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README.md

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+
+---
+
+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: []
+
+---
+
+![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
+
+# QuantFactory/DiscoPOP-zephyr-7b-gemma-GGUF
+This is quantized version of [SakanaAI/DiscoPOP-zephyr-7b-gemma](https://huggingface.co/SakanaAI/DiscoPOP-zephyr-7b-gemma) created using llama.cpp
+
+# Original Model Card
+
+
+# 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
+