Instructions to use inference-optimization/Kimi-K3-0.40B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inference-optimization/Kimi-K3-0.40B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="inference-optimization/Kimi-K3-0.40B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("inference-optimization/Kimi-K3-0.40B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 96064685fc1b91b8dd94f2921e128d512fc3126e783520cfd8a8f5db5af99343
- Size of remote file:
- 3.11 GB
- SHA256:
- d0b981d9f535753b2f8aff744231d0ac0d4d974beaedd9f7ceea5003ba47c7b7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.