RM-HH-Gemma_harmless_gpt3_20000_gemma2b_shuffleFalse_extractchosenFalse

Holarissun/RM-HH-Gemma_harmless_gpt3_20000_gemma2b_shuffleFalse_extractchosenFalse

RM-HH-Gemma_harmless_gpt3_20000_gemma2b_shuffleFalse_extractchosenFalse 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

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Hugging Face

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.json17.6 MB · 60%

At a glance

Library: peft
License: gemma
Access: Public
Created: Apr 19, 2024
Updated: Apr 19, 2024
SHA: c78b0b45

Base models

google/gemma-2b

Model card

From the Hugging Face model README

RM-HH-Gemma_harmless_gpt3_20000_gemma2b_shuffleFalse_extractchosenFalse

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

Loss: 0.0236
Accuracy: 0.9907

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: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5999	0.03	250	0.1410	0.9717
0.4197	0.06	500	0.0394	0.9882
0.4246	0.08	750	0.0347	0.9895
0.4616	0.11	1000	0.0345	0.9885
0.4141	0.14	1250	0.0308	0.9900
0.3989	0.17	1500	0.0311	0.9887
0.4122	0.19	1750	0.0299	0.9895
0.4106	0.22	2000	0.0298	0.9892
0.4657	0.25	2250	0.0270	0.9905
0.4311	0.28	2500	0.0304	0.9890
0.4474	0.31	2750	0.0277	0.9905
0.4202	0.33	3000	0.0293	0.9892
0.4487	0.36	3250	0.0287	0.9902
0.4219	0.39	3500	0.0257	0.9910
0.4525	0.42	3750	0.0264	0.9910
0.3805	0.44	4000	0.0277	0.9897
0.3824	0.47	4250	0.0241	0.9910
0.4217	0.5	4500	0.0235	0.9912
0.4275	0.53	4750	0.0259	0.9905
0.4395	0.56	5000	0.0247	0.9910
0.3848	0.58	5250	0.0250	0.9910
0.4297	0.61	5500	0.0249	0.9900
0.4167	0.64	5750	0.0258	0.9892
0.4205	0.67	6000	0.0244	0.9902
0.4072	0.69	6250	0.0264	0.9890
0.4033	0.72	6500	0.0253	0.9892
0.3699	0.75	6750	0.0244	0.9905
0.4101	0.78	7000	0.0259	0.9887
0.3969	0.81	7250	0.0249	0.9892
0.3845	0.83	7500	0.0236	0.9907
0.4208	0.86	7750	0.0232	0.9907
0.3925	0.89	8000	0.0232	0.9907
0.3769	0.92	8250	0.0231	0.9912
0.4323	0.94	8500	0.0232	0.9912
0.3999	0.97	8750	0.0236	0.9907

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 19, 2024
Updated: Apr 19, 2024