---
license: gemma
base_model: HuggingFaceH4/zephyr-7b-gemma-sft-v0.1
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- argilla/dpo-mix-7k
model-index:
- name: DiscoPOP-zephyr-7b-gemma
  results: []
---

# DiscoPOP-zephyr-7b-gemma

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.

This model is from the paper ["Discovering Preference Optimization Algorithms with and for Large Language Models"](https://arxiv.org/abs/2406.08414)

Read the [blog post on it here!](https://sakana.ai/llm-squared)

See the codebase to generate it here: [https://github.com/SakanaAI/DiscoPOP](https://github.com/SakanaAI/DiscoPOP)

## Model description

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.

DiscoPOP is our Discovered Preference Optimization algorithm, which is defined as follows:

```
def log_ratio_modulated_loss(
    self,
    policy_chosen_logps: torch.FloatTensor,
    policy_rejected_logps: torch.FloatTensor,
    reference_chosen_logps: torch.FloatTensor,
    reference_rejected_logps: torch.FloatTensor,
) -> torch.FloatTensor:
    pi_logratios = policy_chosen_logps - policy_rejected_logps
    ref_logratios = reference_chosen_logps - reference_rejected_logps
    logits = pi_logratios - ref_logratios
    # Modulate the mixing coefficient based on the log ratio magnitudes
    log_ratio_modulation = torch.sigmoid(logits)
    logistic_component = -F.logsigmoid(self.beta * logits)
    exp_component = torch.exp(-self.beta * logits)
    # Blend between logistic and exponential component based on log ratio modulation
    losses = logistic_component * (1 - log_ratio_modulation) + exp_component * log_ratio_modulation
    return losses
```


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2

### Framework versions

- Transformers 4.40.1
- Pytorch 2.1.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1