Instructions to use Evryvilanislemn/laya-decider-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Evryvilanislemn/laya-decider-v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Evryvilanislemn/laya-decider-v2", device_map="auto") - Laya
How to use Evryvilanislemn/laya-decider-v2 with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Laya coding decider v2 (SWE-Gym fine-tune)
Fine-tuned from convaiinnovations/laya on ~9k coding-decision rows derived from
SWE-Gym/SWE-Gym (real GitHub issues + gold fix diffs): keep/drop file relevance,
priority score, injection noul.
v2 held-out results (1150 rows)
- Overall accuracy: 0.833 | ECE: 0.147 | Brier: 0.086
- Relevance-only accuracy: 0.907 on 1150 items (majority-drop baseline ~0.64)
- Score MAE: 0.411 | p50 latency: 126.4 ms
Usage
import laya
agent = laya.load("evryvilanislemn/laya-decider-v2")
result = agent.predict(state, questions)
print(result["answers"])
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