Instructions to use swadeshb/Llama-3.2-3B-Instruct-TRACE_GRPO-V15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use swadeshb/Llama-3.2-3B-Instruct-TRACE_GRPO-V15 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("swadeshb/Llama-3.2-3B-Instruct-TRACE_GRPO-V15", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 026f89d624b0f47e3899462ef505b3f4303f117a423be12adaca2edf6cba466c
- Size of remote file:
- 97.3 MB
- SHA256:
- 76006709ba30b85b9c741ca399e9fccf2daacc6ae81593fdaaba25b506f9000b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.