Instructions to use thanhdath/FINER-SQL-3B-CoRE-R2-RL3000-runpod-20261023 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thanhdath/FINER-SQL-3B-CoRE-R2-RL3000-runpod-20261023 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("thanhdath/FINER-SQL-3B-CoRE-R2-RL3000-runpod-20261023", device_map="auto") - Notebooks
- Google Colab
- Kaggle
CoRE3B recovery checkpoints
Public resumable backups for one user-authorized3000-step run, not an accuracy-validated release. Initialization: released FINER-SQL-3B-BIRD. Fixed9687 sampled training examples (5100BIRD+4587Spider), all215 CoRE DBs, beta0/reward5/memory+atomic enabled. Fresh optimizer and memory; no matched ORI3B control.
Every500 steps (plus early recovery step20): model shards, optimizer, scheduler, RNG, trainer state, drained step-aligned memory. Restore checkpoint and sibling memory together. Full1534BIRD/1034Spider greedy evaluation at2000/3000. These are separate from the0.5B k30+VAV comparison.
Input limitations are recorded in RUN_SPEC.json:550 rows have one CoRE state;29 rows have all-NULL gold on selected CoRE states. No row is silently dropped and no missing DB falls back to ORI. Generic FROM parsing is repaired; Spider reward executes the SQL exactly as supplied, with no DISTINCT rewriting.
Model tree for thanhdath/FINER-SQL-3B-CoRE-R2-RL3000-runpod-20261023
Base model
griffith-bigdata/FINER-SQL-3B-BIRD