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Jennny/
llama-3.1-correctness-reg-adapter

Name: llama-3.1-correctness-reg-adapter
Availability: InStock

peft

safetensors

generated_from_trainer

meta-llama/Llama-3.1-8B-Instruct

adapter

llama3.1

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llama-3.1-correctness-reg-adapter

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.1128
Mse: 1.1128
Rmse: 1.0549
Mae: 0.8341
R2: 0.3010
Rounded Accuracy: 0.3656
Mae Class 0: 1.7920
Mse Class 0: 4.0470
Mae Class 1: 1.3730
Mse Class 1: 2.4817
Mae Class 2: 0.9256
Mse Class 2: 1.1026
Mae Class 3: 0.4746
Mse Class 3: 0.3342
Mae Class 4: 0.7528
Mse Class 4: 0.8202
Pred Count 0: 42
Pred Percent 0: 1.0332
Pred Count 1: 208
Pred Percent 1: 5.1169
Pred Count 2: 613
Pred Percent 2: 15.0800
Pred Count 3: 2137
Pred Percent 3: 52.5707
Pred Count 4: 1065
Pred Percent 4: 26.1993

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: 2e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Mse	Rmse	Mae	R2	Rounded Accuracy	Mae Class 0	Mse Class 0	Mae Class 1	Mse Class 1	Mae Class 2	Mse Class 2	Mae Class 3	Mse Class 3	Mae Class 4	Mse Class 4	Pred Count 0	Pred Percent 0	Pred Count 1	Pred Percent 1	Pred Count 2	Pred Percent 2	Pred Count 3	Pred Percent 3	Pred Count 4	Pred Percent 4
1.3585	0.2460	500	1.4487	1.4487	1.2036	0.9755	0.0901	0.2615	2.1072	4.9013	1.4185	2.4644	0.7422	0.8469	0.3903	0.3569	1.0780	1.4468	44	1.0824	142	3.4932	901	22.1648	2811	69.1513	167	4.1082
1.4516	0.4920	1000	1.1929	1.1929	1.0922	0.8503	0.2508	0.3811	1.7873	3.9885	1.3354	2.4576	0.9463	1.1696	0.5475	0.4553	0.7526	0.9228	44	1.0824	288	7.0849	621	15.2768	1838	45.2153	1274	31.3407
1.2905	0.7381	1500	1.1512	1.1512	1.0729	0.8684	0.2769	0.2940	1.6919	3.6392	1.2749	2.1948	0.8302	0.8937	0.3947	0.2967	0.9209	1.0826	43	1.0578	256	6.2977	659	16.2116	2810	69.1267	297	7.3063
1.2888	0.9841	2000	1.1128	1.1128	1.0549	0.8341	0.3010	0.3656	1.7920	4.0470	1.3730	2.4817	0.9256	1.1026	0.4746	0.3342	0.7528	0.8202	42	1.0332	208	5.1169	613	15.0800	2137	52.5707	1065	26.1993

Framework versions

PEFT 0.13.2
Transformers 4.49.0
Pytorch 2.5.1+cu124
Datasets 3.3.2
Tokenizers 0.21.1

Jennny/llama-3.1-correctness-reg-adapter

llama-3.1-correctness-reg-adapter

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Jennny/
llama-3.1-correctness-reg-adapter