers

AWS Machine Learning

In this post, we dive deep into the new features with the latest release of LMI DLCs, discuss performance benchmarks, and outline the steps required to deploy LLMs with LMI DLCs to maximize performance and reduce costs. To use SmoothQuant, set option.quantize=smoothquan t with engine = DeepSpeed in serving.properties.

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Achieving Excellence: Best Practices for Contact Center Performance and Quality Assurance

Hodusoft

Performance in a contact center refers to how effectively agents manage calls, resolve issues, and meet established benchmarks. Agent Script Adherence: Monitoring and measuring how well agents follow provided scripts. HoduCC call and contact center software is engineered to enhance agents’ performance.