AlphaMonarch-daser

AlphaMonarch-daser is a mixture of two techniques that are LaserQlora and Dora. This model is a DPO fine-tuned of mlabonne/NeuralMonarch-7B using the argilla/OpenHermes2.5-dpo-binarized-alpha preference dataset. I have fine-tuned this model only on half of the projections, but have achieved better results as compared to the version released AlphaMonarch-dora. I have trained this model for 1080 steps. Comparison of AlphaMonarch, AlphaMonarch-laser, AlphaMonarch-daser, and AlphaMonarch-dora on the OpenLLM leaderboard are:

🏆 Evaluation results

On YALL leaderboard: AlphaMonarch-daser > AlphaMonarch-dora > AlphaMonarch > AlphaMonarch-laser

On OpenLLM bench: AlphaMonarch-laser > AlphaMonarch > AlphaMonarch-daser > AlphaMonarch-dora

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-07
• train_batch_size: 1
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• training_steps: 1080

Framework versions

• Transformers 4.38.0.dev0
• Pytorch 2.1.2+cu118
• Datasets 2.17.0
• Tokenizers 0.15.0