Instructions to use shuvankar77/sqlcoder2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shuvankar77/sqlcoder2.0 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("defog/sqlcoder-7b-2") model = PeftModel.from_pretrained(base_model, "shuvankar77/sqlcoder2.0") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-18/trainer_state.json from shuvankar77/sqlcoder2.0: direct link, hf CLI and curl.
- Browser
- Download file 938 Bytes
-
https://huggingface.co/shuvankar77/sqlcoder2.0/resolve/main/checkpoint-18/trainer_state.json
- Command line
-
hf download hf://shuvankar77/sqlcoder2.0/checkpoint-18/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/shuvankar77/sqlcoder2.0/resolve/main/checkpoint-18/trainer_state.json
938 Bytes
| { | |
| "best_global_step": null, | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 3.0, | |
| "eval_steps": 500, | |
| "global_step": 18, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.6666666666666665, | |
| "grad_norm": 20.203990936279297, | |
| "learning_rate": 3.888888888888889e-05, | |
| "loss": 4.2917, | |
| "step": 10 | |
| } | |
| ], | |
| "logging_steps": 10, | |
| "max_steps": 18, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 3, | |
| "save_steps": 100, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": true | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 670257911955456.0, | |
| "train_batch_size": 2, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |