Meta-Llama-3.1-405B
Maintainer: meta-llama
734
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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 is a large language model (LLM) developed by Meta as part of the Meta Llama 3.1 collection of multilingual LLMs. The Llama 3.1 collection includes models in 8B, 70B, and 405B sizes, all of which are optimized for multilingual dialogue use cases and outperform many available open-source and closed chat models on common industry benchmarks. The 405B version is the largest in the Llama 3.1 family.
Llama 3.1 models are built using an optimized transformer architecture and are trained on a new mix of publicly available online data. The tuned versions, including the Meta-Llama-3.1-405B, utilize supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align the models with human preferences for helpfulness and safety.
Similar models in the Llama 3.1 collection include the Meta-Llama-3.1-8B and Meta-Llama-3.1-405B-Instruct, which offer different parameter sizes and tuning approaches.
Model inputs and outputs
Inputs
- Multilingual Text: The
Meta-Llama-3.1-405Bmodel can accept text input in 8 supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
Outputs
- Multilingual Text and Code: The model can generate text and code output in the same 8 supported languages.
- The model has a context length of 128k tokens.
Capabilities
The Meta-Llama-3.1-405B model is capable of a wide range of natural language processing tasks, including dialogue, text generation, and code generation. It outperforms many industry benchmarks, demonstrating strong performance in areas like multitask learning, reading comprehension, and reasoning.
What can I use it for?
The Meta-Llama-3.1-405B model is intended for commercial and research use cases that require multilingual language understanding and generation capabilities. Some potential applications include:
- Building multilingual chatbots and virtual assistants
- Generating content in multiple languages for marketing, education, or other domains
- Enabling cross-lingual information retrieval and translation
- Developing multilingual natural language interfaces for software applications
The Llama 3.1 Community License allows for these use cases and more.
Things to try
One interesting aspect of the Meta-Llama-3.1-405B model is its ability to handle longer context lengths of up to 128k tokens. This can be useful for applications that require understanding and generating coherent text over extended passages, such as summarization, dialogue, or creative writing. Developers may want to experiment with leveraging this extended context to see how it impacts the model's performance on their specific use cases.
Additionally, the multilingual capabilities of the Llama 3.1 models present opportunities to explore cross-lingual knowledge transfer and zero-shot learning. Developers could try fine-tuning the Meta-Llama-3.1-405B on tasks in one language and evaluating its performance on related tasks in other supported languages, or using the model for multilingual information retrieval and question answering.
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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