Instructions to use dbaysal/code-unit-unlearning-qwen2_5_coder_3b-prod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dbaysal/code-unit-unlearning-qwen2_5_coder_3b-prod with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-3B") model = PeftModel.from_pretrained(base_model, "dbaysal/code-unit-unlearning-qwen2_5_coder_3b-prod") - Notebooks
- Google Colab
- Kaggle
File size: 2,614 Bytes
897fc52 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 | {
"best_global_step": null,
"best_metric": null,
"best_model_checkpoint": null,
"epoch": 2.4210526315789473,
"eval_steps": 500,
"global_step": 12,
"is_hyper_param_search": false,
"is_local_process_zero": true,
"is_world_process_zero": true,
"log_history": [
{
"epoch": 0.21052631578947367,
"grad_norm": 258.9384460449219,
"learning_rate": 0.0003,
"loss": 109.7683,
"step": 1
},
{
"epoch": 0.42105263157894735,
"grad_norm": 307.8008117675781,
"learning_rate": 0.0003,
"loss": 63.592,
"step": 2
},
{
"epoch": 0.631578947368421,
"grad_norm": 68.41874694824219,
"learning_rate": 0.0003,
"loss": 45.0693,
"step": 3
},
{
"epoch": 0.8421052631578947,
"grad_norm": 42.555973052978516,
"learning_rate": 0.0003,
"loss": 38.2675,
"step": 4
},
{
"epoch": 1.0,
"grad_norm": 20.205808639526367,
"learning_rate": 0.0003,
"loss": 24.643,
"step": 5
},
{
"epoch": 1.2105263157894737,
"grad_norm": 40.714134216308594,
"learning_rate": 0.0003,
"loss": 28.5208,
"step": 6
},
{
"epoch": 1.4210526315789473,
"grad_norm": 28.32428741455078,
"learning_rate": 0.0003,
"loss": 26.8694,
"step": 7
},
{
"epoch": 1.631578947368421,
"grad_norm": 36.77808380126953,
"learning_rate": 0.0003,
"loss": 25.45,
"step": 8
},
{
"epoch": 1.8421052631578947,
"grad_norm": 12.102893829345703,
"learning_rate": 0.0003,
"loss": 23.5542,
"step": 9
},
{
"epoch": 2.0,
"grad_norm": 18.711143493652344,
"learning_rate": 0.0003,
"loss": 16.8427,
"step": 10
},
{
"epoch": 2.2105263157894735,
"grad_norm": 22.953947067260742,
"learning_rate": 0.0003,
"loss": 21.976,
"step": 11
},
{
"epoch": 2.4210526315789473,
"grad_norm": 16.876480102539062,
"learning_rate": 0.0003,
"loss": 20.9446,
"step": 12
}
],
"logging_steps": 1,
"max_steps": 12,
"num_input_tokens_seen": 0,
"num_train_epochs": 3,
"save_steps": 500,
"stateful_callbacks": {
"TrainerControl": {
"args": {
"should_epoch_stop": false,
"should_evaluate": false,
"should_log": false,
"should_save": true,
"should_training_stop": true
},
"attributes": {}
}
},
"total_flos": 0.0,
"train_batch_size": 4,
"trial_name": null,
"trial_params": null
}
|