Llama-2-70b-chat

Maintainer: meta-llama

Total Score

387

Last updated 4/28/2024

🌿

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

Llama-2-70b-chat is a large language model developed by Meta that is part of the Llama 2 family of models. It is a 70 billion parameter model that has been fine-tuned for dialogue use cases, optimizing it for helpfulness and safety. The Llama-2-13b-chat-hf and Llama-2-7b-chat-hf are similar models that are smaller in scale but also optimized for chat. According to the maintainer's profile, the Llama 2 models are intended to outperform open-source chat models and be on par with popular closed-source models like ChatGPT and PaLM in terms of helpfulness and safety.

Model inputs and outputs

Inputs

  • Text: The Llama-2-70b-chat model takes text as input.

Outputs

  • Text: The model generates text as output.

Capabilities

The Llama-2-70b-chat model is capable of engaging in natural language conversations and assisting with a variety of tasks, such as answering questions, providing explanations, and generating text. It has been fine-tuned to optimize for helpfulness and safety, making it suitable for use in assistant-like applications.

What can I use it for?

The Llama-2-70b-chat model can be used for commercial and research purposes in English. The maintainer suggests it is well-suited for assistant-like chat applications, though the pretrained versions can also be adapted for other natural language generation tasks. Developers should carefully review the Responsible Use Guide available at https://ai.meta.com/llama/responsible-use-guide/ before deploying any applications using this model.

Things to try

Some ideas for things to try with the Llama-2-70b-chat model include:

  • Engaging it in open-ended conversations to test its dialog capabilities
  • Prompting it with a variety of tasks to assess its versatility
  • Evaluating its performance on specific benchmarks or use cases relevant to your needs
  • Exploring ways to further fine-tune or customize the model for your particular application

Remember to always review the model's limitations and ensure responsible use, as with any large language model.



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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