You can deploy this model and start earning money today!

mtzig/
v0_mistral_lora_last_n_train_3

Name: v0_mistral_lora_last_n_train_3
Availability: InStock

peft

safetensors

generated_from_trainer

peiyi9979/math-shepherd-mistral-7b-prm

adapter

Views

No views yet

v0_mistral_lora_last_n_train_3

This model is a fine-tuned version of peiyi9979/math-shepherd-mistral-7b-prm on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2838
Accuracy: 0.8991
Precision: 0.8701
Recall: 0.67
F1: 0.7571

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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 32
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.8403	0.0054	5	0.6496	0.6315	0.3113	0.47	0.3745
0.7673	0.0109	10	0.6456	0.6385	0.3176	0.47	0.3790
0.8495	0.0163	15	0.6429	0.6432	0.3194	0.46	0.3770
0.7675	0.0217	20	0.6363	0.6549	0.3333	0.47	0.3900
0.7347	0.0271	25	0.6235	0.6831	0.3529	0.42	0.3836
0.7087	0.0326	30	0.6053	0.7160	0.3846	0.35	0.3665
0.7302	0.0380	35	0.5893	0.7394	0.4247	0.31	0.3584
0.6417	0.0434	40	0.5743	0.7465	0.4259	0.23	0.2987
0.6635	0.0488	45	0.5677	0.7535	0.4419	0.19	0.2657
0.5966	0.0543	50	0.5625	0.7512	0.4167	0.15	0.2206
0.5228	0.0597	55	0.5590	0.7535	0.4286	0.15	0.2222
0.5335	0.0651	60	0.5517	0.7629	0.4815	0.13	0.2047
0.539	0.0705	65	0.5417	0.7653	0.5	0.13	0.2063
0.5672	0.0760	70	0.5224	0.7676	0.5161	0.16	0.2443
0.5947	0.0814	75	0.5087	0.7746	0.5556	0.2	0.2941
0.5674	0.0868	80	0.4976	0.7817	0.5854	0.24	0.3404
0.4785	0.0922	85	0.4872	0.7934	0.625	0.3	0.4054
0.4498	0.0977	90	0.4765	0.8075	0.6957	0.32	0.4384
0.5334	0.1031	95	0.4615	0.8099	0.7436	0.29	0.4173
0.5016	0.1085	100	0.4501	0.8216	0.8333	0.3	0.4412
0.5298	0.1139	105	0.4345	0.8357	0.8125	0.39	0.5270
0.4018	0.1194	110	0.4256	0.8498	0.8	0.48	0.6
0.4767	0.1248	115	0.4180	0.8638	0.7692	0.6	0.6742
0.3461	0.1302	120	0.4038	0.8615	0.7662	0.59	0.6667
0.4099	0.1356	125	0.3915	0.8638	0.7838	0.58	0.6667
0.4437	0.1411	130	0.3839	0.8615	0.7662	0.59	0.6667
0.5666	0.1465	135	0.3759	0.8638	0.7692	0.6	0.6742
0.4014	0.1519	140	0.3721	0.8615	0.7412	0.63	0.6811
0.481	0.1574	145	0.3649	0.8709	0.8	0.6	0.6857
0.4848	0.1628	150	0.3642	0.8662	0.7945	0.58	0.6705
0.3549	0.1682	155	0.3610	0.8685	0.7895	0.6	0.6818
0.4809	0.1736	160	0.3606	0.8638	0.7692	0.6	0.6742
0.4665	0.1791	165	0.3536	0.8615	0.7662	0.59	0.6667
0.4774	0.1845	170	0.3541	0.8568	0.7468	0.59	0.6592
0.3454	0.1899	175	0.3516	0.8592	0.7439	0.61	0.6703
0.3102	0.1953	180	0.3500	0.8638	0.7692	0.6	0.6742
0.2953	0.2008	185	0.3473	0.8662	0.8028	0.57	0.6667
0.4097	0.2062	190	0.3501	0.8709	0.8169	0.58	0.6784
0.3379	0.2116	195	0.3466	0.8638	0.7625	0.61	0.6778
0.3081	0.2170	200	0.3466	0.8662	0.7792	0.6	0.6780
0.3262	0.2225	205	0.3493	0.8685	0.7821	0.61	0.6854
0.3463	0.2279	210	0.3461	0.8662	0.7529	0.64	0.6919
0.402	0.2333	215	0.3423	0.8662	0.7416	0.66	0.6984
0.3456	0.2387	220	0.3401	0.8709	0.7778	0.63	0.6961
0.3949	0.2442	225	0.3384	0.8826	0.8378	0.62	0.7126
0.4872	0.2496	230	0.3386	0.8826	0.8205	0.64	0.7191
0.3817	0.2550	235	0.3359	0.8803	0.8101	0.64	0.7151
0.3366	0.2604	240	0.3328	0.8803	0.8101	0.64	0.7151
0.3824	0.2659	245	0.3286	0.8803	0.8182	0.63	0.7119
0.325	0.2713	250	0.3290	0.8732	0.7875	0.63	0.7
0.2943	0.2767	255	0.3264	0.8685	0.7821	0.61	0.6854
0.3434	0.2821	260	0.3275	0.8732	0.7875	0.63	0.7
0.3224	0.2876	265	0.3280	0.8779	0.7857	0.66	0.7174
0.3386	0.2930	270	0.3257	0.8826	0.8289	0.63	0.7159
0.304	0.2984	275	0.3238	0.8826	0.8125	0.65	0.7222
0.4793	0.3039	280	0.3250	0.8850	0.8228	0.65	0.7263
0.4138	0.3093	285	0.3238	0.8850	0.8312	0.64	0.7232
0.3852	0.3147	290	0.3237	0.8897	0.8272	0.67	0.7403
0.3196	0.3201	295	0.3240	0.8850	0.8	0.68	0.7351
0.4023	0.3256	300	0.3214	0.8897	0.8533	0.64	0.7314
0.3974	0.3310	305	0.3214	0.8944	0.8667	0.65	0.7429
0.2933	0.3364	310	0.3207	0.8967	0.85	0.68	0.7556
0.3391	0.3418	315	0.3151	0.8991	0.8608	0.68	0.7598
0.2682	0.3473	320	0.3155	0.9014	0.8452	0.71	0.7717
0.4006	0.3527	325	0.3179	0.8944	0.8090	0.72	0.7619
0.3129	0.3581	330	0.3144	0.8967	0.8415	0.69	0.7582
0.3386	0.3635	335	0.3114	0.8920	0.8553	0.65	0.7386
0.3413	0.3690	340	0.3116	0.8920	0.8649	0.64	0.7356
0.3122	0.3744	345	0.3118	0.8920	0.8553	0.65	0.7386
0.3871	0.3798	350	0.3110	0.8920	0.8375	0.67	0.7444
0.2153	0.3852	355	0.3101	0.8944	0.8481	0.67	0.7486
0.3732	0.3907	360	0.3098	0.8920	0.8553	0.65	0.7386
0.3858	0.3961	365	0.3082	0.8944	0.8395	0.68	0.7514
0.2806	0.4015	370	0.3079	0.8944	0.8767	0.64	0.7399
0.3634	0.4069	375	0.3081	0.8944	0.8571	0.66	0.7458
0.3554	0.4124	380	0.3134	0.8920	0.8375	0.67	0.7444
0.35	0.4178	385	0.3092	0.8944	0.8481	0.67	0.7486
0.369	0.4232	390	0.3090	0.8897	0.8841	0.61	0.7219
0.3476	0.4286	395	0.3047	0.8897	0.8630	0.63	0.7283
0.3525	0.4341	400	0.3073	0.8850	0.7802	0.71	0.7435
0.3312	0.4395	405	0.3042	0.8826	0.7907	0.68	0.7312
0.3379	0.4449	410	0.2998	0.8920	0.8375	0.67	0.7444
0.3113	0.4504	415	0.2999	0.9014	0.8816	0.67	0.7614
0.3772	0.4558	420	0.2991	0.8967	0.85	0.68	0.7556
0.3598	0.4612	425	0.3028	0.8873	0.8023	0.69	0.7419
0.3031	0.4666	430	0.2989	0.8967	0.8590	0.67	0.7528
0.3537	0.4721	435	0.2986	0.8991	0.8904	0.65	0.7514
0.3188	0.4775	440	0.2993	0.8991	0.8904	0.65	0.7514
0.2694	0.4829	445	0.2999	0.8991	0.88	0.66	0.7543
0.2925	0.4883	450	0.3000	0.8920	0.8857	0.62	0.7294
0.2575	0.4938	455	0.3008	0.8873	0.8714	0.61	0.7176
0.2553	0.4992	460	0.2954	0.8897	0.8533	0.64	0.7314
0.2947	0.5046	465	0.2991	0.8944	0.8235	0.7	0.7568
0.1847	0.5100	470	0.2949	0.8944	0.8667	0.65	0.7429
0.3747	0.5155	475	0.2963	0.8920	0.8649	0.64	0.7356
0.2153	0.5209	480	0.2954	0.8944	0.8667	0.65	0.7429
0.283	0.5263	485	0.2911	0.8967	0.85	0.68	0.7556
0.255	0.5317	490	0.2924	0.8944	0.8313	0.69	0.7541
0.3864	0.5372	495	0.2920	0.9014	0.8625	0.69	0.7667
0.3019	0.5426	500	0.2953	0.8920	0.8214	0.69	0.75
0.3855	0.5480	505	0.2941	0.8991	0.8608	0.68	0.7598
0.2762	0.5534	510	0.2938	0.8967	0.8784	0.65	0.7471
0.3806	0.5589	515	0.2970	0.8944	0.8767	0.64	0.7399
0.2644	0.5643	520	0.2951	0.8991	0.8904	0.65	0.7514
0.4142	0.5697	525	0.2952	0.9014	0.8625	0.69	0.7667
0.2897	0.5751	530	0.3044	0.8756	0.7474	0.71	0.7282
0.2989	0.5806	535	0.2940	0.8991	0.8701	0.67	0.7571
0.3252	0.5860	540	0.2947	0.9014	0.8919	0.66	0.7586
0.3944	0.5914	545	0.2938	0.8967	0.8590	0.67	0.7528
0.2853	0.5969	550	0.2991	0.8920	0.8293	0.68	0.7473
0.2416	0.6023	555	0.2970	0.8991	0.8701	0.67	0.7571
0.4176	0.6077	560	0.3034	0.8944	0.8767	0.64	0.7399
0.3137	0.6131	565	0.2977	0.8967	0.8784	0.65	0.7471
0.3712	0.6186	570	0.2952	0.9014	0.8625	0.69	0.7667
0.2587	0.6240	575	0.2931	0.8991	0.8701	0.67	0.7571
0.3369	0.6294	580	0.2975	0.8967	0.8889	0.64	0.7442
0.3566	0.6348	585	0.2960	0.8991	0.8904	0.65	0.7514
0.316	0.6403	590	0.2939	0.8967	0.8684	0.66	0.75
0.321	0.6457	595	0.2914	0.9014	0.8718	0.68	0.7640
0.3272	0.6511	600	0.2931	0.8991	0.8701	0.67	0.7571
0.4241	0.6565	605	0.2937	0.9014	0.8718	0.68	0.7640
0.2708	0.6620	610	0.2928	0.9014	0.8625	0.69	0.7667
0.3422	0.6674	615	0.2930	0.9038	0.8734	0.69	0.7709
0.2884	0.6728	620	0.2945	0.8967	0.8684	0.66	0.75
0.247	0.6782	625	0.2931	0.8967	0.8684	0.66	0.75
0.3017	0.6837	630	0.2915	0.8967	0.8684	0.66	0.75
0.301	0.6891	635	0.2890	0.8967	0.8684	0.66	0.75
0.4131	0.6945	640	0.2909	0.8967	0.8415	0.69	0.7582
0.3617	0.6999	645	0.2936	0.8991	0.8353	0.71	0.7676
0.3389	0.7054	650	0.2929	0.8967	0.8182	0.72	0.7660
0.3274	0.7108	655	0.2903	0.9061	0.8659	0.71	0.7802
0.295	0.7162	660	0.2898	0.8967	0.8684	0.66	0.75
0.1267	0.7216	665	0.2917	0.9014	0.8919	0.66	0.7586
0.3636	0.7271	670	0.2919	0.9038	0.9041	0.66	0.7630
0.3415	0.7325	675	0.2926	0.9038	0.9041	0.66	0.7630
0.3356	0.7379	680	0.2914	0.9014	0.8919	0.66	0.7586
0.2784	0.7434	685	0.2861	0.9014	0.8718	0.68	0.7640
0.3187	0.7488	690	0.2856	0.9014	0.8537	0.7	0.7692
0.249	0.7542	695	0.2854	0.9038	0.8554	0.71	0.7760
0.3047	0.7596	700	0.2856	0.9061	0.8571	0.72	0.7826
0.358	0.7651	705	0.2851	0.9014	0.8537	0.7	0.7692
0.3681	0.7705	710	0.2846	0.8991	0.8608	0.68	0.7598
0.2785	0.7759	715	0.2854	0.8944	0.8571	0.66	0.7458
0.2938	0.7813	720	0.2862	0.8967	0.8784	0.65	0.7471
0.322	0.7868	725	0.2866	0.8967	0.8784	0.65	0.7471
0.3773	0.7922	730	0.2845	0.8944	0.8571	0.66	0.7458
0.3124	0.7976	735	0.2857	0.8991	0.8608	0.68	0.7598
0.1688	0.8030	740	0.2866	0.8991	0.8519	0.69	0.7624
0.389	0.8085	745	0.2853	0.8944	0.8481	0.67	0.7486
0.2116	0.8139	750	0.2861	0.8967	0.8590	0.67	0.7528
0.2834	0.8193	755	0.2864	0.8967	0.8590	0.67	0.7528
0.2635	0.8247	760	0.2852	0.8991	0.8701	0.67	0.7571
0.3773	0.8302	765	0.2856	0.9014	0.8816	0.67	0.7614
0.2997	0.8356	770	0.2855	0.8991	0.8701	0.67	0.7571
0.3151	0.8410	775	0.2861	0.8991	0.8701	0.67	0.7571
0.3008	0.8464	780	0.2863	0.8991	0.88	0.66	0.7543
0.3162	0.8519	785	0.2852	0.8991	0.8701	0.67	0.7571
0.2638	0.8573	790	0.2848	0.9014	0.8816	0.67	0.7614
0.4227	0.8627	795	0.2849	0.9014	0.8816	0.67	0.7614
0.2635	0.8681	800	0.2856	0.8991	0.8701	0.67	0.7571
0.2781	0.8736	805	0.2841	0.8991	0.8701	0.67	0.7571
0.42	0.8790	810	0.2845	0.8991	0.8701	0.67	0.7571
0.4196	0.8844	815	0.2849	0.8991	0.8701	0.67	0.7571
0.2923	0.8899	820	0.2830	0.8991	0.8701	0.67	0.7571
0.2545	0.8953	825	0.2843	0.8967	0.8590	0.67	0.7528
0.4537	0.9007	830	0.2848	0.8991	0.8701	0.67	0.7571
0.2664	0.9061	835	0.2847	0.8991	0.8701	0.67	0.7571
0.292	0.9116	840	0.2842	0.8991	0.8701	0.67	0.7571
0.453	0.9170	845	0.2847	0.8967	0.8590	0.67	0.7528
0.3671	0.9224	850	0.2833	0.8991	0.8701	0.67	0.7571
0.2909	0.9278	855	0.2847	0.8967	0.8590	0.67	0.7528
0.3266	0.9333	860	0.2837	0.8944	0.8481	0.67	0.7486
0.2051	0.9387	865	0.2838	0.8991	0.8701	0.67	0.7571
0.2648	0.9441	870	0.2839	0.8991	0.8701	0.67	0.7571
0.3606	0.9495	875	0.2837	0.8991	0.8701	0.67	0.7571
0.2971	0.9550	880	0.2833	0.8991	0.8701	0.67	0.7571
0.282	0.9604	885	0.2826	0.8967	0.8590	0.67	0.7528
0.3994	0.9658	890	0.2838	0.8967	0.8590	0.67	0.7528
0.2317	0.9712	895	0.2845	0.8967	0.8590	0.67	0.7528
0.2784	0.9767	900	0.2836	0.8967	0.8590	0.67	0.7528
0.3679	0.9821	905	0.2828	0.8991	0.8701	0.67	0.7571
0.2623	0.9875	910	0.2839	0.8967	0.8590	0.67	0.7528
0.2232	0.9929	915	0.2838	0.8991	0.8701	0.67	0.7571
0.3868	0.9984	920	0.2838	0.8991	0.8701	0.67	0.7571

Framework versions

PEFT 0.12.0
Transformers 4.46.0
Pytorch 2.4.0+cu118
Datasets 3.0.0
Tokenizers 0.20.1

mtzig/v0_mistral_lora_last_n_train_3

v0_mistral_lora_last_n_train_3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mtzig/
v0_mistral_lora_last_n_train_3