Instructions to use deep20998/sqlcoder-7b-2-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use deep20998/sqlcoder-7b-2-v1 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, "deep20998/sqlcoder-7b-2-v1") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-3/trainer_state.json from deep20998/sqlcoder-7b-2-v1: direct link, hf CLI and curl.
- Browser
- Download file 761 Bytes
-
https://huggingface.co/deep20998/sqlcoder-7b-2-v1/resolve/main/checkpoint-3/trainer_state.json
- Command line
-
hf download hf://deep20998/sqlcoder-7b-2-v1/checkpoint-3/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/deep20998/sqlcoder-7b-2-v1/resolve/main/checkpoint-3/trainer_state.json
761 Bytes
| { | |
| "best_global_step": null, | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 3.0, | |
| "eval_steps": 500, | |
| "global_step": 3, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [], | |
| "logging_steps": 10, | |
| "max_steps": 3, | |
| "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": 60932537450496.0, | |
| "train_batch_size": 2, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |