AlphaMonarch-daser / README.md
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metadata
license: cc-by-nc-4.0
base_model: mlabonne/NeuralMonarch-7B
tags:
  - generated_from_trainer
  - axolotl
  - mistral
  - instruct
  - finetune
  - chatml
  - gpt4
  - synthetic data
  - distillation
model-index:
  - name: AlphaMonarch-laser
    results: []
datasets:
  - argilla/OpenHermes2.5-dpo-binarized-alpha
language:
  - en
library_name: transformers
pipeline_tag: text-generation

AlphaMonarch-daser

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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:

πŸ† Evaluation results

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

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

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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