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--- |
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base_model: argsearch/llama-7b-sft-float32 |
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tags: |
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- alignment-handbook |
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- trl |
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- dpo |
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- generated_from_trainer |
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- trl |
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- dpo |
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- generated_from_trainer |
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datasets: |
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- Dahoas/full-hh-rlhf |
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model-index: |
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- name: llama-7b-sft-DPO |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# llama-7b-sft-DPO |
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This model is a fine-tuned version of [argsearch/llama-7b-sft-float32](https://huggingface.co/argsearch/llama-7b-sft-float32) on the Dahoas/full-hh-rlhf dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6525 |
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- Rewards/chosen: 0.3315 |
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- Rewards/rejected: 0.1953 |
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- Rewards/accuracies: 0.6080 |
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- Rewards/margins: 0.1362 |
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- Logps/rejected: -633.3815 |
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- Logps/chosen: -690.5654 |
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- Logits/rejected: -1.9212 |
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- Logits/chosen: -1.9766 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-07 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| |
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| 0.6884 | 0.06 | 100 | 0.6886 | 0.0879 | 0.0774 | 0.5647 | 0.0105 | -645.1731 | -714.9250 | -2.7786 | -2.8754 | |
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| 0.6769 | 0.11 | 200 | 0.6809 | 0.2546 | 0.2194 | 0.5747 | 0.0352 | -630.9728 | -698.2556 | -2.6094 | -2.6971 | |
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| 0.6734 | 0.17 | 300 | 0.6755 | 0.2980 | 0.2471 | 0.5833 | 0.0508 | -628.1946 | -693.9142 | -2.5226 | -2.6062 | |
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| 0.6684 | 0.23 | 400 | 0.6713 | 0.3480 | 0.2822 | 0.5888 | 0.0658 | -624.6848 | -688.9108 | -2.4007 | -2.4782 | |
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| 0.6647 | 0.29 | 500 | 0.6671 | 0.3495 | 0.2706 | 0.6048 | 0.0789 | -625.8477 | -688.7593 | -2.3026 | -2.3749 | |
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| 0.6598 | 0.34 | 600 | 0.6636 | 0.3311 | 0.2429 | 0.6058 | 0.0882 | -628.6143 | -690.6030 | -2.1694 | -2.2345 | |
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| 0.6598 | 0.4 | 700 | 0.6606 | 0.2824 | 0.1853 | 0.6106 | 0.0971 | -634.3779 | -695.4718 | -1.9252 | -1.9781 | |
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| 0.6563 | 0.46 | 800 | 0.6585 | 0.3476 | 0.2374 | 0.6071 | 0.1102 | -629.1707 | -688.9521 | -2.0030 | -2.0599 | |
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| 0.6636 | 0.51 | 900 | 0.6572 | 0.3569 | 0.2427 | 0.6119 | 0.1142 | -628.6379 | -688.0209 | -1.9872 | -2.0440 | |
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| 0.6436 | 0.57 | 1000 | 0.6558 | 0.2921 | 0.1732 | 0.6096 | 0.1190 | -635.5912 | -694.4999 | -1.9618 | -2.0181 | |
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| 0.6759 | 0.63 | 1100 | 0.6548 | 0.3436 | 0.2165 | 0.6071 | 0.1272 | -631.2626 | -689.3489 | -1.9627 | -2.0198 | |
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| 0.6679 | 0.69 | 1200 | 0.6542 | 0.3533 | 0.2212 | 0.6077 | 0.1321 | -630.7878 | -688.3820 | -1.9058 | -1.9598 | |
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| 0.6358 | 0.74 | 1300 | 0.6533 | 0.3363 | 0.2036 | 0.6074 | 0.1327 | -632.5449 | -690.0779 | -1.9447 | -2.0015 | |
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| 0.6473 | 0.8 | 1400 | 0.6528 | 0.3378 | 0.2021 | 0.6080 | 0.1357 | -632.6981 | -689.9300 | -1.9072 | -1.9621 | |
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| 0.6447 | 0.86 | 1500 | 0.6526 | 0.3221 | 0.1869 | 0.6080 | 0.1352 | -634.2156 | -691.5005 | -1.9226 | -1.9781 | |
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| 0.6546 | 0.91 | 1600 | 0.6525 | 0.3303 | 0.1941 | 0.6074 | 0.1362 | -633.5018 | -690.6824 | -1.9134 | -1.9684 | |
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| 0.6725 | 0.97 | 1700 | 0.6525 | 0.3312 | 0.1950 | 0.6074 | 0.1363 | -633.4115 | -690.5892 | -1.9098 | -1.9645 | |
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### Framework versions |
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- Transformers 4.39.0.dev0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.14.6 |
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- Tokenizers 0.15.2 |
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