v3d_mistral_lora

mtzig/v3d_mistral_lora

v3d_mistral_lora is a machine learning model from mtzig. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft.

should probably proofread and complete it, then remove this comment. --

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Hugging Face

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.distcp83.8 MB · 72%

At a glance

Library: peft
Access: Public
Created: Nov 30, 2024
Updated: Nov 30, 2024
SHA: cabe0ea6

Base models

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

Model card

From the Hugging Face model README

v3d_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: 0.3848
Accuracy: 0.8329
Precision: 0.7398
Recall: 0.4527
F1: 0.5617

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	0.6112	0.7506	0.3659	0.0746	0.1240
0.5265	0.0532	20	0.6081	0.7647	0.5455	0.0299	0.0566
0.4127	0.1064	40	0.5412	0.7682	0.7	0.0348	0.0664
0.3347	0.1596	60	0.5020	0.7741	0.7368	0.0697	0.1273
0.3077	0.2128	80	0.4463	0.7965	0.6944	0.2488	0.3663
0.307	0.2660	100	0.4498	0.8	0.6782	0.2935	0.4097
0.2413	0.3191	120	0.4316	0.8141	0.7087	0.3632	0.4803
0.326	0.3723	140	0.4107	0.8235	0.7257	0.4080	0.5223
0.2404	0.4255	160	0.4615	0.8094	0.7671	0.2786	0.4088
0.2962	0.4787	180	0.4205	0.8282	0.7619	0.3980	0.5229
0.2727	0.5319	200	0.4830	0.8	0.7627	0.2239	0.3462
0.2844	0.5851	220	0.4187	0.8259	0.7524	0.3930	0.5163
0.2632	0.6383	240	0.4037	0.8235	0.7339	0.3980	0.5161
0.2499	0.6915	260	0.3885	0.8247	0.7241	0.4179	0.5300
0.2121	0.7447	280	0.3953	0.8224	0.7232	0.4030	0.5176
0.2704	0.7979	300	0.3849	0.8329	0.736	0.4577	0.5644
0.2333	0.8511	320	0.3878	0.8318	0.7417	0.4428	0.5545
0.2896	0.9043	340	0.3886	0.8306	0.7395	0.4378	0.55
0.2398	0.9574	360	0.3848	0.8329	0.7398	0.4527	0.5617

Framework versions

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

Library: peft
Created: Nov 30, 2024
Updated: Nov 30, 2024