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
File size: 757 Bytes
2f44e25 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"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
}
} |