RM-HH-Gemma_helpful_human_20000_gemma2b_shuffleFalse_extractchosenTrue

Holarissun/RM-HH-Gemma_helpful_human_20000_gemma2b_shuffleFalse_extractchosenTrue

RM-HH-Gemma_helpful_human_20000_gemma2b_shuffleFalse_extractchosenTrue is a machine learning model from Holarissun. 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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Repo size

24.9 MB

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Hugging Face

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.json17.6 MB · 70%

At a glance

Library: peft
License: gemma
Access: Public
Created: Apr 24, 2024
Updated: Apr 24, 2024
SHA: 5d5cf0bd

Base models

google/gemma-2b

Model card

From the Hugging Face model README

RM-HH-Gemma_helpful_human_20000_gemma2b_shuffleFalse_extractchosenTrue

This model is a fine-tuned version of google/gemma-2b on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6203
Accuracy: 0.6600

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: 1.41e-05
train_batch_size: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.733	0.06	250	0.7295	0.5123
0.7034	0.11	500	0.7061	0.5498
0.7165	0.17	750	0.6913	0.5729
0.6821	0.22	1000	0.6809	0.5884
0.6707	0.28	1250	0.6744	0.6024
0.6551	0.33	1500	0.6692	0.6114
0.6744	0.39	1750	0.6617	0.6144
0.6418	0.45	2000	0.6591	0.6335
0.6546	0.5	2250	0.6567	0.6355
0.6465	0.56	2500	0.6537	0.6375
0.6489	0.61	2750	0.6471	0.6390
0.6555	0.67	3000	0.6399	0.6390
0.647	0.72	3250	0.6374	0.6460
0.6555	0.78	3500	0.6365	0.6490
0.6165	0.83	3750	0.6347	0.6465
0.6385	0.89	4000	0.6338	0.6485
0.6202	0.95	4250	0.6317	0.6490
0.6198	1.0	4500	0.6316	0.6520
0.6092	1.06	4750	0.6325	0.6515
0.6091	1.11	5000	0.6339	0.6510
0.605	1.17	5250	0.6338	0.6540
0.673	1.22	5500	0.6263	0.6550
0.6119	1.28	5750	0.6267	0.6565
0.6153	1.34	6000	0.6267	0.6580
0.6048	1.39	6250	0.6249	0.6560
0.62	1.45	6500	0.6228	0.6540
0.6213	1.5	6750	0.6234	0.6595
0.6107	1.56	7000	0.6228	0.6605
0.6266	1.61	7250	0.6212	0.6580
0.6088	1.67	7500	0.6211	0.6595
0.6282	1.72	7750	0.6210	0.6615
0.6384	1.78	8000	0.6197	0.6610
0.5987	1.84	8250	0.6198	0.6580
0.5911	1.89	8500	0.6201	0.6600
0.5981	1.95	8750	0.6203	0.6600

Framework versions

PEFT 0.9.0
Transformers 4.38.2
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2

Library: peft
License: gemma
Created: Apr 24, 2024
Updated: Apr 24, 2024