Instructions to use hf-internal-testing/tiny-random-Starcoder2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Starcoder2Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-Starcoder2Model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-Starcoder2Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-Starcoder2Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-Starcoder2Model: direct link, hf CLI and curl.
- Browser
- Download file 180 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-Starcoder2Model/resolve/refs%2Fpr%2F2/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-Starcoder2Model@refs/pr/2/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-Starcoder2Model/resolve/refs%2Fpr%2F2/model.safetensors
180 kB
- Xet hash:
- ef1d18d0f9d9e7cc9c28bdb234e1b494843acdcef4c5d04d2fa33a8bc3fca2dd
- Size of remote file:
- 180 kB
- SHA256:
- 5ff1317f5a4cdff6b004475d8f55c1b9a04fbe7f7f33bd954c10f1726692f34d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.