Llama2-7B-lora-r-32-generic-step-1800-labels_40.0-full-precision-augmented-bck – AI Model by Siqi-Hu | AlphaNeural AI

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Siqi-Hu/
Llama2-7B-lora-r-32-generic-step-1800-labels_40.0-full-precision-augmented-bck

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meta-llama/Llama-2-7b-hf

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Llama2-7B-lora-r-32-generic-step-1800-labels_40.0-full-precision-augmented

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on an unknown dataset. It achieves the following results on the evaluation set:

Loss: nan

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
training_steps: 1800
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
5.7766	0.0366	20	5.7248
5.4712	0.0731	40	5.4960
5.172	0.1097	60	5.1008
4.738	0.1463	80	4.5591
4.2327	0.1828	100	4.1306
3.9266	0.2194	120	3.8741
3.6921	0.2559	140	3.7012
3.6237	0.2925	160	3.5645
3.4321	0.3291	180	3.4501
3.3499	0.3656	200	3.3555
3.2527	0.4022	220	3.2778
3.2229	0.4388	240	3.2034
3.1517	0.4753	260	3.1372
3.0409	0.5119	280	3.0812
3.0456	0.5484	300	3.0284
2.9754	0.5850	320	2.9794
2.9351	0.6216	340	2.9334
2.9134	0.6581	360	2.8936
2.8644	0.6947	380	2.8584
2.7915	0.7313	400	2.8248
2.793	0.7678	420	2.7944
2.7589	0.8044	440	2.7643
2.7992	0.8410	460	2.7376
2.7098	0.8775	480	2.7131
2.6724	0.9141	500	2.6859
2.6667	0.9506	520	2.6642
2.6535	0.9872	540	nan
2.5662	1.0238	560	nan
2.5738	1.0603	580	nan

Framework versions

PEFT 0.15.2
Transformers 4.45.2
Pytorch 2.5.0+cu121
Datasets 3.2.0
Tokenizers 0.20.3