solar_10.7_darulm_unigram_proj_init_darulm_part2_r128_a512_v5

RefalMachine/solar_10.7_darulm_unigram_proj_init_darulm_part2_r128_a512_v5

solar_10.7_darulm_unigram_proj_init_darulm_part2_r128_a512_v5 is a machine learning model from RefalMachine. 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.

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

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At a glance

Library: peft
Access: Public
Created: Mar 4, 2024
Updated: Mar 4, 2024
SHA: 701114f7

Base models

outputs/solar_10.7_darulm_unigram_proj_init_8node_darulm_part1_v3_1.0_512_12_02_24

Model card

From the Hugging Face model README

solar_10.7_darulm_unigram_proj_init_darulm_part2_r128_a512_v3_1.0_512_28_02_24

This model is a fine-tuned version of outputs/solar_10.7_darulm_unigram_proj_init_8node_darulm_part1_v3_1.0_512_12_02_24 on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.2309
Accuracy: 0.5309

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: 5e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 24
gradient_accumulation_steps: 5
total_train_batch_size: 120
total_eval_batch_size: 24
optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
lr_scheduler_type: linear
num_epochs: 1.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.0	1	2.3534	0.5148
2.4427	0.01	500	2.3338	0.5155
2.4399	0.02	1000	2.3276	0.5164
2.4244	0.03	1500	2.3231	0.5169
2.4336	0.04	2000	2.3194	0.5177
2.4201	0.06	2500	2.3156	0.5180
2.4245	0.07	3000	2.3128	0.5185
2.4157	0.08	3500	2.3097	0.5187
2.4054	0.09	4000	2.3070	0.5194
2.4161	0.1	4500	2.3033	0.5197
2.395	0.11	5000	2.3020	0.5201
2.4037	0.12	5500	2.3001	0.5204
2.4188	0.13	6000	2.2977	0.5206
2.406	0.15	6500	2.2961	0.5208
2.4022	0.16	7000	2.2943	0.5210
2.3952	0.17	7500	2.2926	0.5217
2.394	0.18	8000	2.2909	0.5217
2.3828	0.19	8500	2.2891	0.5218
2.3903	0.2	9000	2.2882	0.5223
2.3943	0.21	9500	2.2861	0.5224
2.3944	0.22	10000	2.2851	0.5224
2.3872	0.23	10500	2.2841	0.5227
2.381	0.25	11000	2.2820	0.5228
2.3832	0.26	11500	2.2798	0.5232
2.3813	0.27	12000	2.2793	0.5237
2.3715	0.28	12500	2.2779	0.5241
2.3898	0.29	13000	2.2764	0.5240
2.3717	0.3	13500	2.2757	0.5240
2.3745	0.31	14000	2.2742	0.5244
2.3657	0.32	14500	2.2732	0.5244
2.3782	0.34	15000	2.2715	0.5247
2.3761	0.35	15500	2.2706	0.5247
2.3827	0.36	16000	2.2692	0.5249
2.3659	0.37	16500	2.2678	0.5251
2.3551	0.38	17000	2.2674	0.5252
2.3605	0.39	17500	2.2662	0.5255
2.3579	0.4	18000	2.2654	0.5256
2.361	0.41	18500	2.2642	0.5257
2.3632	0.42	19000	2.2652	0.5254
2.3409	0.44	19500	2.2625	0.5261
2.3546	0.45	20000	2.2631	0.5259
2.361	0.46	20500	2.2611	0.5264
2.355	0.47	21000	2.2598	0.5264
2.3599	0.48	21500	2.2588	0.5265
2.3554	0.49	22000	2.2583	0.5265
2.3552	0.5	22500	2.2571	0.5268
2.3574	0.51	23000	2.2565	0.5268
2.3527	0.53	23500	2.2557	0.5272
2.3574	0.54	24000	2.2548	0.5272
2.3395	0.55	24500	2.2534	0.5274
2.3517	0.56	25000	2.2531	0.5272
2.346	0.57	25500	2.2521	0.5275
2.3469	0.58	26000	2.2515	0.5275
2.3451	0.59	26500	2.2509	0.5278
2.3373	0.6	27000	2.2501	0.5277
2.3512	0.61	27500	2.2493	0.5281
2.3351	0.63	28000	2.2485	0.5282
2.3431	0.64	28500	2.2476	0.5282
2.3399	0.65	29000	2.2463	0.5283
2.3376	0.66	29500	2.2463	0.5284
2.3574	0.67	30000	2.2456	0.5285
2.3312	0.68	30500	2.2447	0.5289
2.3442	0.69	31000	2.2442	0.5288
2.338	0.7	31500	2.2434	0.5289
2.3345	0.72	32000	2.2433	0.5291
2.3314	0.73	32500	2.2420	0.5292
2.326	0.74	33000	2.2414	0.5293
2.3247	0.75	33500	2.2409	0.5295
2.3363	0.76	34000	2.2403	0.5296
2.3409	0.77	34500	2.2395	0.5297
2.335	0.78	35000	2.2391	0.5295
2.3194	0.79	35500	2.2383	0.5298
2.3367	0.8	36000	2.2379	0.5301
2.3286	0.82	36500	2.2372	0.5301
2.3225	0.83	37000	2.2366	0.5302
2.3198	0.84	37500	2.2363	0.5301
2.3274	0.85	38000	2.2355	0.5301
2.3195	0.86	38500	2.2349	0.5303
2.3418	0.87	39000	2.2344	0.5303
2.323	0.88	39500	2.2340	0.5304
2.3211	0.89	40000	2.2336	0.5304
2.3332	0.91	40500	2.2334	0.5306
2.3226	0.92	41000	2.2329	0.5307
2.3329	0.93	41500	2.2325	0.5308
2.3172	0.94	42000	2.2321	0.5307
2.3231	0.95	42500	2.2319	0.5308
2.314	0.96	43000	2.2316	0.5309
2.3205	0.97	43500	2.2315	0.5308
2.3208	0.98	44000	2.2312	0.5309
2.3228	0.99	44500	2.2310	0.5309

Framework versions

Transformers 4.37.2
Pytorch 2.1.2
Datasets 2.16.1
Tokenizers 0.15.2

Training procedure

Framework versions

PEFT 0.6.0

Library: peft
Created: Mar 4, 2024
Updated: Mar 4, 2024