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
Upload tokenizer
Browse files
tokenization_mamba_fast.py
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from transformers import GPTNeoXTokenizerFast
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class MambaTokenizerFast(GPTNeoXTokenizerFast):
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def __init__(
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self,
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from transformers import GPTNeoXTokenizerFast
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class MambaTokenizerFast(GPTNeoXTokenizerFast):
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def __init__(
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self,
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