74 lines
2.4 KiB
Markdown
74 lines
2.4 KiB
Markdown
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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://arxiv.org/abs/2406.08414)
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Read the [blog post on it here!](https://sakana.ai/llm-squared)
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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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