Meta-Llama-3-8B-fashion-v7.0 – AI Model by mlrun | AlphaNeural AI

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mlrun/
Meta-Llama-3-8B-fashion-v7.0

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meta-llama/Meta-Llama-3-8B

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results

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6367
Rewards/chosen: -0.0965
Rewards/rejected: -0.4524
Rewards/accuracies: 1.0
Rewards/margins: 0.3560
Logps/rejected: -2.2621
Logps/chosen: -0.4823
Logits/rejected: -1.8871
Logits/chosen: -1.6815
Nll Loss: 0.5673
Log Odds Ratio: -0.0697
Log Odds Chosen: 2.8182

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: 0.001
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen	Nll Loss	Log Odds Ratio	Log Odds Chosen
4.5921	0.2126	11	3.5043	-0.6803	-0.8039	1.0	0.1236	-4.0195	-3.4015	-3.2932	-3.2264	3.3934	-0.4430	0.6340
1.3234	0.4251	22	0.8683	-0.1360	-0.2818	1.0	0.1458	-1.4090	-0.6802	-2.6124	-2.3854	0.7738	-0.2790	1.1626
0.7333	0.6377	33	0.8013	-0.1269	-0.3679	1.0	0.2410	-1.8394	-0.6346	-2.3638	-2.1864	0.7403	-0.1581	1.8427
0.5916	0.8502	44	0.6367	-0.0965	-0.4524	1.0	0.3560	-2.2621	-0.4823	-1.8871	-1.6815	0.5673	-0.0697	2.8182

Framework versions

PEFT 0.11.1
Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1