Downloads · 30 days
13
37% of all-time downloads
RM-HH-Gemma_harmless_human_20000_gemma2b
Holarissun/RM-HH-Gemma_harmless_human_20000_gemma2b
RM-HH-Gemma_harmless_human_20000_gemma2b 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. --
All-time downloads
35
Public
Repo size
29.1 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.json17.6 MB · 60%
At a glance
- Library
- peft
- License
- gemma
- Access
- Public
- Created
- Apr 19, 2024
- Updated
- Apr 19, 2024
- SHA
- 83532c70
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
RM-HH-Gemma_harmless_human_20000_gemma2b
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: 0.6196
- Accuracy: 0.6678
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 |
|---|---|---|---|---|
| 0.7635 | 0.06 | 250 | 0.7536 | 0.4972 |
| 0.7325 | 0.11 | 500 | 0.7235 | 0.5378 |
| 0.7285 | 0.17 | 750 | 0.7007 | 0.5673 |
| 0.7183 | 0.22 | 1000 | 0.6823 | 0.5973 |
| 0.6913 | 0.28 | 1250 | 0.6703 | 0.6113 |
| 0.6918 | 0.33 | 1500 | 0.6594 | 0.6263 |
| 0.6673 | 0.39 | 1750 | 0.6509 | 0.6368 |
| 0.6753 | 0.44 | 2000 | 0.6432 | 0.6398 |
| 0.6616 | 0.5 | 2250 | 0.6382 | 0.6503 |
| 0.672 | 0.56 | 2500 | 0.6336 | 0.6488 |
| 0.6457 | 0.61 | 2750 | 0.6308 | 0.6533 |
| 0.6705 | 0.67 | 3000 | 0.6276 | 0.6563 |
| 0.6534 | 0.72 | 3250 | 0.6247 | 0.6593 |
| 0.6176 | 0.78 | 3500 | 0.6228 | 0.6623 |
| 0.6597 | 0.83 | 3750 | 0.6210 | 0.6643 |
| 0.6404 | 0.89 | 4000 | 0.6201 | 0.6663 |
| 0.6199 | 0.94 | 4250 | 0.6196 | 0.6678 |
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