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alizaidi/
phi-3-mini-LoRA

Name: phi-3-mini-LoRA
Availability: InStock

generated_from_trainer

microsoft/Phi-3-mini-4k-instruct

adapter

mit

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phi-3-mini-LoRA

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

Loss: 1.2015

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
1.9715	0.0242	100	1.9517
1.8637	0.0484	200	1.7875
1.7019	0.0725	300	1.6473
1.6127	0.0967	400	1.5828
1.5545	0.1209	500	1.5389
1.5144	0.1451	600	1.5045
1.4823	0.1693	700	1.4746
1.4535	0.1935	800	1.4502
1.4293	0.2176	900	1.4270
1.4132	0.2418	1000	1.4073
1.388	0.2660	1100	1.3880
1.3757	0.2902	1200	1.3706
1.3594	0.3144	1300	1.3543
1.3399	0.3386	1400	1.3410
1.3314	0.3627	1500	1.3284
1.3161	0.3869	1600	1.3167
1.3005	0.4111	1700	1.3084
1.2937	0.4353	1800	1.2987
1.2824	0.4595	1900	1.2920
1.2806	0.4836	2000	1.2859
1.2773	0.5078	2100	1.2793
1.2717	0.5320	2200	1.2738
1.2654	0.5562	2300	1.2692
1.2597	0.5804	2400	1.2644
1.2536	0.6046	2500	1.2601
1.2486	0.6287	2600	1.2560
1.2416	0.6529	2700	1.2527
1.2462	0.6771	2800	1.2494
1.2402	0.7013	2900	1.2465
1.2353	0.7255	3000	1.2434
1.2285	0.7497	3100	1.2410
1.2314	0.7738	3200	1.2384
1.2342	0.7980	3300	1.2357
1.2195	0.8222	3400	1.2339
1.2306	0.8464	3500	1.2316
1.2225	0.8706	3600	1.2301
1.2174	0.8947	3700	1.2281
1.2293	0.9189	3800	1.2267
1.2194	0.9431	3900	1.2250
1.2169	0.9673	4000	1.2234
1.2138	0.9915	4100	1.2224
1.2105	1.0157	4200	1.2214
1.2081	1.0398	4300	1.2201
1.2129	1.0640	4400	1.2188
1.1995	1.0882	4500	1.2177
1.196	1.1124	4600	1.2167
1.2041	1.1366	4700	1.2163
1.2104	1.1608	4800	1.2151
1.205	1.1849	4900	1.2144
1.2055	1.2091	5000	1.2135
1.1966	1.2333	5100	1.2128
1.2017	1.2575	5200	1.2120
1.1995	1.2817	5300	1.2117
1.2015	1.3058	5400	1.2108
1.1978	1.3300	5500	1.2103
1.2017	1.3542	5600	1.2098
1.196	1.3784	5700	1.2093
1.1976	1.4026	5800	1.2089
1.2057	1.4268	5900	1.2082
1.2012	1.4509	6000	1.2079
1.2067	1.4751	6100	1.2074
1.2048	1.4993	6200	1.2071
1.2011	1.5235	6300	1.2068
1.1911	1.5477	6400	1.2064
1.1974	1.5719	6500	1.2061
1.1934	1.5960	6600	1.2059
1.1896	1.6202	6700	1.2057
1.1895	1.6444	6800	1.2052
1.203	1.6686	6900	1.2051
1.191	1.6928	7000	1.2048
1.1995	1.7169	7100	1.2045
1.1979	1.7411	7200	1.2043
1.1918	1.7653	7300	1.2042
1.1969	1.7895	7400	1.2040
1.1869	1.8137	7500	1.2038
1.1871	1.8379	7600	1.2036
1.1988	1.8620	7700	1.2035
1.1942	1.8862	7800	1.2034
1.1931	1.9104	7900	1.2033
1.1947	1.9346	8000	1.2030
1.1932	1.9588	8100	1.2030
1.1922	1.9830	8200	1.2028
1.192	2.0071	8300	1.2027
1.1997	2.0313	8400	1.2027
1.1945	2.0555	8500	1.2026
1.1934	2.0797	8600	1.2026
1.1955	2.1039	8700	1.2024
1.1901	2.1280	8800	1.2024
1.1898	2.1522	8900	1.2023
1.186	2.1764	9000	1.2022
1.1858	2.2006	9100	1.2022
1.1965	2.2248	9200	1.2021
1.1835	2.2490	9300	1.2021
1.1983	2.2731	9400	1.2020
1.1813	2.2973	9500	1.2020
1.1903	2.3215	9600	1.2019
1.1952	2.3457	9700	1.2019
1.1899	2.3699	9800	1.2018
1.2011	2.3941	9900	1.2018
1.1936	2.4182	10000	1.2018
1.1931	2.4424	10100	1.2018
1.1991	2.4666	10200	1.2017
1.19	2.4908	10300	1.2017
1.1913	2.5150	10400	1.2016
1.1886	2.5391	10500	1.2017
1.1848	2.5633	10600	1.2016
1.1875	2.5875	10700	1.2016
1.1887	2.6117	10800	1.2016
1.1866	2.6359	10900	1.2016
1.188	2.6601	11000	1.2016
1.1952	2.6842	11100	1.2015
1.1947	2.7084	11200	1.2015
1.1905	2.7326	11300	1.2015
1.1838	2.7568	11400	1.2015
1.1893	2.7810	11500	1.2015
1.1808	2.8052	11600	1.2015
1.1909	2.8293	11700	1.2015
1.1858	2.8535	11800	1.2015
1.185	2.8777	11900	1.2015
1.1947	2.9019	12000	1.2015
1.1868	2.9261	12100	1.2014
1.1872	2.9502	12200	1.2015
1.1852	2.9744	12300	1.2015
1.185	2.9986	12400	1.2015

Framework versions

PEFT 0.11.1
Transformers 4.43.1
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

alizaidi/phi-3-mini-LoRA

phi-3-mini-LoRA

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

alizaidi/
phi-3-mini-LoRA