Instructions to use bigcode/santacoder-fast-inference with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigcode/santacoder-fast-inference with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="bigcode/santacoder-fast-inference")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("bigcode/santacoder-fast-inference") model = AutoModelWithLMHead.from_pretrained("bigcode/santacoder-fast-inference", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from bigcode/santacoder-fast-inference: direct link, hf CLI and curl.
- Browser
- Download file 2.25 GB
-
https://huggingface.co/bigcode/santacoder-fast-inference/resolve/d328d4d74f941d64da69fe0e4aa561082875dfca/pytorch_model.bin
- Command line
-
hf download hf://bigcode/santacoder-fast-inference@d328d4d74f941d64da69fe0e4aa561082875dfca/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bigcode/santacoder-fast-inference/resolve/d328d4d74f941d64da69fe0e4aa561082875dfca/pytorch_model.bin
2.25 GB
- Xet hash:
- 4f93f727d5631b02c4a4cfd7714e24a86541b0d4df601dddcda8621237373469
- Size of remote file:
- 2.25 GB
- SHA256:
- 90379993a9bff7bbd8749be2a911ead7c410459d616e81b5dc3054bf9045b156
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