Llama-2-7b-hf

Maintainer: meta-llama

Total Score

1.4K

Last updated 4/28/2024

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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-2-7b-hf is a 7 billion parameter generative language model developed and released by Meta. It is part of the Llama 2 family of models, which range in size from 7 billion to 70 billion parameters. The Llama 2 models are trained on a new mix of publicly available online data and use an optimized transformer architecture. The tuned versions, called Llama-2-Chat, are further fine-tuned using supervised fine-tuning and reinforcement learning with human feedback to optimize for helpfulness and safety. These models are intended to outperform open-source chat models on many benchmarks.

The Llama-2-70b-chat-hf model is a 70 billion parameter version of the Llama 2 family that is fine-tuned specifically for dialogue use cases, also developed and released by Meta. Both the 7B and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.

Model inputs and outputs

Inputs

  • Text prompts

Outputs

  • Generated text continuations

Capabilities

Llama-2-7b-hf is a powerful generative language model capable of producing high-quality text on a wide range of topics. It can be used for tasks like summarization, language translation, question answering, and creative writing. The fine-tuned Llama-2-Chat models are particularly adept at engaging in open-ended dialogue and assisting with task completion.

What can I use it for?

Llama-2-7b-hf and the other Llama 2 models can be used for a variety of commercial and research applications, including chatbots, content generation, language understanding, and more. The Llama-2-Chat models are well-suited for building assistant-like applications that require helpful and safe responses.

To get started, you can fine-tune the models on your own data or use them directly for inference. Meta provides a custom commercial license for the Llama 2 models, which you can access by visiting the website and agreeing to the terms.

Things to try

One interesting aspect of the Llama 2 models is their ability to scale in size while maintaining strong performance. The 70 billion parameter version of the model significantly outperforms the 7 billion version on many benchmarks, highlighting the value of large language models. Developers could experiment with using different sized Llama 2 models for their specific use cases to find the right balance of performance and resource requirements.

Another avenue to explore is the safety and helpfulness of the Llama-2-Chat models. The developers have put a strong emphasis on aligning these models to human preferences, and it would be interesting to see how they perform in real-world applications that require reliable and trustworthy responses.



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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