Instructions to use mjschock/mamba-130m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mjschock/mamba-130m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mjschock/mamba-130m", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mjschock/mamba-130m", trust_remote_code=True) model = AutoModel.from_pretrained("mjschock/mamba-130m", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 140337dc1f91147627893eb39f922c51c7be03fedca17a13b0b162f26aac4b13
- Size of remote file:
- 517 MB
- SHA256:
- 29a023467229397eb03fd31e088c08c9193b734249f0c07d51af03873162e723
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