Instructions to use bgsach/WizardCoder-Python-7B-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-7B-V1.0-ct2-float16 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bgsach/WizardCoder-Python-7B-V1.0-ct2-float16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from bgsach/WizardCoder-Python-7B-V1.0-ct2-float16: direct link, hf CLI and curl.
- Browser
- Download file 735 Bytes
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https://huggingface.co/bgsach/WizardCoder-Python-7B-V1.0-ct2-float16/resolve/main/README.md
- Command line
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hf download hf://bgsach/WizardCoder-Python-7B-V1.0-ct2-float16/README.md
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curl -L -o README.md https://huggingface.co/bgsach/WizardCoder-Python-7B-V1.0-ct2-float16/resolve/main/README.md
735 Bytes
metadata
license: llama2
tags:
- code
This is a quantized version of WizardLM/WizardCoder-Python-7B-V1.0, quantized using ctranslate2 (see inference instructions there).
The license/caveats/intended usage is the same as the original model.
The quality of its output may have
been negatively affected by the quantization process.
The command run to quantize the model was:
ct2-transformers-converter --model ./models-hf/WizardLM/WizardCoder-Python-7B-V1.0 --quantization float16 --output_dir ./models-ct/WizardLM/WizardCoder-Python-7B-V1.0-ct2-float16
The quantization was run on a 'high-mem', CPU only (8 core, 51GB) colab instance and took approximately 10 minutes.