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--- |
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license: gemma |
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base_model: HuggingFaceH4/zephyr-7b-gemma-sft-v0.1 |
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tags: |
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- alignment-handbook |
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- generated_from_trainer |
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datasets: |
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- argilla/dpo-mix-7k |
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model-index: |
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- name: DiscoPOP-zephyr-7b-gemma |
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results: [] |
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--- |
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# DiscoPOP-zephyr-7b-gemma |
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This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-gemma-sft-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1) on the argilla/dpo-mix-7k dataset. |
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This model is from the paper ["Discovering Preference Optimization Algorithms with and for Large Language Models"](https://huggingface.co/SakanaAI/DiscoPOP-zephyr-7b-gemma/discussions) |
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Read the [blog post on it here!](https://sakana.ai/) |
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See the codebase to generate it here: [https://github.com/SakanaAI/DiscoPOP](https://github.com/SakanaAI/DiscoPOP) |
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## Model description |
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This model is identical in training to [HuggingFaceH4/zephyr-7b-gemma-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1), except instead of using Direct Preference Optimization (DPO), it uses DiscoPOP. |
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DiscoPOP is our Discovered Preference Optimization algorithm, which is defined as follows: |
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``` |
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def log_ratio_modulated_loss( |
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self, |
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policy_chosen_logps: torch.FloatTensor, |
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policy_rejected_logps: torch.FloatTensor, |
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reference_chosen_logps: torch.FloatTensor, |
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reference_rejected_logps: torch.FloatTensor, |
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) -> torch.FloatTensor: |
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pi_logratios = policy_chosen_logps - policy_rejected_logps |
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ref_logratios = reference_chosen_logps - reference_rejected_logps |
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logits = pi_logratios - ref_logratios |
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# Modulate the mixing coefficient based on the log ratio magnitudes |
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log_ratio_modulation = torch.sigmoid(logits) |
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logistic_component = -F.logsigmoid(self.beta * logits) |
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exp_component = torch.exp(-self.beta * logits) |
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# Blend between logistic and exponential component based on log ratio modulation |
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losses = logistic_component * (1 - log_ratio_modulation) + exp_component * log_ratio_modulation |
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return losses |
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``` |
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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: 2 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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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: 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: 2 |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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