Llama3-8B-lora-r-32-generic-step-1200-lr-1e-5-labels_40.0-1 – AI Model by Siqi-Hu | AlphaNeural AI

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Siqi-Hu/
Llama3-8B-lora-r-32-generic-step-1200-lr-1e-5-labels_40.0-1

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

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Llama3-8B-lora-r-32-generic-step-1200-lr-1e-5-labels_40.0-1

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: 2.9038

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: 50
training_steps: 1200
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
5.0258	1.0870	50	4.1291
3.728	2.1739	100	3.4327
3.2554	3.2609	150	3.1749
3.0389	4.3478	200	3.0403
2.8709	5.4348	250	2.9543
2.7513	6.5217	300	2.8973
2.6369	7.6087	350	2.8526
2.5456	8.6957	400	2.8278
2.4591	9.7826	450	2.8082
2.3865	10.8696	500	2.8015
2.3214	11.9565	550	2.8006
2.2617	13.0435	600	2.8072
2.203	14.1304	650	2.8272
2.1612	15.2174	700	2.8441
2.1271	16.3043	750	2.8511
2.075	17.3913	800	2.8676
2.0602	18.4783	850	2.8769
2.0296	19.5652	900	2.8869
2.0106	20.6522	950	2.8915
2.0026	21.7391	1000	2.8979
1.9941	22.8261	1050	2.9038

Framework versions

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