magnum-4b-KTO-test
This model is a fine-tuned version of anthracite-org/magnum-v2-4b on the combined_new_22k.json dataset. It achieves the following results on the evaluation set:
- Loss: 0.5030
- Rewards/chosen: 0.0007
- Logps/chosen: -11.2857
- Rewards/rejected: -0.0006
- Logps/rejected: -10.6547
- Rewards/margins: 0.0013
- Kl: 0.0009
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-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 48
- total_train_batch_size: 768
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 2.0
Training results
Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Logps/chosen | Rewards/rejected | Logps/rejected | Rewards/margins | Kl |
---|---|---|---|---|---|---|---|---|---|
0.5042 | 0.2788 | 16 | 0.5038 | 0.0004 | -11.2884 | -0.0004 | -10.6529 | 0.0008 | 0.0022 |
0.5037 | 0.5575 | 32 | 0.5033 | 0.0006 | -11.2865 | -0.0008 | -10.6565 | 0.0014 | 0.0013 |
0.5035 | 0.8363 | 48 | 0.5041 | 0.0003 | -11.2899 | -0.0006 | -10.6546 | 0.0008 | 0.0016 |
0.5037 | 1.1151 | 64 | 0.5035 | 0.0005 | -11.2872 | -0.0005 | -10.6540 | 0.0011 | 0.0017 |
0.5036 | 1.3938 | 80 | 0.5036 | 0.0005 | -11.2874 | -0.0005 | -10.6535 | 0.0010 | 0.0010 |
0.5032 | 1.6726 | 96 | 0.5035 | 0.0006 | -11.2867 | -0.0005 | -10.6541 | 0.0011 | 0.0012 |
0.5036 | 1.9514 | 112 | 0.5037 | 0.0006 | -11.2869 | -0.0006 | -10.6546 | 0.0011 | 0.0009 |
Framework versions
- PEFT 0.12.0
- Transformers 4.45.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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