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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ library_name: peft
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+ tags:
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+ - trl
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+ - dpo
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+ - generated_from_trainer
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+ base_model: cognitivecomputations/dolphin-2.1-mistral-7b
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+ model-index:
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+ - name: gemma-dpo
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+ results: []
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+ ---
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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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+
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+ # gemma-dpo
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+
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+ This model is a fine-tuned version of [cognitivecomputations/dolphin-2.1-mistral-7b](https://huggingface.co/cognitivecomputations/dolphin-2.1-mistral-7b) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6646
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+ - Rewards/chosen: -9.3932
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+ - Rewards/rejected: -13.4385
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+ - Rewards/accuracies: 0.7852
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+ - Rewards/margins: 4.0453
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+ - Logps/rejected: -440.0019
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+ - Logps/chosen: -450.6083
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+ - Logits/rejected: -2.5347
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+ - Logits/chosen: -2.6367
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 12
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+ - eval_batch_size: 4
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+ - seed: 42
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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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+
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+ ### Training results
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+
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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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+ | 0.7403 | 0.76 | 700 | 0.6646 | -9.3932 | -13.4385 | 0.7852 | 4.0453 | -440.0019 | -450.6083 | -2.5347 | -2.6367 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.9.0
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+ - Transformers 4.38.2
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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