llama_3_gsm8k_midset_cot_simplest – AI Model by CharlesLi | AlphaNeural AI

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CharlesLi/
llama_3_gsm8k_midset_cot_simplest

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

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llama_3_gsm8k_midset_cot_simplest

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5414

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: 0.0002
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
training_steps: 200

Training results

Training Loss	Epoch	Step	Validation Loss
1.121	0.04	5	1.0499
0.7969	0.08	10	0.7587
0.7177	0.12	15	0.6761
0.6315	0.16	20	0.6275
0.5984	0.2	25	0.5965
0.5678	0.24	30	0.5893
0.6067	0.28	35	0.5732
0.6306	0.32	40	0.5717
0.6134	0.36	45	0.5688
0.5396	0.4	50	0.5616
0.5796	0.44	55	0.5596
0.582	0.48	60	0.5543
0.5793	0.52	65	0.5518
0.4974	0.56	70	0.5556
0.5791	0.6	75	0.5544
0.5484	0.64	80	0.5518
0.5611	0.68	85	0.5500
0.5736	0.72	90	0.5474
0.5269	0.76	95	0.5490
0.58	0.8	100	0.5485
0.5295	0.84	105	0.5476
0.5124	0.88	110	0.5447
0.5368	0.92	115	0.5409
0.5705	0.96	120	0.5408
0.5244	1.0	125	0.5394
0.4334	1.04	130	0.5402
0.5256	1.08	135	0.5425
0.4398	1.12	140	0.5464
0.4843	1.16	145	0.5472
0.4437	1.2	150	0.5457
0.5133	1.24	155	0.5449
0.497	1.28	160	0.5427
0.4682	1.32	165	0.5420
0.4872	1.3600	170	0.5415
0.4763	1.4	175	0.5413
0.4647	1.44	180	0.5417
0.4679	1.48	185	0.5417
0.476	1.52	190	0.5414
0.462	1.56	195	0.5414
0.4665	1.6	200	0.5414

Framework versions

PEFT 0.12.0
Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.0
Tokenizers 0.19.1