Instructions to use AaronTekle/SQLQwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AaronTekle/SQLQwen with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-0.5B-Instruct") model = PeftModel.from_pretrained(base_model, "AaronTekle/SQLQwen") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -141,7 +141,7 @@ Training automatically uses **BF16** where supported, falling back to FP16 on CU
|
|
| 141 |
|
| 142 |
Training was completed locally on an **NVIDIA GeForce RTX 3050**.
|
| 143 |
|
| 144 |
-
Validation loss decreased from **0.2839** at the first logged evaluation to a best value of **0.2198** at checkpoint 2,400. The final checkpoint
|
| 145 |
|
| 146 |
## Held-Out Generation Evaluation
|
| 147 |
|
|
|
|
| 141 |
|
| 142 |
Training was completed locally on an **NVIDIA GeForce RTX 3050**.
|
| 143 |
|
| 144 |
+
Validation loss decreased from **0.2839** at the first logged evaluation to a best value of **0.2198** at checkpoint 2,400. The final checkpoint shows an evaluation loss of **0.2200**, indicating that training had largely converged by the end of the second epoch.
|
| 145 |
|
| 146 |
## Held-Out Generation Evaluation
|
| 147 |
|