You can deploy this model and start earning money today!

mtzig/
v1_mistral_lora

peft

safetensors

generated_from_trainer

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

adapter

Views

No views yet

v1_mistral_lora

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: 0.2947
Accuracy: 0.8899
Precision: 0.8933
Recall: 0.7910
F1: 0.8391

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 OptimizerNames.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
No log	0	0	0.6321	0.6480	0.5125	0.6119	0.5578
0.6598	0.0153	20	0.6284	0.6552	0.5221	0.5871	0.5527
0.6948	0.0306	40	0.6222	0.6787	0.5742	0.4428	0.5
0.6394	0.0459	60	0.6187	0.6877	0.6228	0.3532	0.4508
0.6466	0.0612	80	0.5946	0.7148	0.6257	0.5323	0.5753
0.5551	0.0765	100	0.5566	0.7256	0.6140	0.6567	0.6346
0.5631	0.0918	120	0.4903	0.7924	0.75	0.6418	0.6917
0.5009	0.1072	140	0.4552	0.7978	0.7432	0.6766	0.7083
0.4532	0.1225	160	0.4340	0.8267	0.8344	0.6517	0.7318
0.3813	0.1378	180	0.4414	0.8285	0.8630	0.6269	0.7262
0.3897	0.1531	200	0.4202	0.8394	0.8784	0.6468	0.7450
0.427	0.1684	220	0.4066	0.8430	0.8654	0.6716	0.7563
0.346	0.1837	240	0.4156	0.8339	0.7685	0.7761	0.7723
0.3623	0.1990	260	0.4000	0.8502	0.8734	0.6866	0.7688
0.3446	0.2143	280	0.3941	0.8520	0.8650	0.7015	0.7747
0.2533	0.2296	300	0.3808	0.8556	0.8954	0.6816	0.7740
0.3451	0.2449	320	0.3897	0.8357	0.7895	0.7463	0.7673
0.3667	0.2602	340	0.3895	0.8375	0.7761	0.7761	0.7761
0.3378	0.2755	360	0.3691	0.8592	0.8773	0.7114	0.7857
0.3216	0.2909	380	0.3751	0.8394	0.7947	0.7512	0.7724
0.3109	0.3062	400	0.3736	0.8538	0.8093	0.7811	0.7949
0.2893	0.3215	420	0.3466	0.8664	0.8802	0.7313	0.7989
0.3635	0.3368	440	0.3490	0.8610	0.8523	0.7463	0.7958
0.3582	0.3521	460	0.3370	0.8718	0.8824	0.7463	0.8086
0.3879	0.3674	480	0.3521	0.8556	0.7980	0.8060	0.8020
0.3741	0.3827	500	0.3298	0.8682	0.8810	0.7363	0.8022
0.3291	0.3980	520	0.3347	0.8628	0.8743	0.7264	0.7935
0.3697	0.4133	540	0.3236	0.8682	0.8636	0.7562	0.8064
0.3143	0.4286	560	0.3294	0.8628	0.8571	0.7463	0.7979
0.2442	0.4439	580	0.3167	0.8700	0.8909	0.7313	0.8033
0.361	0.4592	600	0.3247	0.8664	0.8360	0.7861	0.8103
0.3877	0.4746	620	0.3325	0.8700	0.8342	0.8010	0.8173
0.2342	0.4899	640	0.3178	0.8736	0.8659	0.7711	0.8158
0.2483	0.5052	660	0.3146	0.8718	0.8963	0.7313	0.8055
0.2841	0.5205	680	0.3226	0.8718	0.9167	0.7114	0.8011
0.3065	0.5358	700	0.3122	0.8845	0.9363	0.7313	0.8212
0.2231	0.5511	720	0.3075	0.8809	0.8689	0.7910	0.8281
0.2701	0.5664	740	0.3041	0.8809	0.8814	0.7761	0.8254
0.263	0.5817	760	0.3054	0.8773	0.8674	0.7811	0.8220
0.3769	0.5970	780	0.3036	0.8755	0.8708	0.7711	0.8179
0.184	0.6123	800	0.3055	0.8755	0.8511	0.7960	0.8226
0.3339	0.6276	820	0.3079	0.8773	0.8482	0.8060	0.8265
0.2078	0.6429	840	0.3000	0.8827	0.8736	0.7910	0.8303
0.3542	0.6582	860	0.3014	0.8827	0.8778	0.7861	0.8294
0.2316	0.6736	880	0.3074	0.8755	0.8587	0.7861	0.8208
0.2983	0.6889	900	0.3038	0.8809	0.8771	0.7811	0.8263
0.3039	0.7042	920	0.3024	0.8845	0.8870	0.7811	0.8307
0.311	0.7195	940	0.3016	0.8827	0.8820	0.7811	0.8285
0.406	0.7348	960	0.3040	0.8827	0.8617	0.8060	0.8329
0.2306	0.7501	980	0.2975	0.8863	0.9059	0.7662	0.8302
0.3494	0.7654	1000	0.3009	0.8863	0.875	0.8010	0.8364
0.3237	0.7807	1020	0.3034	0.8899	0.8723	0.8159	0.8432
0.4034	0.7960	1040	0.2988	0.8899	0.8977	0.7861	0.8382
0.2682	0.8113	1060	0.3001	0.8845	0.8663	0.8060	0.8351
0.2921	0.8266	1080	0.2982	0.8845	0.8785	0.7910	0.8325
0.3732	0.8419	1100	0.3003	0.8791	0.8564	0.8010	0.8278
0.324	0.8573	1120	0.2997	0.8845	0.8743	0.7960	0.8333
0.3607	0.8726	1140	0.2987	0.8827	0.8736	0.7910	0.8303
0.2201	0.8879	1160	0.2960	0.8881	0.8883	0.7910	0.8368
0.2767	0.9032	1180	0.2949	0.8899	0.8933	0.7910	0.8391
0.2563	0.9185	1200	0.2939	0.8899	0.8933	0.7910	0.8391
0.2681	0.9338	1220	0.2956	0.8899	0.8933	0.7910	0.8391
0.3409	0.9491	1240	0.2950	0.8881	0.8883	0.7910	0.8368
0.3316	0.9644	1260	0.2939	0.8899	0.8933	0.7910	0.8391
0.1957	0.9797	1280	0.2946	0.8899	0.8933	0.7910	0.8391
0.2439	0.9950	1300	0.2947	0.8899	0.8933	0.7910	0.8391

Framework versions

PEFT 0.13.2
Transformers 4.46.0
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.20.3

mtzig/v1_mistral_lora

v1_mistral_lora

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mtzig/
v1_mistral_lora