Llama-3.1-70B
Maintainer: meta-llama
273
🗣️
| 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-3.1-70B is part of the Meta Llama 3.1 collection of multilingual large language models (LLMs) developed by meta-llama. This 70 billion parameter model is optimized for multilingual dialogue use cases and outperforms many available open source and closed chat models on common industry benchmarks. It uses a transformer architecture with supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align the model with human preferences for helpfulness and safety. Similar models in the Llama 3.1 family include the Meta-Llama-3.1-8B and Meta-Llama-3.1-405B.
Model inputs and outputs
The Llama-3.1-70B model takes multilingual text as input and can generate multilingual text and code as output. It has a context length of 128k tokens and uses Grouped-Query Attention (GQA) for improved inference scalability. The model was pretrained on around 15 trillion tokens of data from publicly available sources, with a cutoff date of December 2023.
Inputs
- Multilingual text
Outputs
- Multilingual text
- Multilingual code
Capabilities
The Llama-3.1-70B model excels at a variety of natural language processing tasks, including general question answering, commonsense reasoning, reading comprehension, and code generation. It outperforms many other large language models on benchmarks like MMLU, ARC-Challenge, and GSM-8K.
What can I use it for?
The Llama-3.1-70B 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 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, such as through synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases.
Things to try
With its multilingual capabilities and strong performance on benchmarks, the Llama-3.1-70B model could be a powerful tool for developers working on language-based applications that need to support multiple languages. Try fine-tuning the model on your own datasets or using it as a starting point for building more specialized models tailored to your specific use case.
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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