Text Classification
PEFT
Safetensors
English
lora
complexity-classification
llm-routing
query-difficulty
brick
semantic-router
inference-optimization
cost-reduction
reasoning-budget
Instructions to use regolo/brick-complexity-2-eco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use regolo/brick-complexity-2-eco 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-2-eco") - Notebooks
- Google Colab
- Kaggle
| { | |
| "config": { | |
| "base": "Qwen/Qwen3.5-0.8B", | |
| "train": "/data/dataset/empirical_train.jsonl", | |
| "val": "/data/dataset/empirical_val.jsonl", | |
| "test": "/data/dataset/empirical_test.jsonl", | |
| "output": "/data/output/qwen35-empirical-asym-lora", | |
| "epochs": 3, | |
| "batch_size": 16, | |
| "lr": "1e-4", | |
| "max_length": 768, | |
| "lora_r": 32, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.1, | |
| "over_lambda": 0.7, | |
| "label_smoothing": 0.08, | |
| "eval_steps": 200 | |
| }, | |
| "training_time_s": 2338.5, | |
| "system_prompt": "PRODUCTION (for Brick drop-in compatibility)", | |
| "test": { | |
| "n": 1994, | |
| "accuracy": 0.7276830491474423, | |
| "over_rate": 0.07773319959879639, | |
| "under_rate": 0.1945837512537613, | |
| "macro_f1": 0.42460581599084596 | |
| } | |
| } |