run_gemma-2-2b_20250507_215200

ethansandbar/run_gemma-2-2b_20250507_215200

run_gemma-2-2b_20250507_215200 is a machine learning model from ethansandbar. 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. The card lists the license as gemma.

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

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Hugging Face

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At a glance

Library: peft
License: gemma
Access: Public
Created: May 7, 2025
Updated: May 7, 2025
SHA: 674fcca6

Base models

google/gemma-2-2b

Model card

From the Hugging Face model README

run_gemma-2-2b_20250507_215200

This model is a fine-tuned version of google/gemma-2-2b on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3085
Accuracy: 0.9522
Precision General: 0.9810
Recall General: 0.9537
F1 General: 0.9671
Precision Memo: 0.8831
Recall Memo: 0.9444
F1 Memo: 0.9128
Precision Album: 0.8333
Recall Album: 1.0
F1 Album: 0.9091

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: 16
eval_batch_size: 8
seed: 42
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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision General	Recall General	F1 General	Precision Memo	Recall Memo	F1 Memo	Precision Album	Recall Album	F1 Album
0.3519	1.0	147	0.2828	0.9144	0.9481	0.9349	0.9415	0.8228	0.9028	0.8609	1.0	0.2	0.3333
0.2311	2.0	294	0.6490	0.8801	0.9592	0.8744	0.9148	0.7363	0.9306	0.8221	0.4	0.4	0.4
0.2362	3.0	441	0.2727	0.9349	0.9375	0.9767	0.9567	0.9254	0.8611	0.8921	1.0	0.2	0.3333
0.1502	4.0	588	0.4032	0.9212	0.9444	0.9488	0.9466	0.8514	0.875	0.8630	1.0	0.4	0.5714
0.0399	5.0	735	0.3743	0.9452	0.9543	0.9721	0.9631	0.9155	0.9028	0.9091	1.0	0.4	0.5714
0.0442	6.0	882	0.5794	0.9418	0.9420	0.9814	0.9613	0.9403	0.875	0.9065	1.0	0.2	0.3333
0.0285	7.0	1029	0.5223	0.9349	0.9455	0.9674	0.9563	0.9	0.875	0.8873	1.0	0.4	0.5714
0.0	8.0	1176	0.6654	0.9384	0.9378	0.9814	0.9591	0.9394	0.8611	0.8986	1.0	0.2	0.3333
0.0048	9.0	1323	0.5900	0.9349	0.9455	0.9674	0.9563	0.9	0.875	0.8873	1.0	0.4	0.5714
0.0014	10.0	1470	0.6443	0.9384	0.9457	0.9721	0.9587	0.9130	0.875	0.8936	1.0	0.4	0.5714
0.0	11.0	1617	0.6786	0.9384	0.9417	0.9767	0.9589	0.9265	0.875	0.9	1.0	0.2	0.3333
0.0	12.0	1764	0.6797	0.9384	0.9417	0.9767	0.9589	0.9265	0.875	0.9	1.0	0.2	0.3333
0.0	13.0	1911	0.6791	0.9384	0.9417	0.9767	0.9589	0.9265	0.875	0.9	1.0	0.2	0.3333
0.0	14.0	2058	0.6785	0.9384	0.9417	0.9767	0.9589	0.9265	0.875	0.9	1.0	0.2	0.3333
0.0	15.0	2205	0.6798	0.9384	0.9417	0.9767	0.9589	0.9265	0.875	0.9	1.0	0.2	0.3333

Framework versions

PEFT 0.15.0
Transformers 4.50.0.dev0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

Library: peft
License: gemma
Created: May 7, 2025
Updated: May 7, 2025