Instructions to use greatakela/Llama-3.2-3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use greatakela/Llama-3.2-3B-Instruct with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("greatakela/Llama-3.2-3B-Instruct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download training_args.bin from greatakela/Llama-3.2-3B-Instruct: direct link, hf CLI and curl.
- Browser
- Download file 5.62 kB
-
https://huggingface.co/greatakela/Llama-3.2-3B-Instruct/resolve/main/training_args.bin
- Command line
-
hf download hf://greatakela/Llama-3.2-3B-Instruct/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/greatakela/Llama-3.2-3B-Instruct/resolve/main/training_args.bin
5.62 kB
- Xet hash:
- 2678df5c44d59733aac03c20a70fcb0368633cc6e99407e72584306b3450a377
- Size of remote file:
- 5.62 kB
- SHA256:
- a102da80453ef6a5f98a5a5a6290a8d1c9e6bac4353b45a32fb8f86efa8b7f69
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.