|
--- |
|
license: gemma |
|
library_name: peft |
|
tags: |
|
- trl |
|
- reward-trainer |
|
- generated_from_trainer |
|
base_model: google/gemma-2b |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: RM-HH-AllMix_harmless_gpt3_20000_gemma2b_shuffleTrue_extractchosenTrue |
|
results: [] |
|
--- |
|
|
|
<!-- 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-AllMix_harmless_gpt3_20000_gemma2b_shuffleTrue_extractchosenTrue |
|
|
|
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.5277 |
|
- Accuracy: 0.7168 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 0.8315 | 0.04 | 250 | 0.7587 | 0.5337 | |
|
| 0.7136 | 0.08 | 500 | 0.6394 | 0.6303 | |
|
| 0.6091 | 0.13 | 750 | 0.6058 | 0.6544 | |
|
| 0.6005 | 0.17 | 1000 | 0.5916 | 0.6634 | |
|
| 0.5844 | 0.21 | 1250 | 0.5839 | 0.6743 | |
|
| 0.5823 | 0.25 | 1500 | 0.5729 | 0.6796 | |
|
| 0.5845 | 0.29 | 1750 | 0.5629 | 0.6815 | |
|
| 0.5726 | 0.33 | 2000 | 0.5599 | 0.6833 | |
|
| 0.5564 | 0.38 | 2250 | 0.5675 | 0.6886 | |
|
| 0.5681 | 0.42 | 2500 | 0.5550 | 0.6912 | |
|
| 0.5713 | 0.46 | 2750 | 0.5367 | 0.6897 | |
|
| 0.5403 | 0.5 | 3000 | 0.5392 | 0.6980 | |
|
| 0.5299 | 0.54 | 3250 | 0.5502 | 0.7029 | |
|
| 0.5397 | 0.59 | 3500 | 0.5411 | 0.7025 | |
|
| 0.5629 | 0.63 | 3750 | 0.5377 | 0.7048 | |
|
| 0.5307 | 0.67 | 4000 | 0.5290 | 0.7119 | |
|
| 0.5154 | 0.71 | 4250 | 0.5322 | 0.7104 | |
|
| 0.5307 | 0.75 | 4500 | 0.5363 | 0.7123 | |
|
| 0.5414 | 0.79 | 4750 | 0.5320 | 0.7161 | |
|
| 0.5444 | 0.84 | 5000 | 0.5269 | 0.7194 | |
|
| 0.4831 | 0.88 | 5250 | 0.5325 | 0.7183 | |
|
| 0.528 | 0.92 | 5500 | 0.5281 | 0.7187 | |
|
| 0.527 | 0.96 | 5750 | 0.5277 | 0.7168 | |
|
|
|
|
|
### Framework versions |
|
|
|
- PEFT 0.10.0 |
|
- Transformers 4.38.2 |
|
- Pytorch 2.1.2+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
