Instructions to use HaadesX/Iconoclast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HaadesX/Iconoclast with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("HaadesX/Iconoclast", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,097 Bytes
3236af9 | 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 29 30 31 32 33 | import sys
import unittest
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
from iconoclast.config import WarmStartTrial
class ConfigTests(unittest.TestCase):
def test_warm_start_trial_accepts_mixed_optuna_param_types(self):
trial = WarmStartTrial.model_validate(
{
"description": "Strong per-layer variance anchor",
"params": {
"direction_scope": "per layer",
"direction_method": "variance",
"direction_blend": 0.42,
"attn.o_proj.max_weight": 1.34,
},
}
)
self.assertEqual(trial.description, "Strong per-layer variance anchor")
self.assertEqual(trial.params["direction_scope"], "per layer")
self.assertEqual(trial.params["direction_method"], "variance")
self.assertAlmostEqual(trial.params["direction_blend"], 0.42)
self.assertAlmostEqual(trial.params["attn.o_proj.max_weight"], 1.34)
if __name__ == "__main__":
unittest.main()
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