cyllama3

khangmacon/cyllama3

cyllama3 is a machine learning model from khangmacon. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as llama3.

should probably proofread and complete it, then remove this comment. --

Downloads · 30 days

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All-time downloads

175

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Repo size

218 GB

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Public

Hugging Face

Repo makeup

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.safetensors4.9 GB · 100%

At a glance

Library: peft
License: llama3
Access: Public
Created: May 7, 2024
Updated: May 28, 2024
SHA: b6650199

Base models

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

Datasets

khangmacon/llmtrain

Model card

From the Hugging Face model README

cyllama3

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

Loss: 1.9930
Accuracy: 0.5590

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: 3e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 128
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.2432	0.01	500	2.1239	0.5358
2.209	0.02	1000	2.0922	0.5404
2.1988	0.03	1500	2.0742	0.5436
2.1877	0.04	2000	2.0615	0.5463
2.1743	0.05	2500	2.0514	0.5479
2.1885	0.06	3000	2.0427	0.5495
2.1883	0.07	3500	2.0355	0.5509
2.1954	0.08	4000	2.0298	0.5519
2.1597	0.09	4500	2.0254	0.5526
2.1763	0.1	5000	2.0222	0.5532
2.1413	0.11	5500	2.0195	0.5541
2.1812	0.12	6000	2.0169	0.5545
2.1526	0.14	6500	2.0148	0.5547
2.155	0.15	7000	2.0131	0.5554
2.1594	0.16	7500	2.0110	0.5558
2.1681	0.17	8000	2.0097	0.5559
2.1572	0.18	8500	2.0083	0.5562
2.0943	0.19	9000	2.0074	0.5566
2.1421	0.2	9500	2.0063	0.5566
2.1196	0.21	10000	2.0049	0.5568
2.1634	0.22	10500	2.0042	0.5568
2.1361	0.23	11000	2.0035	0.5573
2.1614	0.24	11500	2.0027	0.5572
2.1205	0.25	12000	2.0021	0.5576
2.0984	0.26	12500	2.0011	0.5576
2.1226	0.27	13000	2.0006	0.5575
2.1054	0.28	13500	2.0001	0.5577
2.1297	0.29	14000	1.9997	0.5578
2.1233	0.3	14500	1.9988	0.5581
2.1348	0.31	15000	1.9984	0.5581
2.1494	0.32	15500	1.9980	0.5582
2.0827	0.33	16000	1.9976	0.5584
2.0991	0.34	16500	1.9975	0.5582
2.1108	0.35	17000	1.9972	0.5582
2.1209	0.36	17500	1.9968	0.5583
2.1012	0.37	18000	1.9963	0.5584
2.1155	0.38	18500	1.9959	0.5585
2.1493	0.4	19000	1.9956	0.5585
2.1219	0.41	19500	1.9953	0.5587
2.1584	0.42	20000	1.9952	0.5588
2.1167	0.43	20500	1.9950	0.5587
2.1507	0.44	21000	1.9948	0.5586
2.1043	0.45	21500	1.9946	0.5587
2.0864	0.46	22000	1.9945	0.5587
2.1074	0.47	22500	1.9943	0.5587
2.0858	0.48	23000	1.9942	0.5590
2.1178	0.49	23500	1.9941	0.5588
2.1148	0.5	24000	1.9940	0.5588
2.1165	0.51	24500	1.9939	0.5588
2.1012	0.52	25000	1.9938	0.5590
2.1573	0.53	25500	1.9936	0.5590
2.1674	0.54	26000	1.9936	0.5589
2.1184	0.55	26500	1.9935	0.5590
2.1424	0.56	27000	1.9935	0.5590
2.1437	0.57	27500	1.9935	0.5590
2.1244	0.58	28000	1.9933	0.5591
2.0767	0.59	28500	1.9933	0.5589
2.1182	0.6	29000	1.9934	0.5591
2.1277	0.61	29500	1.9933	0.5591
2.1407	0.62	30000	1.9932	0.5591
2.1222	0.63	30500	1.9932	0.5591
2.1146	0.64	31000	1.9931	0.5591
2.1441	0.65	31500	1.9932	0.5591
2.1224	0.67	32000	1.9931	0.5590
2.0878	0.68	32500	1.9932	0.5591
2.1172	0.69	33000	1.9932	0.5590
2.1166	0.7	33500	1.9931	0.5592
2.1054	0.71	34000	1.9931	0.5591
2.0972	0.72	34500	1.9931	0.5590
2.1228	0.73	35000	1.9931	0.5590
2.1231	0.74	35500	1.9931	0.5592
2.0974	0.75	36000	1.9931	0.5590
2.1025	0.76	36500	1.9931	0.5591
2.1217	0.77	37000	1.9931	0.5590
2.1227	0.78	37500	1.9930	0.5591
2.1272	0.79	38000	1.9931	0.5592
2.117	0.8	38500	1.9931	0.5591
2.1325	0.81	39000	1.9931	0.5591
2.1046	0.82	39500	1.9930	0.5591
2.1096	0.83	40000	1.9930	0.5591
2.1149	0.84	40500	1.9931	0.5591
2.122	0.85	41000	1.9931	0.5591
2.1137	0.86	41500	1.9931	0.5591
2.0983	0.87	42000	1.9930	0.5590
2.1109	0.88	42500	1.9931	0.5591
2.172	0.89	43000	1.9930	0.5590
2.0882	0.9	43500	1.9930	0.5591
2.0646	0.91	44000	1.9930	0.5591
2.1223	0.93	44500	1.9930	0.5591
2.1342	0.94	45000	1.9930	0.5591
2.0991	0.95	45500	1.9930	0.5590
2.1431	0.96	46000	1.9930	0.5592
2.0965	0.97	46500	1.9931	0.5590
2.1377	0.98	47000	1.9931	0.5592
2.1118	0.99	47500	1.9931	0.5592
2.089	1.0	48000	1.9930	0.5590

Framework versions

PEFT 0.10.1.dev0
Transformers 4.39.3
Pytorch 2.2.0
Datasets 2.18.0
Tokenizers 0.15.2

Library: peft
License: llama3
Created: May 7, 2024
Updated: May 28, 2024