---
license: apache-2.0
language:
- en
base_model: PowerInfer/Bamboo-base-v0_1
library_name: transformers
pipeline_tag: feature-extraction
---

# Bamboo-base-v0.1-GGUF

- Model creator: [PowerInfer](https://huggingface.co/PowerInfer)
- Original model: [Bamboo base v0.1](https://huggingface.co/PowerInfer/Bamboo-base-v0_1) 

## Description

Sparse computing is increasingly recognized as an important direction to improve the computational efficiency (e.g., inference speed) of large language models (LLM).

Recent studies ([Zhang el al., 2021](https://arxiv.org/abs/2110.01786); [Liu et al., 2023](https://openreview.net/pdf?id=wIPIhHd00i); [Mirzadeh et al., 2023](https://arxiv.org/abs/2310.04564)) reveal that LLMs inherently exhibit properties conducive to sparse computation when employing the ReLU activation function.
This insight opens up new avenues for inference speed, akin to MoE's selective activation.
By dynamically choosing model parameters for computation, we can substantially boost inference speed.

However, the widespread adoption of ReLU-based models in the LLM field remains limited.
Here we introduce a new 7B ReLU-based LLM, Bamboo (Github link: [https://github.com/SJTU-IPADS/Bamboo](https://github.com/SJTU-IPADS/Bamboo)),
which boasts nearly 85% sparsity and performance levels on par with [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1).

## Citation

Please kindly cite using the following BibTeX:

```
@misc{bamboo,
    title={Bamboo: Harmonizing Sparsity and Performance in Large Language Models}, 
    author={Yixin Song, Haotong Xie, Zeyu Mi, Li Ma, Haibo Chen},
    year={2024}
}
```