tfa_output_2025_m02_d07_t07h_43m_33s

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: 1.4660

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-06
• train_batch_size: 2
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: constant_with_warmup
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0	0	1.4694
4.604	0.0030	1	1.4694
4.6485	0.0060	2	1.4693
4.6515	0.0091	3	1.4693
4.9659	0.0121	4	1.4692
4.5235	0.0151	5	1.4692
4.7107	0.0181	6	1.4690
4.3335	0.0211	7	1.4686
4.8938	0.0242	8	1.4682
4.8609	0.0272	9	1.4677
4.5648	0.0302	10	1.4667
4.5394	0.0332	11	1.4655
4.7629	0.0363	12	1.4644
4.5808	0.0393	13	1.4632
4.5545	0.0423	14	1.4619
4.4343	0.0453	15	1.4605
4.5662	0.0483	16	1.4591
4.398	0.0514	17	1.4575
4.3894	0.0544	18	1.4550
4.61	0.0574	19	1.4524
4.4373	0.0604	20	1.4501
4.2311	0.0634	21	1.4478
4.3044	0.0665	22	1.4454
4.2496	0.0695	23	1.4431
4.3269	0.0725	24	1.4409
4.2602	0.0755	25	1.4385
4.4063	0.0785	26	1.4362
4.2922	0.0816	27	1.4341
3.8772	0.0846	28	1.4320
4.2066	0.0876	29	1.4298
4.1774	0.0906	30	1.4280
4.1788	0.0937	31	1.4259
4.2413	0.0967	32	1.4240
4.2196	0.0997	33	1.4220
4.4204	0.1027	34	1.4203
4.1017	0.1057	35	1.4185
4.25	0.1088	36	1.4168
4.1705	0.1118	37	1.4152
4.0192	0.1148	38	1.4136
4.1317	0.1178	39	1.4123
4.243	0.1208	40	1.4107
3.9988	0.1239	41	1.4094
3.9451	0.1269	42	1.4081
4.0244	0.1299	43	1.4068
4.0956	0.1329	44	1.4055
3.9313	0.1360	45	1.4043
3.9223	0.1390	46	1.4033
3.7763	0.1420	47	1.4021
3.8153	0.1450	48	1.4011
4.1018	0.1480	49	1.4003
4.2674	0.1511	50	1.3994
4.0947	0.1541	51	1.3984
3.8134	0.1571	52	1.3976
3.7836	0.1601	53	1.3969
3.9645	0.1631	54	1.3961
3.5071	0.1662	55	1.3955
3.9643	0.1692	56	1.3950
3.7689	0.1722	57	1.3944
3.9377	0.1752	58	1.3937
3.9542	0.1782	59	1.3932
3.7511	0.1813	60	1.3926
3.7859	0.1843	61	1.3922
3.7963	0.1873	62	1.3916
3.7728	0.1903	63	1.3913
3.7714	0.1934	64	1.3910
3.9066	0.1964	65	1.3906
3.9718	0.1994	66	1.3903
3.7207	0.2024	67	1.3901
3.7618	0.2054	68	1.3896
3.5737	0.2085	69	1.3895
3.6742	0.2115	70	1.3892
3.6627	0.2145	71	1.3892
3.7246	0.2175	72	1.3889
3.5988	0.2205	73	1.3887
3.5042	0.2236	74	1.3885
3.6754	0.2266	75	1.3884
3.8237	0.2296	76	1.3883
3.6541	0.2326	77	1.3882
3.8442	0.2356	78	1.3881
3.8189	0.2387	79	1.3881
3.4796	0.2417	80	1.3879
3.7061	0.2447	81	1.3880
3.7453	0.2477	82	1.3880
3.4375	0.2508	83	1.3880
3.6748	0.2538	84	1.3881
3.6132	0.2568	85	1.3880
3.6022	0.2598	86	1.3879
3.9084	0.2628	87	1.3879
3.5629	0.2659	88	1.3882
3.6004	0.2689	89	1.3883
3.8498	0.2719	90	1.3883
3.5523	0.2749	91	1.3884
3.7526	0.2779	92	1.3886
3.7638	0.2810	93	1.3887
3.6319	0.2840	94	1.3888
3.551	0.2870	95	1.3888
3.8053	0.2900	96	1.3890
3.6299	0.2931	97	1.3891
3.8778	0.2961	98	1.3891
3.4661	0.2991	99	1.3893
3.6199	0.3021	100	1.3894
3.7169	0.3051	101	1.3897
3.6181	0.3082	102	1.3898
3.712	0.3112	103	1.3900
3.426	0.3142	104	1.3903
3.2462	0.3172	105	1.3905
3.4656	0.3202	106	1.3907
3.5511	0.3233	107	1.3910
3.5268	0.3263	108	1.3913
3.4383	0.3293	109	1.3915
3.5351	0.3323	110	1.3919
3.4221	0.3353	111	1.3922
3.064	0.3384	112	1.3926
3.4006	0.3414	113	1.3928
3.5908	0.3444	114	1.3931
3.5341	0.3474	115	1.3935
3.4771	0.3505	116	1.3938
3.4362	0.3535	117	1.3940
3.5801	0.3565	118	1.3941
3.5304	0.3595	119	1.3943
3.6278	0.3625	120	1.3945
3.5677	0.3656	121	1.3948
3.5208	0.3686	122	1.3950
3.5702	0.3716	123	1.3952
3.4074	0.3746	124	1.3954
3.1593	0.3776	125	1.3956
3.4503	0.3807	126	1.3958
3.6251	0.3837	127	1.3961
3.4879	0.3867	128	1.3965
3.3838	0.3897	129	1.3968
3.411	0.3927	130	1.3972
3.3504	0.3958	131	1.3975
3.2542	0.3988	132	1.3980
3.5616	0.4018	133	1.3984
3.3994	0.4048	134	1.3990
3.3805	0.4079	135	1.3994
3.4415	0.4109	136	1.3997
3.6288	0.4139	137	1.4001
3.2875	0.4169	138	1.4004
3.2699	0.4199	139	1.4007
3.6125	0.4230	140	1.4011
3.4689	0.4260	141	1.4013
3.4061	0.4290	142	1.4015
3.3196	0.4320	143	1.4019
3.4037	0.4350	144	1.4020
3.4214	0.4381	145	1.4024
3.3448	0.4411	146	1.4025
3.4775	0.4441	147	1.4028
3.4709	0.4471	148	1.4032
3.4283	0.4502	149	1.4033
3.4671	0.4532	150	1.4035
3.2426	0.4562	151	1.4038
3.4191	0.4592	152	1.4042
3.4264	0.4622	153	1.4045
3.2461	0.4653	154	1.4050
3.3855	0.4683	155	1.4053
3.313	0.4713	156	1.4056
3.3058	0.4743	157	1.4059
3.5508	0.4773	158	1.4062
3.2109	0.4804	159	1.4066
3.5045	0.4834	160	1.4068
3.4068	0.4864	161	1.4071
3.3438	0.4894	162	1.4075
3.3953	0.4924	163	1.4078
3.2312	0.4955	164	1.4079
3.2971	0.4985	165	1.4084
3.3118	0.5015	166	1.4086
3.4395	0.5045	167	1.4088
3.7162	0.5076	168	1.4089
3.3864	0.5106	169	1.4091
3.0887	0.5136	170	1.4092
2.9898	0.5166	171	1.4095
3.4697	0.5196	172	1.4099
3.2762	0.5227	173	1.4102
3.1383	0.5257	174	1.4106
3.2522	0.5287	175	1.4112
3.309	0.5317	176	1.4117
3.4431	0.5347	177	1.4121
3.1366	0.5378	178	1.4126
3.3094	0.5408	179	1.4131
3.4466	0.5438	180	1.4136
3.3411	0.5468	181	1.4140
3.013	0.5498	182	1.4143
3.4785	0.5529	183	1.4147
3.0358	0.5559	184	1.4152
3.2833	0.5589	185	1.4158
3.2953	0.5619	186	1.4162
3.3485	0.5650	187	1.4167
3.4911	0.5680	188	1.4172
3.3863	0.5710	189	1.4176
3.1944	0.5740	190	1.4180
3.2994	0.5770	191	1.4185
3.4385	0.5801	192	1.4187
3.3346	0.5831	193	1.4191
3.3318	0.5861	194	1.4194
3.4027	0.5891	195	1.4198
3.2532	0.5921	196	1.4201
3.2351	0.5952	197	1.4205
3.4589	0.5982	198	1.4203
3.4375	0.6012	199	1.4204
3.0901	0.6042	200	1.4207
3.3186	0.6073	201	1.4210
3.2891	0.6103	202	1.4212
3.2752	0.6133	203	1.4217
3.3808	0.6163	204	1.4221
3.376	0.6193	205	1.4223
3.4086	0.6224	206	1.4226
3.3506	0.6254	207	1.4228
3.4508	0.6284	208	1.4232
3.4237	0.6314	209	1.4235
3.2154	0.6344	210	1.4240
3.2379	0.6375	211	1.4244
2.8335	0.6405	212	1.4251
3.1927	0.6435	213	1.4251
3.1871	0.6465	214	1.4256
3.1004	0.6495	215	1.4259
3.2405	0.6526	216	1.4264
3.1544	0.6556	217	1.4269
3.1204	0.6586	218	1.4274
3.3257	0.6616	219	1.4280
3.2689	0.6647	220	1.4286
3.0117	0.6677	221	1.4289
3.4276	0.6707	222	1.4295
3.2358	0.6737	223	1.4301
3.1374	0.6767	224	1.4307
3.2972	0.6798	225	1.4312
3.2838	0.6828	226	1.4318
3.2839	0.6858	227	1.4322
3.2228	0.6888	228	1.4328
3.2605	0.6918	229	1.4334
3.2945	0.6949	230	1.4340
3.3155	0.6979	231	1.4344
3.1988	0.7009	232	1.4349
3.2921	0.7039	233	1.4355
2.8752	0.7069	234	1.4359
3.0065	0.7100	235	1.4361
3.1689	0.7130	236	1.4366
3.1959	0.7160	237	1.4370
3.3473	0.7190	238	1.4373
3.2927	0.7221	239	1.4377
2.9934	0.7251	240	1.4379
3.2058	0.7281	241	1.4384
3.1388	0.7311	242	1.4388
3.2384	0.7341	243	1.4388
3.2028	0.7372	244	1.4392
3.3737	0.7402	245	1.4392
3.166	0.7432	246	1.4397
3.0255	0.7462	247	1.4397
3.0979	0.7492	248	1.4401
3.2436	0.7523	249	1.4404
3.1785	0.7553	250	1.4408
3.2052	0.7583	251	1.4411
3.1967	0.7613	252	1.4413
2.9086	0.7644	253	1.4416
3.355	0.7674	254	1.4421
3.4027	0.7704	255	1.4422
2.9307	0.7734	256	1.4428
3.1738	0.7764	257	1.4429
3.3088	0.7795	258	1.4431
3.4942	0.7825	259	1.4432
2.831	0.7855	260	1.4437
3.1675	0.7885	261	1.4444
3.3274	0.7915	262	1.4447
3.0326	0.7946	263	1.4448
3.3138	0.7976	264	1.4454
3.2153	0.8006	265	1.4455
3.3983	0.8036	266	1.4458
3.179	0.8066	267	1.4461
3.2621	0.8097	268	1.4464
3.0191	0.8127	269	1.4468
3.058	0.8157	270	1.4472
3.3188	0.8187	271	1.4478
2.9837	0.8218	272	1.4481
3.2624	0.8248	273	1.4486
3.2701	0.8278	274	1.4492
3.1579	0.8308	275	1.4497
3.3164	0.8338	276	1.4501
2.9827	0.8369	277	1.4507
3.1842	0.8399	278	1.4512
3.2366	0.8429	279	1.4519
3.0562	0.8459	280	1.4520
2.9503	0.8489	281	1.4526
3.0441	0.8520	282	1.4530
3.4535	0.8550	283	1.4534
3.2656	0.8580	284	1.4537
3.3452	0.8610	285	1.4541
3.0958	0.8640	286	1.4548
3.1579	0.8671	287	1.4553
3.1473	0.8701	288	1.4556
3.2825	0.8731	289	1.4559
2.8554	0.8761	290	1.4563
3.2792	0.8792	291	1.4566
3.0977	0.8822	292	1.4567
3.0414	0.8852	293	1.4568
3.2151	0.8882	294	1.4569
3.1287	0.8912	295	1.4571
3.1167	0.8943	296	1.4572
3.1497	0.8973	297	1.4574
3.0451	0.9003	298	1.4576
3.147	0.9033	299	1.4578
3.2183	0.9063	300	1.4582
3.0974	0.9094	301	1.4585
3.1824	0.9124	302	1.4589
3.0607	0.9154	303	1.4593
3.1255	0.9184	304	1.4598
2.6534	0.9215	305	1.4604
2.9006	0.9245	306	1.4605
3.336	0.9275	307	1.4609
3.2408	0.9305	308	1.4609
3.0551	0.9335	309	1.4610
2.8721	0.9366	310	1.4610
3.1009	0.9396	311	1.4610
3.3979	0.9426	312	1.4608
3.133	0.9456	313	1.4609
3.1008	0.9486	314	1.4609
3.2113	0.9517	315	1.4610
3.161	0.9547	316	1.4612
2.968	0.9577	317	1.4614
2.936	0.9607	318	1.4619
3.4561	0.9637	319	1.4622
3.1529	0.9668	320	1.4625
3.1159	0.9698	321	1.4629
3.2588	0.9728	322	1.4630
2.9729	0.9758	323	1.4633
3.2778	0.9789	324	1.4636
2.9019	0.9819	325	1.4638
3.094	0.9849	326	1.4643
3.0259	0.9879	327	1.4647
3.3842	0.9909	328	1.4652
3.217	0.9940	329	1.4655
3.4145	0.9970	330	1.4658
3.3328	1.0	331	1.4660

Framework versions

• Transformers 4.48.0
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0