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tsavage68_-_Transaminitis_L3_1000steps_1e5rate_05beta_CSFTDPO-8bits
RichardErkhov/tsavage68_-_Transaminitis_L3_1000steps_1e5rate_05beta_CSFTDPO-8bits
tsavage68_-_Transaminitis_L3_1000steps_1e5rate_05beta_CSFTDPO-8bits is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
Transaminitis_L3_1000steps_1e5rate_05beta_CSFTDPO - bnb 8bits
- Model creator: https://huggingface.co/tsavage68/
- Original model: https://huggingface.co/tsavage68/Transaminitis_L3_1000steps_1e5rate_05beta_CSFTDPO/
Original model description:
license: llama3 base_model: tsavage68/Transaminitis_L3_1000rate_1e7_SFT tags:
- trl
- dpo
- generated_from_trainer model-index:
- name: Transaminitis_L3_1000steps_1e5rate_05beta_CSFTDPO 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. -->
Transaminitis_L3_1000steps_1e5rate_05beta_CSFTDPO
This model is a fine-tuned version of tsavage68/Transaminitis_L3_1000rate_1e7_SFT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6914
- Rewards/chosen: -15.4983
- Rewards/rejected: -15.7754
- Rewards/accuracies: 0.3000
- Rewards/margins: 0.2771
- Logps/rejected: -50.1055
- Logps/chosen: -49.5308
- Logits/rejected: -0.7536
- Logits/chosen: -0.7536
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-05
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.3308 | 0.2 | 25 | 1.4218 | -5.1457 | -5.2961 | 0.5400 | 0.1503 | -29.1468 | -28.8257 | -0.7892 | -0.7880 |
| 1.1498 | 0.4 | 50 | 0.7304 | -4.8999 | -4.8425 | 0.4000 | -0.0574 | -28.2397 | -28.3340 | -2.1796 | -2.1797 |
| 1.2832 | 0.6 | 75 | 0.9255 | -1.6896 | -4.2819 | 0.6300 | 2.5923 | -27.1184 | -21.9133 | -1.0885 | -1.0850 |
| 2.8764 | 0.8 | 100 | 3.8444 | -19.0391 | -19.6042 | 0.5400 | 0.5651 | -57.7631 | -56.6124 | -0.1327 | -0.1327 |
| 0.8442 | 1.0 | 125 | 0.7901 | -16.2193 | -16.1877 | 0.5400 | -0.0316 | -50.9301 | -50.9727 | -0.7765 | -0.7765 |
| 0.7539 | 1.2 | 150 | 0.8102 | -15.9518 | -15.8097 | 0.4600 | -0.1421 | -50.1741 | -50.4379 | -0.9130 | -0.9130 |
| 0.7462 | 1.4 | 175 | 0.7415 | -16.1492 | -16.0632 | 0.4100 | -0.0860 | -50.6811 | -50.8325 | -0.8303 | -0.8304 |
| 0.7363 | 1.6 | 200 | 0.7404 | -16.2295 | -16.1487 | 0.4300 | -0.0808 | -50.8521 | -50.9933 | -0.8473 | -0.8473 |
| 0.7666 | 1.8 | 225 | 0.8203 | -16.1693 | -16.0294 | 0.4600 | -0.1399 | -50.6135 | -50.8729 | -0.9939 | -0.9939 |
| 0.7639 | 2.0 | 250 | 0.8115 | -16.1899 | -16.0490 | 0.4600 | -0.1409 | -50.6527 | -50.9140 | -0.8241 | -0.8241 |
| 0.7153 | 2.2 | 275 | 0.7477 | -16.3133 | -16.2548 | 0.5200 | -0.0585 | -51.0642 | -51.1609 | -0.7566 | -0.7566 |
| 0.8015 | 2.4 | 300 | 0.7461 | -16.9989 | -16.9443 | 0.5200 | -0.0546 | -52.4434 | -52.5321 | -0.7484 | -0.7484 |
| 0.7741 | 2.6 | 325 | 0.8205 | -16.7965 | -16.6632 | 0.4600 | -0.1333 | -51.8812 | -52.1273 | -0.8410 | -0.8410 |
| 0.8986 | 2.8 | 350 | 0.7380 | -18.5683 | -18.4872 | 0.3000 | -0.0811 | -55.5292 | -55.6709 | -1.2363 | -1.2363 |
| 0.849 | 3.0 | 375 | 2.3943 | -12.5963 | -12.1503 | 0.4600 | -0.4460 | -42.8553 | -43.7269 | -0.4070 | -0.4065 |
| 0.8088 | 3.2 | 400 | 0.7402 | -15.8638 | -15.7863 | 0.4600 | -0.0775 | -50.1272 | -50.2618 | -0.6327 | -0.6327 |
| 0.8743 | 3.4 | 425 | 0.7330 | -18.1568 | -18.0906 | 0.4100 | -0.0662 | -54.7359 | -54.8479 | -1.1648 | -1.1647 |
| 0.7984 | 3.6 | 450 | 0.7252 | -17.1837 | -17.1365 | 0.3300 | -0.0472 | -52.8276 | -52.9015 | -1.0496 | -1.0496 |
| 0.7968 | 3.8 | 475 | 0.8038 | -15.3963 | -15.3324 | 0.5400 | -0.0639 | -49.2195 | -49.3268 | -0.5901 | -0.5901 |
| 0.6856 | 4.0 | 500 | 0.7152 | -15.3527 | -15.4448 | 0.5300 | 0.0921 | -49.4443 | -49.2396 | -0.6386 | -0.6386 |
| 0.7167 | 4.2 | 525 | 0.7150 | -15.4946 | -15.5966 | 0.5100 | 0.1019 | -49.7478 | -49.5235 | -0.6307 | -0.6307 |
| 0.6039 | 4.4 | 550 | 0.7637 | -15.4627 | -15.6191 | 0.5400 | 0.1563 | -49.7928 | -49.4597 | -0.7779 | -0.7779 |
| 0.7734 | 4.6 | 575 | 0.7098 | -15.4720 | -15.6304 | 0.5300 | 0.1584 | -49.8155 | -49.4783 | -0.7391 | -0.7391 |
| 0.6561 | 4.8 | 600 | 0.7003 | -15.6141 | -15.8015 | 0.5100 | 0.1874 | -50.1577 | -49.7625 | -0.7691 | -0.7691 |
| 0.8328 | 5.0 | 625 | 0.6902 | -15.6776 | -15.8918 | 0.2800 | 0.2141 | -50.3382 | -49.8894 | -0.7913 | -0.7913 |
| 0.6256 | 5.2 | 650 | 0.6963 | -15.6139 | -15.8252 | 0.4800 | 0.2113 | -50.2051 | -49.7620 | -0.7909 | -0.7909 |
| 0.7336 | 5.4 | 675 | 0.7511 | -15.6031 | -15.7883 | 0.5400 | 0.1852 | -50.1313 | -49.7403 | -0.7741 | -0.7741 |
| 0.6527 | 5.6 | 700 | 0.7877 | -15.3869 | -15.6214 | 0.5400 | 0.2345 | -49.7974 | -49.3080 | -0.7426 | -0.7426 |
| 0.705 | 5.8 | 725 | 0.6894 | -15.4753 | -15.7539 | 0.2900 | 0.2786 | -50.0625 | -49.4848 | -0.7283 | -0.7283 |
| 0.7304 | 6.0 | 750 | 0.6899 | -15.4744 | -15.7563 | 0.2600 | 0.2819 | -50.0674 | -49.4830 | -0.7329 | -0.7329 |
| 0.7198 | 6.2 | 775 | 0.6920 | -15.5016 | -15.7713 | 0.3800 | 0.2697 | -50.0972 | -49.5374 | -0.7513 | -0.7513 |
| 0.7129 | 6.4 | 800 | 0.6908 | -15.5077 | -15.7810 | 0.3200 | 0.2733 | -50.1167 | -49.5497 | -0.7483 | -0.7483 |
| 0.6531 | 6.6 | 825 | 0.6900 | -15.4995 | -15.7803 | 0.2900 | 0.2807 | -50.1153 | -49.5333 | -0.7526 | -0.7526 |
| 0.7044 | 6.8 | 850 | 0.6918 | -15.4889 | -15.7660 | 0.3600 | 0.2771 | -50.0868 | -49.5121 | -0.7521 | -0.7520 |
| 0.6293 | 7.0 | 875 | 0.6914 | -15.4926 | -15.7693 | 0.3700 | 0.2766 | -50.0933 | -49.5195 | -0.7537 | -0.7537 |
| 0.7101 | 7.2 | 900 | 0.6905 | -15.4995 | -15.7785 | 0.2800 | 0.2789 | -50.1116 | -49.5333 | -0.7528 | -0.7528 |
| 0.6389 | 7.4 | 925 | 0.6913 | -15.4980 | -15.7753 | 0.3300 | 0.2772 | -50.1052 | -49.5303 | -0.7532 | -0.7532 |
| 0.6333 | 7.6 | 950 | 0.6907 | -15.4984 | -15.7771 | 0.3200 | 0.2786 | -50.1088 | -49.5310 | -0.7534 | -0.7534 |
| 0.6491 | 7.8 | 975 | 0.6912 | -15.4974 | -15.7749 | 0.3200 | 0.2775 | -50.1045 | -49.5291 | -0.7534 | -0.7534 |
| 0.6433 | 8.0 | 1000 | 0.6914 | -15.4983 | -15.7754 | 0.3000 | 0.2771 | -50.1055 | -49.5308 | -0.7536 | -0.7536 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.0.0+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
- Type
- llama
- Created
- Apr 3, 2025
- Updated
- Apr 3, 2025