Instructions to use Tiiny/Bamboo-base-v0_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tiiny/Bamboo-base-v0_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Tiiny/Bamboo-base-v0_1", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Tiiny/Bamboo-base-v0_1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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By dynamically choosing model parameters for computation, we can substantially boost inference speed.
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However, the widespread adoption of ReLU-based models in the LLM field remains limited.
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Here we introduce a new 7B ReLU-based LLM, Bamboo (Github link:[https://github.com/SJTU-IPADS/Bamboo](https://github.com/SJTU-IPADS/Bamboo)),
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which boasts nearly 85% sparsity and performance levels on par with [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1).
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## Model Architecture
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By dynamically choosing model parameters for computation, we can substantially boost inference speed.
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However, the widespread adoption of ReLU-based models in the LLM field remains limited.
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Here we introduce a new 7B ReLU-based LLM, Bamboo (Github link: [https://github.com/SJTU-IPADS/Bamboo](https://github.com/SJTU-IPADS/Bamboo)),
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which boasts nearly 85% sparsity and performance levels on par with [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1).
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## Model Architecture
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