Instructions to use bgsach/WizardCoder-Python-13B-V1.0-ct2-float16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bgsach/WizardCoder-Python-13B-V1.0-ct2-float16 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bgsach/WizardCoder-Python-13B-V1.0-ct2-float16", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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The command run to quantize the model was:
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`ct2-transformers-converter --model ./models-hf/WizardLM/WizardCoder-Python-13B-V1.0 --quantization
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The quantization was run on a 'high-mem', CPU only (8 core, 51GB) colab instance and took approximately 10 minutes.
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The command run to quantize the model was:
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`ct2-transformers-converter --model ./models-hf/WizardLM/WizardCoder-Python-13B-V1.0 --quantization float16 --output_dir ./models-ct/WizardLM/WizardCoder-Python-13B-V1.0-ct2-float16`
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The quantization was run on a 'high-mem', CPU only (8 core, 51GB) colab instance and took approximately 10 minutes.
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