LiamCarter/icl-pruning-wanda-sparsity-0.1
LiamCarter/icl-pruning-wanda-sparsity-0.1 is a 7 billion parameter language model based on the Llama-2-7b-hf architecture. This model applies the Wanda pruning method with a sparsity of 0.1, indicating a reduced parameter count for potentially more efficient inference. It is designed for research into efficient model deployment and understanding the impact of structured sparsity on large language models.
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Model Overview
This model, LiamCarter/icl-pruning-wanda-sparsity-0.1, is a pruned version of the meta-llama/Llama-2-7b-hf base model. It utilizes the Wanda pruning method to achieve a sparsity of 0.1, meaning a significant portion of its parameters have been removed or zeroed out. This approach aims to reduce the model's size and computational requirements while maintaining performance.
Key Characteristics
- Base Model:
meta-llama/Llama-2-7b-hf(7 billion parameters) - Pruning Method: Wanda
- Sparsity Level: 0.1
- Format: Standard Hugging Face
transformers-checkpoint
Potential Use Cases
- Research into Model Compression: Ideal for studying the effects of structured pruning techniques like Wanda on large language models.
- Efficient Deployment: Exploring the trade-offs between model size, inference speed, and performance for resource-constrained environments.
- Understanding Sparsity: Analyzing how different sparsity levels impact model capabilities and knowledge retention.