llama2-22b
Maintainer: chargoddard
46
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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 llama2-22b model is a large language model developed by Meta's researchers and released by the creator chargoddard. It is a version of Llama 2 with some additional attention heads from the original 33B Llama model. The model has been fine-tuned on around 10 million tokens from the RedPajama dataset to help the added components settle in. This model is not intended for use as-is, but rather to serve as a base for further tuning and adaptation, with the goal of providing greater capacity for learning than the 13B Llama 2 model.
The llama2-22b model is similar to other models in the Llama 2 family, such as the Llama-2-13b-hf and Llama-2-13b-chat-hf models, which range in size from 7 billion to 70 billion parameters. These models were developed and released by Meta's AI research team.
Model inputs and outputs
Inputs
- The
llama2-22bmodel takes in text as its input.
Outputs
- The model generates text as its output.
Capabilities
The llama2-22b model has been evaluated on various academic benchmarks, including commonsense reasoning, world knowledge, reading comprehension, and math. The model performs well on these tasks, with the 70B version achieving the best results among the Llama 2 models. The model also exhibits good performance on safety metrics, such as truthfulness and low toxicity, especially in the fine-tuned Llama-2-Chat versions.
What can I use it for?
The llama2-22b model is intended for commercial and research use in English. While the fine-tuned Llama-2-Chat models are optimized for assistant-like dialogue, the pretrained llama2-22b model can be adapted for a variety of natural language generation tasks, such as text summarization, language translation, and content creation. However, developers should perform thorough safety testing and tuning before deploying any applications of the model, as the potential outputs cannot be fully predicted.
Things to try
One interesting aspect of the llama2-22b model is its use of additional attention heads from the original 33B Llama model. This architectural change may allow the model to better capture certain linguistic patterns or relationships, potentially leading to improved performance on specific tasks. Researchers and developers could explore fine-tuning the model on domain-specific datasets or incorporating it into novel application architectures to unlock its full potential.
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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