Text Classification
PEFT
Safetensors
English
lora
complexity-classification
llm-routing
query-difficulty
brick
semantic-router
inference-optimization
cost-reduction
reasoning-budget
Eval Results (legacy)
Instructions to use regolo/brick-complexity-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use regolo/brick-complexity-extractor with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-0.8B") model = PeftModel.from_pretrained(base_model, "regolo/brick-complexity-extractor") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f43df1d5e7215b32a91351e6e9407413b63ca00282e17904bf8272087c8cc29a
- Size of remote file:
- 20 MB
- SHA256:
- 9facde94660f9c53e928aad19c7d6a16d91f5e42d6d581db8d253b4787ee5e19
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.