Instructions to use hyper-accel/tiny-random-internlm2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hyper-accel/tiny-random-internlm2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hyper-accel/tiny-random-internlm2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hyper-accel/tiny-random-internlm2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hyper-accel/tiny-random-internlm2: direct link, hf CLI and curl.
- Browser
- Download file 867 MB
-
https://huggingface.co/hyper-accel/tiny-random-internlm2/resolve/main/model.safetensors
- Command line
-
hf download hf://hyper-accel/tiny-random-internlm2/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hyper-accel/tiny-random-internlm2/resolve/main/model.safetensors
867 MB
- Xet hash:
- e05bae4729ed19ce27bd51e87e121e23ab9d0b504cb022f3665750c62e84c1cd
- Size of remote file:
- 867 MB
- SHA256:
- 120e119166360c7c8237587c06e60439a5efc741a5e2f44ea16f0ae561c1ec13
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.