Text Classification
Transformers
Safetensors
modernbert
agent-safety
tool-calling
long-context
distillation
Eval Results (legacy)
text-embeddings-inference
Instructions to use ProCreations/auto-200m-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProCreations/auto-200m-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ProCreations/auto-200m-2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ProCreations/auto-200m-2") model = AutoModelForSequenceClassification.from_pretrained("ProCreations/auto-200m-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 880 Bytes
0bbb929 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | """Unattended order: download -> prepare -> RoPE sweep/init -> smoke -> reference reproduction -> LR pilots -> p1 -> p2
-> validation-only selection -> single final evaluation. Every step is idempotent, so a rerun resumes."""
import subprocess, sys
from common import *
STEPS = [['download.py'], ['prepare.py'], ['rope.py'], ['smoke.py'], ['evaluate.py', 'reference'], ['train.py', 'pilots'],
['train.py', 'p1_short'], ['train.py', 'p2_long'], ['choose.py'], ['evaluate.py']]
def main():
stop_after = sys.argv[1] if len(sys.argv) > 1 else None
for step in STEPS:
event('step_started', step=' '.join(step))
subprocess.run([sys.executable, str(ROOT/step[0]), *step[1:]], check=True, cwd=str(ROOT))
event('step_complete', step=' '.join(step))
if stop_after and ' '.join(step) == stop_after: break
if __name__ == '__main__': main()
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