RefalMachine/llm_test_unigram - AI Model Zoo

llama7b_rulm_spm_unigram_10_mean_init_tie_rulm_small_1e_12_10_23

This model is a fine-tuned version of llama7b_rulm_spm_unigram_10_mean_init_tie_12_10_23 on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.9526
Accuracy: 0.4388

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: 0.0003
train_batch_size: 12
eval_batch_size: 12
seed: 42
distributed_type: multi-GPU
num_devices: 10
gradient_accumulation_steps: 2
total_train_batch_size: 240
total_eval_batch_size: 120
optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 200
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.5018	0.01	1000	3.5095	0.3768
3.3375	0.02	2000	3.3603	0.3918
3.2694	0.02	3000	3.2995	0.3982
3.236	0.03	4000	3.2626	0.4022
3.2131	0.04	5000	3.2350	0.4048
3.1934	0.05	6000	3.2147	0.4073
3.177	0.06	7000	3.2006	0.4089
3.1653	0.07	8000	3.1890	0.4097
3.1548	0.07	9000	3.1779	0.4109
3.152	0.08	10000	3.1699	0.4119
3.1416	0.09	11000	3.1622	0.4130
3.1387	0.1	12000	3.1561	0.4139
3.1192	0.11	13000	3.1495	0.4143
3.1221	0.12	14000	3.1431	0.4150
3.1136	0.12	15000	3.1391	0.4155
3.106	0.13	16000	3.1348	0.4160
3.1023	0.14	17000	3.1312	0.4165
3.1062	0.15	18000	3.1265	0.4172
3.1007	0.16	19000	3.1230	0.4177
3.0979	0.16	20000	3.1201	0.4178
3.0897	0.17	21000	3.1168	0.4179
3.0863	0.18	22000	3.1128	0.4189
3.0898	0.19	23000	3.1097	0.4191
3.0825	0.2	24000	3.1074	0.4191
3.0808	0.21	25000	3.1037	0.4200
3.0774	0.21	26000	3.1032	0.4197
3.0652	0.22	27000	3.0980	0.4202
3.0693	0.23	28000	3.0968	0.4207
3.0665	0.24	29000	3.0944	0.4209
3.0657	0.25	30000	3.0920	0.4210
3.0608	0.26	31000	3.0911	0.4213
3.0647	0.26	32000	3.0896	0.4213
3.0604	0.27	33000	3.0861	0.4217
3.0577	0.28	34000	3.0845	0.4221
3.0606	0.29	35000	3.0814	0.4220
3.0515	0.3	36000	3.0801	0.4227
3.0527	0.31	37000	3.0772	0.4225
3.0507	0.31	38000	3.0758	0.4228
3.0433	0.32	39000	3.0739	0.4234
3.0546	0.33	40000	3.0717	0.4234
3.0484	0.34	41000	3.0697	0.4236
3.0441	0.35	42000	3.0694	0.4236
3.0292	0.35	43000	3.0662	0.4242
3.0384	0.36	44000	3.0643	0.4244
3.0367	0.37	45000	3.0629	0.4240
3.0337	0.38	46000	3.0622	0.4246
3.0385	0.39	47000	3.0599	0.4245
3.0319	0.4	48000	3.0574	0.4250
3.0255	0.4	49000	3.0573	0.4249
3.021	0.41	50000	3.0557	0.4253
3.0305	0.42	51000	3.0530	0.4254
3.0248	0.43	52000	3.0528	0.4257
3.0269	0.44	53000	3.0495	0.4261
3.0136	0.45	54000	3.0488	0.4259
3.0156	0.45	55000	3.0468	0.4262
3.022	0.46	56000	3.0454	0.4268
3.0193	0.47	57000	3.0442	0.4269
3.0222	0.48	58000	3.0417	0.4270
3.0111	0.49	59000	3.0393	0.4276
3.0148	0.49	60000	3.0384	0.4273
3.0077	0.5	61000	3.0364	0.4276
3.0167	0.51	62000	3.0358	0.4276
3.0049	0.52	63000	3.0343	0.4280
3.016	0.53	64000	3.0322	0.4281
3.0103	0.54	65000	3.0297	0.4285
3.0066	0.54	66000	3.0290	0.4284
2.9958	0.55	67000	3.0281	0.4285
3.0062	0.56	68000	3.0266	0.4288
2.9985	0.57	69000	3.0245	0.4289
3.0031	0.58	70000	3.0224	0.4292
2.9894	0.59	71000	3.0214	0.4295
2.9929	0.59	72000	3.0193	0.4296
2.9904	0.6	73000	3.0176	0.4296
2.9989	0.61	74000	3.0171	0.4301
2.9959	0.62	75000	3.0153	0.4301
2.9847	0.63	76000	3.0142	0.4306
2.9892	0.63	77000	3.0127	0.4308
2.9924	0.64	78000	3.0110	0.4310
2.991	0.65	79000	3.0096	0.4312
2.9824	0.66	80000	3.0080	0.4311
2.9879	0.67	81000	3.0060	0.4315
2.9764	0.68	82000	3.0042	0.4321
2.9827	0.68	83000	3.0030	0.4315
2.9769	0.69	84000	3.0012	0.4324
2.9788	0.7	85000	3.0002	0.4322
2.9734	0.71	86000	2.9987	0.4325
2.9769	0.72	87000	2.9975	0.4328
2.9676	0.73	88000	2.9959	0.4326
2.9677	0.73	89000	2.9943	0.4330
2.9739	0.74	90000	2.9933	0.4330
2.9691	0.75	91000	2.9914	0.4334
2.969	0.76	92000	2.9901	0.4336
2.9602	0.77	93000	2.9889	0.4337
2.965	0.78	94000	2.9872	0.4339
2.9627	0.78	95000	2.9853	0.4341
2.9542	0.79	96000	2.9844	0.4340
2.9552	0.8	97000	2.9822	0.4344
2.9576	0.81	98000	2.9812	0.4347
2.9579	0.82	99000	2.9802	0.4348
2.9508	0.82	100000	2.9784	0.4349
2.9551	0.83	101000	2.9771	0.4353
2.9535	0.84	102000	2.9759	0.4357
2.9479	0.85	103000	2.9743	0.4357
2.9542	0.86	104000	2.9732	0.4359
2.9481	0.87	105000	2.9715	0.4360
2.941	0.87	106000	2.9697	0.4362
2.9435	0.88	107000	2.9684	0.4365
2.9403	0.89	108000	2.9674	0.4368
2.9453	0.9	109000	2.9661	0.4367
2.9396	0.91	110000	2.9644	0.4372
2.9375	0.92	111000	2.9633	0.4372
2.9284	0.92	112000	2.9621	0.4374
2.9418	0.93	113000	2.9606	0.4376
2.934	0.94	114000	2.9594	0.4377
2.9374	0.95	115000	2.9583	0.4380
2.9302	0.96	116000	2.9569	0.4382
2.9273	0.96	117000	2.9560	0.4382
2.9338	0.97	118000	2.9548	0.4384
2.9304	0.98	119000	2.9539	0.4385
2.9361	0.99	120000	2.9531	0.4385
2.927	1.0	121000	2.9526	0.4387

Framework versions

Transformers 4.34.0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.14.1