Meta-Llama-3.1-405B-Instruct-FP8
Maintainer: meta-llama
152
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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-FP8 is a large language model (LLM) developed by Meta. It is part of the Meta Llama 3.1 collection of multilingual LLMs, which includes models in 8B, 70B, and 405B sizes. The Llama 3.1 instruction-tuned text-only models are optimized for multilingual dialogue use cases and outperform many available open-source and closed-chat models on common industry benchmarks.
The Llama 3.1 models use an auto-regressive architecture with supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. The 405B version is the largest model in the Llama 3.1 family and supports 8 languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
According to the provided information, the Meta-Llama-3.1-405B-Instruct and Meta-Llama-3.1-8B are similar models in the Llama 3.1 collection, with the former being a larger instruction-tuned model and the latter a smaller base model.
Model inputs and outputs
Inputs
- Multilingual text
Outputs
- Multilingual text and code
Capabilities
The Meta-Llama-3.1-405B-Instruct-FP8 model is capable of generating high-quality multilingual text and code, with strong performance on a variety of benchmarks covering general language understanding, reasoning, coding, and math tasks. It outperforms many other available models on these metrics, particularly in the instruction-tuned versions.
What can I use it for?
The Llama 3.1 model collection is intended for commercial and research use in multiple languages. The instruction-tuned text-only models are well-suited for assistant-like chat applications, while the pretrained models can be adapted for a variety of natural language generation tasks. The models also support the ability to leverage their outputs to improve other models, such as through synthetic data generation and distillation.
Things to try
Developers can explore using the Meta-Llama-3.1-405B-Instruct-FP8 model for multilingual dialogue and language generation tasks, taking advantage of its strong performance on benchmarks. It may also be interesting to investigate how the model's outputs can be used to enhance other natural language processing systems through techniques like data augmentation and model distillation.
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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