Instructions to use Ananthusajeev190/Experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use Ananthusajeev190/Experimental with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("<base-model-id>") model.load_adapter("Ananthusajeev190/Experimental", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download memory.py from Ananthusajeev190/Experimental: direct link, hf CLI and curl.
- Browser
- Download file 483 Bytes
-
https://huggingface.co/Ananthusajeev190/Experimental/resolve/main/memory.py
- Command line
-
hf download hf://Ananthusajeev190/Experimental/memory.py
-
curl -L -o memory.py https://huggingface.co/Ananthusajeev190/Experimental/resolve/main/memory.py
483 Bytes
| from data.frigate_store import FRIGATE_MEMORY | |
| class Memory: | |
| def __init__(self): | |
| self.knowledge = {} | |
| for k, v in FRIGATE_MEMORY.items(): | |
| self.learn(k, v[:500]) # pre-load knowledge | |
| def retrieve(self, query): | |
| hits = [] | |
| for k, v in self.knowledge.items(): | |
| if query.lower() in v.lower(): | |
| hits.append(v[:300]) | |
| return hits[:5] | |
| def learn(self, key, value): | |
| self.knowledge[key] = value | |