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reward_model
angelaupc/reward_model
reward_model is a machine learning model from angelaupc. 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 llama3.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- License
- llama3
- Access
- Public
- Created
- Aug 12, 2024
- Updated
- Aug 24, 2024
- SHA
- 08399bcc
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. -->
reward_model
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7036
- Accuracy: 0.5236
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7293 | 0.08 | 128 | 0.7252 | 0.4850 |
| 0.7412 | 0.15 | 256 | 0.6925 | 0.5386 |
| 0.7182 | 0.23 | 384 | 0.6954 | 0.5327 |
| 0.6997 | 0.3 | 512 | 0.6941 | 0.5277 |
| 0.7547 | 0.38 | 640 | 0.6959 | 0.5279 |
| 0.7123 | 0.45 | 768 | 0.6993 | 0.5252 |
| 0.7281 | 0.53 | 896 | 0.6962 | 0.5275 |
| 0.7169 | 0.6 | 1024 | 0.6986 | 0.5156 |
| 0.7244 | 0.68 | 1152 | 0.6981 | 0.5125 |
| 0.7199 | 0.75 | 1280 | 0.7000 | 0.5060 |
| 0.7311 | 0.83 | 1408 | 0.6959 | 0.5140 |
| 0.7123 | 0.9 | 1536 | 0.6956 | 0.5154 |
| 0.7344 | 0.98 | 1664 | 0.6970 | 0.5100 |
| 0.7105 | 1.05 | 1792 | 0.6933 | 0.5219 |
| 0.6947 | 1.13 | 1920 | 0.6944 | 0.5259 |
| 0.7261 | 1.21 | 2048 | 0.6960 | 0.5256 |
| 0.6997 | 1.28 | 2176 | 0.6974 | 0.5188 |
| 0.7442 | 1.36 | 2304 | 0.6960 | 0.5163 |
| 0.7004 | 1.43 | 2432 | 0.6987 | 0.5286 |
| 0.7089 | 1.51 | 2560 | 0.6982 | 0.5288 |
| 0.7142 | 1.58 | 2688 | 0.7014 | 0.5154 |
| 0.7364 | 1.66 | 2816 | 0.6997 | 0.5202 |
| 0.6915 | 1.73 | 2944 | 0.7043 | 0.5200 |
| 0.7322 | 1.81 | 3072 | 0.7037 | 0.5229 |
| 0.7524 | 1.88 | 3200 | 0.7019 | 0.5219 |
| 0.7192 | 1.96 | 3328 | 0.7036 | 0.5236 |
Framework versions
- PEFT 0.12.0
- Transformers 4.36.0
- Pytorch 2.2.0
- Datasets 2.20.0
- Tokenizers 0.15.2
- Library
- peft
- License
- llama3
- Created
- Aug 12, 2024
- Updated
- Aug 24, 2024