Instructions to use lleticiasilvaa/CodeS-3B-text2SQL-alias-indentacao with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lleticiasilvaa/CodeS-3B-text2SQL-alias-indentacao with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lleticiasilvaa/CodeS-3B-text2SQL-alias-indentacao", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 0.9335978527249388, | |
| "eval_steps": 250, | |
| "global_step": 1000, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.2333994631812347, | |
| "grad_norm": 6.351889133453369, | |
| "learning_rate": 8.987402315498223e-05, | |
| "loss": 0.3368, | |
| "step": 250 | |
| }, | |
| { | |
| "epoch": 0.2333994631812347, | |
| "eval_loss": 0.18190699815750122, | |
| "eval_runtime": 40.5235, | |
| "eval_samples_per_second": 2.147, | |
| "eval_steps_per_second": 2.147, | |
| "step": 250 | |
| }, | |
| { | |
| "epoch": 0.4667989263624694, | |
| "grad_norm": 3.1040618419647217, | |
| "learning_rate": 5.828471682626175e-05, | |
| "loss": 0.1395, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 0.4667989263624694, | |
| "eval_loss": 0.12028516829013824, | |
| "eval_runtime": 40.8355, | |
| "eval_samples_per_second": 2.13, | |
| "eval_steps_per_second": 2.13, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 0.7001983895437041, | |
| "grad_norm": 2.8537700176239014, | |
| "learning_rate": 2.2174321662025427e-05, | |
| "loss": 0.1029, | |
| "step": 750 | |
| }, | |
| { | |
| "epoch": 0.7001983895437041, | |
| "eval_loss": 0.10648668557405472, | |
| "eval_runtime": 40.4951, | |
| "eval_samples_per_second": 2.148, | |
| "eval_steps_per_second": 2.148, | |
| "step": 750 | |
| }, | |
| { | |
| "epoch": 0.9335978527249388, | |
| "grad_norm": 2.1876468658447266, | |
| "learning_rate": 1.2487983905362933e-06, | |
| "loss": 0.0892, | |
| "step": 1000 | |
| }, | |
| { | |
| "epoch": 0.9335978527249388, | |
| "eval_loss": 0.10130416601896286, | |
| "eval_runtime": 40.5522, | |
| "eval_samples_per_second": 2.145, | |
| "eval_steps_per_second": 2.145, | |
| "step": 1000 | |
| } | |
| ], | |
| "logging_steps": 250, | |
| "max_steps": 1071, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 1, | |
| "save_steps": 250, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 7.525263805128192e+16, | |
| "train_batch_size": 1, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |