Instructions to use bumblebee-testing/tiny-random-LlamaModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bumblebee-testing/tiny-random-LlamaModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bumblebee-testing/tiny-random-LlamaModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bumblebee-testing/tiny-random-LlamaModel") model = AutoModel.from_pretrained("bumblebee-testing/tiny-random-LlamaModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from bumblebee-testing/tiny-random-LlamaModel: direct link, hf CLI and curl.
- Browser
- Download file 195 kB
-
https://huggingface.co/bumblebee-testing/tiny-random-LlamaModel/resolve/main/model.safetensors
- Command line
-
hf download hf://bumblebee-testing/tiny-random-LlamaModel/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/bumblebee-testing/tiny-random-LlamaModel/resolve/main/model.safetensors
195 kB
- Xet hash:
- 359c52ffaab5f03a92c47421038f9a40b930f2e23962106d86a550792300418c
- Size of remote file:
- 195 kB
- SHA256:
- 45f8e536042c5c9a25b5f02b8b264df5a45bcbb7d75c7ec61d96f9d901791901
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.