Instructions to use immortalPi/stack_exc_multilabel_base_class_head with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use immortalPi/stack_exc_multilabel_base_class_head with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("google/gemma-2-2b") model = PeftModel.from_pretrained(base_model, "immortalPi/stack_exc_multilabel_base_class_head") - Transformers
How to use immortalPi/stack_exc_multilabel_base_class_head with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("immortalPi/stack_exc_multilabel_base_class_head", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from immortalPi/stack_exc_multilabel_base_class_head: direct link, hf CLI and curl.
- Browser
- Download file 2.23 kB
-
https://huggingface.co/immortalPi/stack_exc_multilabel_base_class_head/resolve/ea661aedefd5a583988a0e69dc6f2637a1d6f812/trainer_state.json
- Command line
-
hf download hf://immortalPi/stack_exc_multilabel_base_class_head@ea661aedefd5a583988a0e69dc6f2637a1d6f812/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/immortalPi/stack_exc_multilabel_base_class_head/resolve/ea661aedefd5a583988a0e69dc6f2637a1d6f812/trainer_state.json
2.23 kB
| { | |
| "best_global_step": 60, | |
| "best_metric": 0.7314814814814815, | |
| "best_model_checkpoint": "/content/gemma_lora_imb/checkpoint-60", | |
| "epoch": 1.1764705882352942, | |
| "eval_steps": 20, | |
| "global_step": 60, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.39215686274509803, | |
| "grad_norm": 33.80354309082031, | |
| "learning_rate": 8.137254901960784e-06, | |
| "loss": 1.2953, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 0.39215686274509803, | |
| "eval_accuracy": 0.645320197044335, | |
| "eval_f1": 0.6326530612244898, | |
| "eval_loss": 0.9012272357940674, | |
| "eval_runtime": 1.3299, | |
| "eval_samples_per_second": 152.646, | |
| "eval_steps_per_second": 5.264, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 0.7843137254901961, | |
| "grad_norm": 21.962677001953125, | |
| "learning_rate": 6.176470588235295e-06, | |
| "loss": 0.7861, | |
| "step": 40 | |
| }, | |
| { | |
| "epoch": 0.7843137254901961, | |
| "eval_accuracy": 0.6847290640394089, | |
| "eval_f1": 0.6923076923076923, | |
| "eval_loss": 0.764927089214325, | |
| "eval_runtime": 1.3089, | |
| "eval_samples_per_second": 155.088, | |
| "eval_steps_per_second": 5.348, | |
| "step": 40 | |
| }, | |
| { | |
| "epoch": 1.1764705882352942, | |
| "grad_norm": 39.092864990234375, | |
| "learning_rate": 4.215686274509805e-06, | |
| "loss": 0.4942, | |
| "step": 60 | |
| }, | |
| { | |
| "epoch": 1.1764705882352942, | |
| "eval_accuracy": 0.7142857142857143, | |
| "eval_f1": 0.7314814814814815, | |
| "eval_loss": 0.7433407306671143, | |
| "eval_runtime": 1.3004, | |
| "eval_samples_per_second": 156.101, | |
| "eval_steps_per_second": 5.383, | |
| "step": 60 | |
| } | |
| ], | |
| "logging_steps": 20, | |
| "max_steps": 102, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 2, | |
| "save_steps": 20, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 3346006641030144.0, | |
| "train_batch_size": 32, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |