Sheared-LLaMA-2.7B
Maintainer: princeton-nlp
54
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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
Sheared-LLaMA-2.7B is a pruned and further pre-trained model derived from the meta-llama/Llama-2-7b-hf model. The model was developed by the princeton-nlp team and is available on the Hugging Face Hub. Like the original LLaMA model, Sheared-LLaMA-2.7B is a large language model based on the transformer architecture. However, this model was pruned and further trained on the RedPajama dataset using a budget of 50 billion tokens.
Model inputs and outputs
Inputs
- Text prompts
Outputs
- Continuation of the input text, generating coherent and relevant text
Capabilities
The Sheared-LLaMA-2.7B model has demonstrated strong performance across a variety of downstream tasks, including reasoning, reading comprehension, language modeling, and knowledge-intensive tasks. The model outperforms existing large language models like OPT-2.7B and Pythia-2.8B on average performance metrics.
What can I use it for?
The Sheared-LLaMA-2.7B model can be used for a wide range of natural language processing tasks, such as text generation, question answering, summarization, and content creation. Developers and researchers can fine-tune the model for specific applications or use it as a strong baseline for further research and development.
Things to try
One interesting aspect of the Sheared-LLaMA-2.7B model is that it was trained with a budget of only 50 billion tokens, which is significantly less than the 1 trillion tokens used to train the original LLaMA models. This suggests that the model's performance can be achieved with a more efficient and cost-effective training process, making it an attractive option for those with limited computational resources.
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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