Llama-3.1-405B
Maintainer: meta-llama
816
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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 is a large language model (LLM) developed by Meta. It is part of the Meta Llama 3.1 collection, which includes models in 8B, 70B and 405B sizes. The Llama 3.1 models are optimized for multilingual dialogue use cases and outperform many open source and closed chat models on common industry benchmarks. The Llama-3.1-405B model specifically uses an optimized transformer architecture and has been trained on a new mix of publicly available online data.
Model inputs and outputs
Inputs
- Multilingual Text: The model supports input in English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
- Multilingual Text and code: The model can process both text and code input.
Outputs
- Multilingual Text: The model can generate multilingual text outputs in the same languages as the inputs.
- Multilingual Text and code: The model can generate both text and code outputs.
Capabilities
The Llama-3.1-405B model has demonstrated strong performance across a variety of tasks, including general language understanding, knowledge reasoning, reading comprehension, and more. It outperforms earlier versions of the Llama model as well as many other large language models on standard benchmarks.
What can I use it for?
The Llama-3.1-405B model is intended for commercial and research use in multiple languages. The instruction tuned text-only models are optimized for assistant-like chat, while the pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the model outputs to improve other models, such as through synthetic data generation and distillation.
Things to try
Developers can experiment with the Llama-3.1-405B model to build multilingual chatbots, language generation tools, and more. The model's strong performance on benchmarks suggests it could be a powerful foundation for a wide range of natural language processing applications. Be sure to refer to the Responsible Use Guide and other resources when deploying the model to ensure safe and responsible development.
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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