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mtzig/
v1_5_mistral_lora32

Name: v1_5_mistral_lora32
Availability: InStock

peft

safetensors

generated_from_trainer

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

adapter

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v1_5_mistral_lora32

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: 0.3153
Accuracy: 0.8614
Precision: 0.7273
Recall: 0.7547
F1: 0.7407

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: 765837
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	0.5958	0.7376	0.5	0.0660	0.1167
0.6819	0.0575	20	0.5694	0.7450	0.6154	0.0755	0.1345
0.4789	0.1149	40	0.4776	0.7550	0.5263	0.6604	0.5858
0.4108	0.1724	60	0.4043	0.8045	0.6286	0.6226	0.6256
0.3486	0.2299	80	0.3752	0.8366	0.6961	0.6698	0.6827
0.2936	0.2874	100	0.3650	0.8465	0.7115	0.6981	0.7048
0.3788	0.3448	120	0.3579	0.8465	0.6864	0.7642	0.7232
0.3918	0.4023	140	0.3483	0.8564	0.7449	0.6887	0.7157
0.3386	0.4598	160	0.3409	0.8614	0.7193	0.7736	0.7455
0.2864	0.5172	180	0.3257	0.8663	0.75	0.7358	0.7429
0.2581	0.5747	200	0.3223	0.8663	0.7407	0.7547	0.7477
0.3373	0.6322	220	0.3174	0.8663	0.75	0.7358	0.7429
0.3006	0.6897	240	0.3172	0.8564	0.7222	0.7358	0.7290
0.3157	0.7471	260	0.3143	0.8639	0.7339	0.7547	0.7442
0.291	0.8046	280	0.3137	0.8639	0.7339	0.7547	0.7442
0.2578	0.8621	300	0.3168	0.8639	0.7339	0.7547	0.7442
0.3223	0.9195	320	0.3157	0.8614	0.7273	0.7547	0.7407
0.3448	0.9770	340	0.3153	0.8614	0.7273	0.7547	0.7407

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_5_mistral_lora32

v1_5_mistral_lora32

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mtzig/
v1_5_mistral_lora32