Instructions to use blitznova666/FRP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use blitznova666/FRP with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("blitznova666/FRP", set_active=True) - Notebooks
- Google Colab
- Kaggle
| license: llama3.1 | |
| datasets: | |
| - HuggingFaceTB/everyday-conversations-llama3.1-2k | |
| - fka/awesome-chatgpt-prompts | |
| - NousResearch/hermes-function-calling-v1 | |
| metrics: | |
| - accuracy | |
| - code_eval | |
| base_model: | |
| - meta-llama/Meta-Llama-3.1-8B-Instruct | |
| - black-forest-labs/FLUX.1-dev | |
| - microsoft/Phi-3.5-MoE-instruct | |
| - microsoft/Phi-3.5-vision-instruct | |
| pipeline_tag: question-answering | |
| library_name: adapter-transformers | |