Meta-Llama-3.1-405B-Instruct
Maintainer: meta-llama
420
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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 Meta-Llama-3.1-405B-Instruct is a large language model developed by Meta that is part of the Meta Llama 3.1 collection of multilingual LLMs. It is an 405B parameter auto-regressive model that has been optimized for multilingual dialogue use cases through supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF). The Llama 3.1 family includes models of 8B, 70B, and 405B sizes, all supporting 8 languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
Similar models in the Llama 3.1 family include the Meta-Llama-3.1-8B-Instruct and Meta-Llama-3.1-70B-Instruct. These models share the same architectural design and training approach, but differ in parameter count and performance characteristics.
Model inputs and outputs
Inputs
- Multilingual text in the 8 supported languages
Outputs
- Multilingual text and code in the 8 supported languages
Capabilities
The Meta-Llama-3.1-405B-Instruct model excels at a variety of natural language generation tasks, particularly in multilingual dialogue scenarios. It demonstrates strong performance on benchmarks like MMLU, CommonSenseQA, and ARC-Challenge, outperforming many open-source and proprietary chat models. The model's ability to generate coherent and helpful responses in multiple languages makes it a valuable tool for building multilingual virtual assistants, translation services, and other multilingual applications.
What can I use it for?
The Meta-Llama-3.1-405B-Instruct model is well-suited for a wide range of commercial and research use cases, including:
- Multilingual chatbots and virtual assistants
- Multilingual content generation (e.g. articles, stories, product descriptions)
- Multilingual translation and language understanding services
- Multilingual code generation and programming assistance
The Llama 3.1 Community License allows for these use cases and more, providing a flexible framework for developers to leverage the model's capabilities.
Things to try
One interesting aspect of the Meta-Llama-3.1-405B-Instruct model is its ability to generate coherent responses in multiple languages. Developers could experiment with prompts that require the model to switch between languages, or that ask the model to translate between languages. Another interesting direction would be to fine-tune the model further for specific multilingual tasks, such as multilingual Q&A or multilingual code generation, to push the boundaries of its capabilities.
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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