UTI_L3_1000steps_1e6rate_01beta_CSFTDPO
This model is a fine-tuned version of tsavage68/UTI_L3_1000steps_1e5rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0073
- Rewards/chosen: 1.2662
- Rewards/rejected: -9.9449
- Rewards/accuracies: 0.9900
- Rewards/margins: 11.2111
- Logps/rejected: -162.6436
- Logps/chosen: -19.8171
- Logits/rejected: -1.4769
- Logits/chosen: -1.4276
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: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6578 | 0.3333 | 25 | 0.5571 | 0.0522 | -0.2527 | 0.9800 | 0.3049 | -65.7218 | -31.9573 | -1.3240 | -1.3090 |
| 0.2092 | 0.6667 | 50 | 0.1456 | 0.3413 | -2.3069 | 0.9900 | 2.6482 | -86.2635 | -29.0656 | -1.3332 | -1.3147 |
| 0.0259 | 1.0 | 75 | 0.0237 | 0.8548 | -5.3505 | 0.9900 | 6.2052 | -116.6992 | -23.9313 | -1.3741 | -1.3452 |
| 0.022 | 1.3333 | 100 | 0.0121 | 1.0079 | -6.6623 | 0.9900 | 7.6702 | -129.8176 | -22.4003 | -1.4059 | -1.3679 |
| 0.0176 | 1.6667 | 125 | 0.0093 | 1.0152 | -8.2320 | 0.9900 | 9.2472 | -145.5143 | -22.3270 | -1.4249 | -1.3830 |
| 0.0408 | 2.0 | 150 | 0.0082 | 1.0301 | -8.5332 | 0.9900 | 9.5633 | -148.5271 | -22.1783 | -1.4531 | -1.4082 |
| 0.0001 | 2.3333 | 175 | 0.0076 | 1.0855 | -8.9116 | 0.9900 | 9.9971 | -152.3104 | -21.6238 | -1.4576 | -1.4119 |
| 0.0014 | 2.6667 | 200 | 0.0090 | 1.3134 | -8.7928 | 0.9900 | 10.1062 | -151.1230 | -19.3451 | -1.4637 | -1.4172 |
| 0.0001 | 3.0 | 225 | 0.0079 | 1.2850 | -9.0072 | 0.9900 | 10.2922 | -153.2668 | -19.6294 | -1.4656 | -1.4188 |
| 0.0174 | 3.3333 | 250 | 0.0078 | 1.2789 | -9.0808 | 0.9900 | 10.3597 | -154.0025 | -19.6896 | -1.4665 | -1.4195 |
| 0.0174 | 3.6667 | 275 | 0.0075 | 1.2500 | -9.1806 | 0.9900 | 10.4306 | -155.0005 | -19.9786 | -1.4670 | -1.4199 |
| 0.0347 | 4.0 | 300 | 0.0074 | 1.2492 | -9.2488 | 0.9900 | 10.4980 | -155.6828 | -19.9870 | -1.4679 | -1.4206 |
| 0.0 | 4.3333 | 325 | 0.0074 | 1.2533 | -9.2948 | 0.9900 | 10.5481 | -156.1422 | -19.9456 | -1.4686 | -1.4211 |
| 0.0174 | 4.6667 | 350 | 0.0074 | 1.2521 | -9.3471 | 0.9900 | 10.5992 | -156.6662 | -19.9582 | -1.4691 | -1.4216 |
| 0.0347 | 5.0 | 375 | 0.0074 | 1.2514 | -9.4059 | 0.9900 | 10.6573 | -157.2535 | -19.9646 | -1.4697 | -1.4219 |
| 0.0174 | 5.3333 | 400 | 0.0074 | 1.2576 | -9.4635 | 0.9900 | 10.7210 | -157.8293 | -19.9033 | -1.4706 | -1.4226 |
| 0.0347 | 5.6667 | 425 | 0.0074 | 1.2577 | -9.5119 | 0.9900 | 10.7696 | -158.3135 | -19.9019 | -1.4713 | -1.4232 |
| 0.0174 | 6.0 | 450 | 0.0074 | 1.2547 | -9.5713 | 0.9900 | 10.8260 | -158.9079 | -19.9318 | -1.4720 | -1.4237 |
| 0.0 | 6.3333 | 475 | 0.0073 | 1.2558 | -9.6179 | 0.9900 | 10.8737 | -159.3735 | -19.9212 | -1.4727 | -1.4243 |
| 0.0347 | 6.6667 | 500 | 0.0073 | 1.2630 | -9.6616 | 0.9900 | 10.9246 | -159.8107 | -19.8490 | -1.4733 | -1.4248 |
| 0.0 | 7.0 | 525 | 0.0073 | 1.2628 | -9.7158 | 0.9900 | 10.9785 | -160.3523 | -19.8515 | -1.4737 | -1.4249 |
| 0.0 | 7.3333 | 550 | 0.0073 | 1.2618 | -9.7475 | 0.9900 | 11.0093 | -160.6694 | -19.8605 | -1.4744 | -1.4256 |
| 0.0174 | 7.6667 | 575 | 0.0073 | 1.2616 | -9.7931 | 0.9900 | 11.0547 | -161.1260 | -19.8632 | -1.4749 | -1.4261 |
| 0.0347 | 8.0 | 600 | 0.0073 | 1.2639 | -9.8192 | 0.9900 | 11.0831 | -161.3868 | -19.8404 | -1.4755 | -1.4265 |
| 0.0347 | 8.3333 | 625 | 0.0073 | 1.2683 | -9.8350 | 0.9900 | 11.1033 | -161.5448 | -19.7963 | -1.4755 | -1.4265 |
| 0.0 | 8.6667 | 650 | 0.0073 | 1.2669 | -9.8617 | 0.9900 | 11.1286 | -161.8117 | -19.8096 | -1.4760 | -1.4269 |
| 0.0 | 9.0 | 675 | 0.0073 | 1.2665 | -9.8881 | 0.9900 | 11.1545 | -162.0752 | -19.8142 | -1.4762 | -1.4270 |
| 0.0173 | 9.3333 | 700 | 0.0073 | 1.2686 | -9.9036 | 0.9900 | 11.1722 | -162.2308 | -19.7932 | -1.4765 | -1.4272 |
| 0.0173 | 9.6667 | 725 | 0.0073 | 1.2674 | -9.9155 | 0.9900 | 11.1829 | -162.3497 | -19.8054 | -1.4767 | -1.4274 |
| 0.0 | 10.0 | 750 | 0.0073 | 1.2691 | -9.9274 | 0.9900 | 11.1965 | -162.4688 | -19.7879 | -1.4768 | -1.4274 |
| 0.0 | 10.3333 | 775 | 0.0073 | 1.2673 | -9.9329 | 0.9900 | 11.2002 | -162.5236 | -19.8057 | -1.4769 | -1.4275 |
| 0.0173 | 10.6667 | 800 | 0.0073 | 1.2685 | -9.9385 | 0.9900 | 11.2070 | -162.5801 | -19.7940 | -1.4769 | -1.4275 |
| 0.0173 | 11.0 | 825 | 0.0073 | 1.2682 | -9.9411 | 0.9900 | 11.2093 | -162.6058 | -19.7972 | -1.4769 | -1.4276 |
| 0.0 | 11.3333 | 850 | 0.0073 | 1.2669 | -9.9412 | 0.9900 | 11.2081 | -162.6066 | -19.8100 | -1.4770 | -1.4276 |
| 0.0347 | 11.6667 | 875 | 0.0073 | 1.2675 | -9.9425 | 0.9900 | 11.2101 | -162.6200 | -19.8038 | -1.4769 | -1.4276 |
| 0.0 | 12.0 | 900 | 0.0073 | 1.2671 | -9.9475 | 0.9900 | 11.2146 | -162.6696 | -19.8084 | -1.4770 | -1.4275 |
| 0.0173 | 12.3333 | 925 | 0.0073 | 1.2677 | -9.9478 | 0.9900 | 11.2155 | -162.6725 | -19.8017 | -1.4769 | -1.4275 |
| 0.0173 | 12.6667 | 950 | 0.0073 | 1.2664 | -9.9456 | 0.9900 | 11.2120 | -162.6507 | -19.8149 | -1.4769 | -1.4276 |
| 0.0347 | 13.0 | 975 | 0.0073 | 1.2662 | -9.9449 | 0.9900 | 11.2111 | -162.6436 | -19.8171 | -1.4769 | -1.4276 |
| 0.0 | 13.3333 | 1000 | 0.0073 | 1.2662 | -9.9449 | 0.9900 | 11.2111 | -162.6436 | -19.8171 | -1.4769 | -1.4276 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.0.0+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for tsavage68/UTI_L3_1000steps_1e6rate_01beta_CSFTDPO
Base model
meta-llama/Meta-Llama-3-8B-Instruct
Finetuned
tsavage68/UTI_L3_1000steps_1e5rate_SFT