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--- |
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license: apache-2.0 |
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base_model: alignment-handbook/zephyr-7b-sft-full |
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tags: |
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- trl |
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- ndcg |
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- generated_from_trainer |
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model-index: |
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- name: mistral-7b-base-dpo-best_with_worst-listsize_8-beta_0.01-batchsize_128 |
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zhaoyang1/huggingface/runs/qs6wlcwl) |
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# mistral-7b-base-dpo-best_with_worst-listsize_8-beta_0.01-batchsize_128 |
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This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6677 |
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- Logps: -337.5862 |
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- Logits: -2.6895 |
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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: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 16 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 16 |
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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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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Logps | Logits | |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:-------:| |
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| 0.6722 | 0.5343 | 250 | 0.6677 | -337.5862 | -2.6895 | |
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### Framework versions |
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- Transformers 4.42.0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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