Llama 3.1 Nemotron Ultra 253B
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Llama 3.1 Nemotron Ultra 253B
Overview :
Llama-3.1-Nemotron-Ultra-253B-v1 is a large language model based on Llama-3.1-405B-Instruct, which has undergone multi-stage post-training to enhance reasoning and chat capabilities. This model supports context lengths up to 128K, offering a good balance between accuracy and efficiency. Suitable for commercial use, it aims to provide developers with powerful AI assistant functionality.
Target Users :
This product is suitable for developers building AI agent systems, chatbots, and other AI applications, especially in scenarios requiring efficient reasoning and human-computer interaction. Its superior performance and large context processing capabilities make it excel in complex tasks.
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Use Cases
Used to build intelligent customer service systems, providing real-time question answering.
Used in education to help students answer math and programming questions.
Used in content creation to assist in generating creative writing and technical documentation.
Features
Efficient Reasoning: Provides faster inference speeds through improved neural architecture search.
Multilingual Support: Supports not only English but also German, French, and other languages.
Large Context Support: Can handle input text up to 128K in length.
Wide Applicability: Can be used for AI agent systems, chatbots, and RAG systems, etc.
Well-Trained: Has excellent instruction-following capabilities through supervised fine-tuning and reinforcement learning optimization.
Strong Compatibility: Compatible with NVIDIA Hopper and Ampere microarchitectures, suitable for various hardware environments.
Open Source: Complies with the NVIDIA open model license for convenient developer use.
How to Use
Access the model page and download the relevant files.
Install necessary dependency libraries, such as transformers.
Load the model and configure inference parameters, such as temperature and maximum output length.
Input the text to be processed and call the model for inference.
Obtain the model output and perform post-processing as needed.
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