This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on the UltraMedical dataset. It achieves the following results on the evaluation set:

Loss: 0.7358

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: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.8656	0.0225	200	0.7711
0.7615	0.0451	400	0.7521
0.748	0.0676	600	0.7457
0.7465	0.0902	800	0.7428
0.7468	0.1127	1000	0.7419
0.7434	0.1352	1200	0.7429
0.7467	0.1578	1400	0.7451
0.7508	0.1803	1600	0.7469
0.7505	0.2029	1800	0.7503
0.7541	0.2254	2000	0.7531
0.7559	0.2479	2200	0.7576
0.7592	0.2705	2400	0.7599
0.7729	0.2930	2600	0.7635
0.772	0.3156	2800	0.7645
0.7707	0.3381	3000	0.7628
0.7616	0.3606	3200	0.7614
0.7632	0.3832	3400	0.7590
0.7613	0.4057	3600	0.7574
0.7581	0.4283	3800	0.7558
0.7583	0.4508	4000	0.7539
0.7509	0.4733	4200	0.7518
0.7559	0.4959	4400	0.7506
0.7523	0.5184	4600	0.7491
0.7461	0.5410	4800	0.7469
0.7504	0.5635	5000	0.7464
0.7486	0.5860	5200	0.7449
0.7454	0.6086	5400	0.7436
0.7451	0.6311	5600	0.7427
0.7431	0.6537	5800	0.7412
0.7438	0.6762	6000	0.7402
0.7471	0.6987	6200	0.7390
0.7416	0.7213	6400	0.7378
0.7345	0.7438	6600	0.7364
0.7437	0.7663	6800	0.7349
0.7431	0.7889	7000	0.7349
0.737	0.8114	7200	0.7339
0.7358	0.8340	7400	0.7333
0.7336	0.8565	7600	0.7320
0.7327	0.8790	7800	0.7310
0.7288	0.9016	8000	0.7303
0.7326	0.9241	8200	0.7295
0.7354	0.9467	8400	0.7287
0.731	0.9692	8600	0.7278
0.7317	0.9917	8800	0.7272
0.6809	1.0143	9000	0.7359
0.6548	1.0368	9200	0.7341
0.6463	1.0594	9400	0.7353
0.6516	1.0819	9600	0.7357
0.6544	1.1044	9800	0.7345
0.6558	1.1270	10000	0.7342
0.6532	1.1495	10200	0.7331
0.653	1.1721	10400	0.7328
0.6583	1.1946	10600	0.7323
0.6537	1.2171	10800	0.7326
0.6622	1.2397	11000	0.7318
0.6596	1.2622	11200	0.7315
0.6522	1.2848	11400	0.7304
0.6517	1.3073	11600	0.7300
0.657	1.3298	11800	0.7296
0.6554	1.3524	12000	0.7286
0.6545	1.3749	12200	0.7287
0.6556	1.3975	12400	0.7283
0.655	1.4200	12600	0.7294
0.6489	1.4425	12800	0.7285
0.6539	1.4651	13000	0.7269
0.654	1.4876	13200	0.7273
0.6556	1.5102	13400	0.7273
0.6529	1.5327	13600	0.7271
0.6504	1.5552	13800	0.7264
0.6498	1.5778	14000	0.7256
0.6517	1.6003	14200	0.7255
0.656	1.6229	14400	0.7252
0.6471	1.6454	14600	0.7242
0.6485	1.6679	14800	0.7243
0.6545	1.6905	15000	0.7242
0.6527	1.7130	15200	0.7238
0.6504	1.7356	15400	0.7236
0.6492	1.7581	15600	0.7229
0.6529	1.7806	15800	0.7232
0.6507	1.8032	16000	0.7226
0.653	1.8257	16200	0.7229
0.6461	1.8483	16400	0.7223
0.6453	1.8708	16600	0.7221
0.6534	1.8933	16800	0.7219
0.6455	1.9159	17000	0.7220
0.6485	1.9384	17200	0.7212
0.6536	1.9610	17400	0.7214
0.6444	1.9835	17600	0.7211
0.6346	2.0060	17800	0.7356
0.5929	2.0286	18000	0.7368
0.5951	2.0511	18200	0.7371
0.6013	2.0736	18400	0.7374
0.6004	2.0962	18600	0.7375
0.5991	2.1187	18800	0.7375
0.5971	2.1413	19000	0.7369
0.597	2.1638	19200	0.7380
0.5951	2.1863	19400	0.7370
0.5916	2.2089	19600	0.7370
0.5992	2.2314	19800	0.7372
0.6011	2.2540	20000	0.7364
0.6003	2.2765	20200	0.7370
0.6003	2.2990	20400	0.7370
0.5985	2.3216	20600	0.7370
0.5988	2.3441	20800	0.7367
0.5959	2.3667	21000	0.7370
0.6019	2.3892	21200	0.7370
0.5977	2.4117	21400	0.7367
0.602	2.4343	21600	0.7368
0.5958	2.4568	21800	0.7368
0.5969	2.4794	22000	0.7360
0.6025	2.5019	22200	0.7362
0.5942	2.5244	22400	0.7361
0.6006	2.5470	22600	0.7361
0.5952	2.5695	22800	0.7366
0.6007	2.5921	23000	0.7363
0.6003	2.6146	23200	0.7363
0.6006	2.6371	23400	0.7359
0.6014	2.6597	23600	0.7360
0.6008	2.6822	23800	0.7356
0.6005	2.7048	24000	0.7357
0.5958	2.7273	24200	0.7356
0.5977	2.7498	24400	0.7358
0.6	2.7724	24600	0.7358
0.5978	2.7949	24800	0.7362
0.6018	2.8175	25000	0.7359
0.6079	2.8400	25200	0.7359
0.6036	2.8625	25400	0.7359
0.5985	2.8851	25600	0.7359
0.6019	2.9076	25800	0.7359
0.5994	2.9302	26000	0.7358
0.6027	2.9527	26200	0.7358
0.6014	2.9752	26400	0.7358
0.5957	2.9978	26600	0.7358

Framework versions

Transformers 4.43.3
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

KrithikV/MedMobile

MedMobile

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

KrithikV/
MedMobile