Instructions to use Holarissun/RM-harmless_harmless_gpt3_loraR64_20000_gemma2b_lr1e-06_bs2_g4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Holarissun/RM-harmless_harmless_gpt3_loraR64_20000_gemma2b_lr1e-06_bs2_g4 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2b") model = PeftModel.from_pretrained(base_model, "Holarissun/RM-harmless_harmless_gpt3_loraR64_20000_gemma2b_lr1e-06_bs2_g4") - Notebooks
- Google Colab
- Kaggle
RM-harmless_harmless_gpt3_loraR64_20000_gemma2b_lr1e-06_bs2_g4
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: 0.1565
- Accuracy: 0.9725
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: 1e-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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 |
|---|---|---|---|---|
| 0.3519 | 1.0 | 2250 | 0.3257 | 0.89 |
| 0.1548 | 2.0 | 4500 | 0.1565 | 0.9725 |
Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for Holarissun/RM-harmless_harmless_gpt3_loraR64_20000_gemma2b_lr1e-06_bs2_g4
Base model
google/gemma-2b