Instructions to use micymike/llama-3.2-3b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use micymike/llama-3.2-3b-instruct with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("micymike/llama-3.2-3b-instruct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ba1d38320e3b10ccbff80e8f6dc5cdbea1e748cb5dcfdb95b28e88b2ca0b539
- Size of remote file:
- 17.2 MB
- SHA256:
- 46f59a9ba4a19e74a67aff6cbc5414ad8691ac1411a2340350c34646a3326556
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.