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%2F43/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-Starcoder2Model@refs/pr/43/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-Starcoder2Model/resolve/refs%2Fpr%2F43/model.safetensors
180 kB
- Xet hash:
- 4973d580922dbaf8e400beaafa27f6c8f2ce77e90f4100f4d9a8bf72ee94910b
- Size of remote file:
- 180 kB
- SHA256:
- d5f8bd7913e92ea7bc34473fc4361e34e9dbf0db436ac328d73501a062e05dea
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.