Instructions to use Jeckmu/Qwen2-VL-2B-Instruct-GPTQ-Int4-lora-SurveillanceVideo-250210 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Jeckmu/Qwen2-VL-2B-Instruct-GPTQ-Int4-lora-SurveillanceVideo-250210 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4") model = PeftModel.from_pretrained(base_model, "Jeckmu/Qwen2-VL-2B-Instruct-GPTQ-Int4-lora-SurveillanceVideo-250210") - Notebooks
- Google Colab
- Kaggle
File size: 2,865 Bytes
424baac | 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 | {
"best_metric": null,
"best_model_checkpoint": null,
"epoch": 1.3957597173144876,
"eval_steps": 500,
"global_step": 50,
"is_hyper_param_search": false,
"is_local_process_zero": true,
"is_world_process_zero": true,
"log_history": [
{
"epoch": 0.1413427561837456,
"grad_norm": 1.3223986625671387,
"learning_rate": 4.972077065562821e-05,
"loss": 0.5319,
"num_input_tokens_seen": 89360,
"step": 5
},
{
"epoch": 0.2826855123674912,
"grad_norm": 1.8465224504470825,
"learning_rate": 4.888932014465352e-05,
"loss": 0.346,
"num_input_tokens_seen": 173680,
"step": 10
},
{
"epoch": 0.42402826855123676,
"grad_norm": 2.7824206352233887,
"learning_rate": 4.752422169756048e-05,
"loss": 0.3586,
"num_input_tokens_seen": 256320,
"step": 15
},
{
"epoch": 0.5653710247349824,
"grad_norm": 2.0576891899108887,
"learning_rate": 4.5655969357899874e-05,
"loss": 0.3476,
"num_input_tokens_seen": 354080,
"step": 20
},
{
"epoch": 0.7067137809187279,
"grad_norm": 2.399836778640747,
"learning_rate": 4.332629679574566e-05,
"loss": 0.2828,
"num_input_tokens_seen": 445120,
"step": 25
},
{
"epoch": 0.8480565371024735,
"grad_norm": 3.21662974357605,
"learning_rate": 4.058724504646834e-05,
"loss": 0.3261,
"num_input_tokens_seen": 536160,
"step": 30
},
{
"epoch": 0.9893992932862191,
"grad_norm": 2.1142466068267822,
"learning_rate": 3.7500000000000003e-05,
"loss": 0.1752,
"num_input_tokens_seen": 622160,
"step": 35
},
{
"epoch": 1.1130742049469964,
"grad_norm": 2.0932960510253906,
"learning_rate": 3.413352560915988e-05,
"loss": 0.1521,
"num_input_tokens_seen": 702992,
"step": 40
},
{
"epoch": 1.254416961130742,
"grad_norm": 1.1899418830871582,
"learning_rate": 3.056302334890786e-05,
"loss": 0.1056,
"num_input_tokens_seen": 795712,
"step": 45
},
{
"epoch": 1.3957597173144876,
"grad_norm": 1.8297990560531616,
"learning_rate": 2.686825233966061e-05,
"loss": 0.2243,
"num_input_tokens_seen": 880032,
"step": 50
}
],
"logging_steps": 5,
"max_steps": 105,
"num_input_tokens_seen": 880032,
"num_train_epochs": 3,
"save_steps": 50,
"stateful_callbacks": {
"TrainerControl": {
"args": {
"should_epoch_stop": false,
"should_evaluate": false,
"should_log": false,
"should_save": true,
"should_training_stop": false
},
"attributes": {}
}
},
"total_flos": 3561971226476544.0,
"train_batch_size": 2,
"trial_name": null,
"trial_params": null
}
|