Text Classification
PEFT
Safetensors
English
gemma4
lora
multi-label-classification
text-only
research-prototype
Instructions to use mts-the-architect/open-sensations-gemma4-e2b-bf16-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mts-the-architect/open-sensations-gemma4-e2b-bf16-lora with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("google/gemma-4-E2B") model = PeftModel.from_pretrained(base_model, "mts-the-architect/open-sensations-gemma4-e2b-bf16-lora") - Notebooks
- Google Colab
- Kaggle
Download synthetic-candidate.zip from mts-the-architect/open-sensations-gemma4-e2b-bf16-lora: direct link, hf CLI and curl.
- Browser
- Download file 10.6 MB
-
https://huggingface.co/mts-the-architect/open-sensations-gemma4-e2b-bf16-lora/resolve/main/synthetic-candidate.zip
- Command line
-
hf download hf://mts-the-architect/open-sensations-gemma4-e2b-bf16-lora/synthetic-candidate.zip
-
curl -L -o synthetic-candidate.zip https://huggingface.co/mts-the-architect/open-sensations-gemma4-e2b-bf16-lora/resolve/main/synthetic-candidate.zip
10.6 MB
- Xet hash:
- dece64c2695633d9ba3a2f142c889cf3a6cd0104f61f783b3860edc902db2363
- Size of remote file:
- 10.6 MB
- SHA256:
- 1d480582428c856f21d5c8443b6b9957d7dfd94724450534525397fec6a01812
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