Llama-2-13b-hf
Maintainer: NousResearch
69
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| Property | Value |
|---|---|
| Model Link | View 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
Llama-2-13b-hf is a large language model developed by Meta (NousResearch) that is part of the Llama 2 family of models. Llama 2 models range in size from 7 billion to 70 billion parameters, with this 13B variant being one of the mid-sized options. The Llama 2 models are trained on a mix of publicly available online data and fine-tuned using both supervised learning and reinforcement learning with human feedback to optimize for helpfulness and safety. According to the maintainer, the Llama-2-13b-chat-hf and Llama-2-70b-chat-hf versions are further optimized for dialogue use cases and outperform open-source chat models on many benchmarks.
Model inputs and outputs
Inputs
- The
Llama-2-13b-hfmodel takes text inputs only.
Outputs
- The model generates text outputs only.
Capabilities
The Llama-2-13b-hf model is a powerful generative language model that can be used for a variety of natural language processing tasks, such as text generation, summarization, question answering, and language translation. Its large size and strong performance on academic benchmarks suggest it has broad capabilities across many domains.
What can I use it for?
The Llama-2-13b-hf model is intended for commercial and research use in English. The maintainer notes that the fine-tuned chat versions like Llama-2-13b-chat-hf and Llama-2-70b-chat-hf are optimized for assistant-like dialogue use cases and may be particularly well-suited for building conversational AI applications. The pretrained versions can also be adapted for a variety of natural language generation tasks.
Things to try
One interesting aspect of the Llama-2-13b-hf model is its use of the Grouped-Query Attention (GQA) mechanism for the larger 70B variant. This technique is designed to improve the scalability and efficiency of the model during inference, which could make it particularly well-suited for real-world applications with high computational demands. Experimenting with the different Llama 2 model sizes and architectures could yield valuable insights into balancing performance, efficiency, and resource requirements for 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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