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%2F6/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-Starcoder2Model@refs/pr/6/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-Starcoder2Model/resolve/refs%2Fpr%2F6/model.safetensors
180 kB
- Xet hash:
- 8b9f77ec51e6f12a3b3277a7f64e748946701f8148f56edca8c0cfcfd090ce62
- Size of remote file:
- 180 kB
- SHA256:
- 9bdb1343289c805a36dc287fa9ac4922cce6af1c9b8c432330c4a4ff153bcd29
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.