Llama-3.1-405B-Instruct
Maintainer: meta-llama
479
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The Llama-3.1-405B-Instruct model is part of the Meta Llama 3.1 collection of multilingual large language models (LLMs) developed by meta-llama. The Llama 3.1 models come in 8B, 70B and 405B sizes and are optimized for multilingual dialogue use cases. The 405B version is a large, instruction-tuned text-only model that has been shown to outperform many open-source and commercial chat models on common industry benchmarks.
Model inputs and outputs
Inputs
- Multilingual text input in one of the supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
- The model can also accept code as input.
Outputs
- Multilingual text output in one of the supported languages.
- The model can also generate code output.
Capabilities
The Llama-3.1-405B-Instruct model demonstrates strong performance across a wide range of tasks, including general language understanding, reasoning, coding, math, and tool use. It excels at open-ended dialogue and can be used as a powerful virtual assistant for a variety of applications.
What can I use it for?
The Llama-3.1-405B-Instruct model is intended for commercial and research use in multiple languages. The instruction-tuned version is well-suited for assistant-like chat applications, while the base pretrained model can be adapted for a variety of natural language generation tasks. Developers can also leverage the model's outputs to improve other models through techniques like synthetic data generation and distillation.
Things to try
One interesting capability of the Llama-3.1-405B-Instruct model is its strong performance on multilingual benchmarks. The model achieves high scores on the MMLU benchmark across several languages, demonstrating its ability to understand and communicate effectively in a diverse set of languages. Developers looking to build multilingual applications should consider incorporating this model into their systems.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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