DiscoPOP-zephyr-7b-gemma
This model is a fine-tuned version of 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"
Read the blog post on it here!
See the codebase to generate it here: https://github.com/SakanaAI/DiscoPOP
Model description
This model is identical in training to 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