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--- |
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license: mit |
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library_name: peft |
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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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- jan-hq/distilabel_dpo_pairs_binarized |
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- argilla/OpenHermes2.5-dpo-binarized-alpha |
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- jan-hq/capybara_dpo_binarized |
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- jan-hq/bagel_dpo_binarized |
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- jan-hq/ultrafeedback_preferences_cleaned_binarized |
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- jan-hq/openmath_instruct_dpo_binarized |
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- jan-hq/distil_math_dpo_binarized |
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- jan-hq/evol_codealpaca_dpo_binarized |
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base_model: TomGrc/FusionNet_7Bx2_MoE_v0.1 |
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model-index: |
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- name: stealth-finance-v2-dpo-adapter |
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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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# stealth-finance-v2-dpo-adapter |
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This model is a fine-tuned version of [TomGrc/FusionNet_7Bx2_MoE_v0.1](https://huggingface.co/TomGrc/FusionNet_7Bx2_MoE_v0.1) on the jan-hq/distilabel_dpo_pairs_binarized, the argilla/OpenHermes2.5-dpo-binarized-alpha, the jan-hq/capybara_dpo_binarized, the jan-hq/bagel_dpo_binarized, the jan-hq/ultrafeedback_preferences_cleaned_binarized, the jan-hq/openmath_instruct_dpo_binarized, the jan-hq/distil_math_dpo_binarized and the jan-hq/evol_codealpaca_dpo_binarized datasets. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1290 |
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- Rewards/chosen: -0.1799 |
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- Rewards/rejected: -6.0696 |
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- Rewards/accuracies: 0.8597 |
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- Rewards/margins: 5.8897 |
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- Logps/rejected: -324.0384 |
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- Logps/chosen: -275.3572 |
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- Logits/rejected: -0.7749 |
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- Logits/chosen: -0.7773 |
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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-05 |
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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: 2 |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 2 |
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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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| 0.3593 | 1.0 | 3280 | 0.1290 | -0.1799 | -6.0696 | 0.8597 | 5.8897 | -324.0384 | -275.3572 | -0.7749 | -0.7773 | |
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### Framework versions |
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- PEFT 0.8.2 |
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- Transformers 4.37.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.14.6 |
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- Tokenizers 0.15.0 |
