Instructions to use Wilderss/QuantumedgeAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use Wilderss/QuantumedgeAI with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("<base-model-id>") model.load_adapter("Wilderss/QuantumedgeAI", set_active=True) - Notebooks
- Google Colab
- Kaggle
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Download README.md from Wilderss/QuantumedgeAI: direct link, hf CLI and curl.
- Browser
- Download file 745 Bytes
-
https://huggingface.co/Wilderss/QuantumedgeAI/resolve/main/README.md
- Command line
-
hf download hf://Wilderss/QuantumedgeAI/README.md
-
curl -L -o README.md https://huggingface.co/Wilderss/QuantumedgeAI/resolve/main/README.md
745 Bytes
metadata
license: mit
datasets:
- openai/MMMLU
- argilla/FinePersonas-v0.1
- SkunkworksAI/reasoning-0.01
- fka/awesome-chatgpt-prompts
metrics:
- accuracy
- bleu
- bertscore
- bleurt
base_model:
- meta-llama/Llama-3.2-11B-Vision-Instruct
- nvidia/NVLM-D-72B
- openai/whisper-large-v3-turbo
new_version: meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: question-answering
library_name: adapter-transformers
from transformers import
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("Wilderss/QuantumedgeAI", set_active=True)
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("Wilderss/QuantumedgeAI", set_active=True)