Feature Extraction
Transformers
Safetensors
hetero_moe_projspace
mixture-of-experts
heterogeneous
dimension-projection-space
ffn-fusion
custom_code
Instructions to use redmingwei/HeteroMoEProjSpace-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use redmingwei/HeteroMoEProjSpace-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="redmingwei/HeteroMoEProjSpace-v2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("redmingwei/HeteroMoEProjSpace-v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download expected_alpha.npy from redmingwei/HeteroMoEProjSpace-v2: direct link, hf CLI and curl.
- Browser
- Download file 640 Bytes
-
https://huggingface.co/redmingwei/HeteroMoEProjSpace-v2/resolve/main/expected_alpha.npy
- Command line
-
hf download hf://redmingwei/HeteroMoEProjSpace-v2/expected_alpha.npy
-
curl -L -o expected_alpha.npy https://huggingface.co/redmingwei/HeteroMoEProjSpace-v2/resolve/main/expected_alpha.npy
640 Bytes
- Xet hash:
- cc0c94a95fad7ae7cd2a86cdc5f659860066010bb13e40e907d9c92d4215fcc1
- Size of remote file:
- 640 Bytes
- SHA256:
- 1008219287ec1585ca553aa535da300453f0dcc93a96931b8f3c80190db177e2
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