Instructions to use CodCodingCode/llama-3.1-8b-GRPO-V2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CodCodingCode/llama-3.1-8b-GRPO-V2.0 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CodCodingCode/llama-3.1-8b-GRPO-V2.0", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f63e44f1f83a245e15a8e6e2417a8febc5135a3cc06eaf39bbea809d7d8c3ba
- Size of remote file:
- 17.2 MB
- SHA256:
- ddbace6205bfa81607d177aa4ae2364c99412a5b40fd400935cbd423cb3138d2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.