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
metadata
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