| --- |
| license: cc-by-nc-4.0 |
| base_model: mlabonne/NeuralMonarch-7B |
| tags: |
| - generated_from_trainer |
| - mistral |
| - instruct |
| - finetune |
| - chatml |
| - gpt4 |
| - synthetic data |
| - distillation |
| model-index: |
| - name: AlphaMonarch-dora |
| results: [] |
| datasets: |
| - argilla/OpenHermes2.5-dpo-binarized-alpha |
| language: |
| - en |
| library_name: transformers |
| pipeline_tag: text-generation |
| --- |
| # AlphaMonarch-dora |
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| <!-- Provide a quick summary of what the model is/does. --> |
| AlphaMonarch-dora is a DPO fine-tuned of [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B/) using the [argilla/OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/argilla/OpenHermes2.5-dpo-binarized-alpha) preference dataset using DoRA. This model is slightly less performant on the Nous and Openllm leaderboards in comparison to base [AlphaMonarch](https://huggingface.co/mlabonne/AlphaMonarch-7B) and [AlphaMonarch-laser](https://huggingface.co/abideen/AlphaMonarch-laser). I have trained this model for 1080 steps. All hyperparams were kept consist across all these experiments. |
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| ## 🏆 Evaluation results |
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| # OpenLLM Benchmark |
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| # Nous Benchmark |
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| ### AGIEVAL |
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| | Task | Version | Accuracy | Accuracy StdErr | Normalized Accuracy | Normalized Accuracy StdErr | |
| |--------------------------------|---------|----------|-----------------|---------------------|-----------------------------| |
| | agieval_aqua_rat | 0 | 28.35% | 2.83% | 26.38% | 2.77% | |
| | agieval_logiqa_en | 0 | 38.71% | 1.91% | 38.25% | 1.90% | |
| | agieval_lsat_ar | 0 | 23.91% | 2.82% | 23.48% | 2.80% | |
| | agieval_lsat_lr | 0 | 52.55% | 2.21% | 53.73% | 2.21% | |
| | agieval_lsat_rc | 0 | 66.91% | 2.87% | 66.54% | 2.88% | |
| | agieval_sat_en | 0 | 78.64% | 2.86% | 78.64% | 2.86% | |
| | agieval_sat_en_without_passage | 0 | 45.15% | 3.48% | 44.17% | 3.47% | |
| | agieval_sat_math | 0 | 33.64% | 3.19% | 31.82% | 3.15% | |
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| AVG = 45.976 |
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| ### GPT4ALL |
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| | Task | Version | Accuracy | Accuracy StdErr | Normalized Accuracy | Normalized Accuracy StdErr | |
| |--------------|---------|----------|-----------------|---------------------|-----------------------------| |
| | arc_challenge| 0 | 65.87% | 1.39% | 67.92% | 1.36% | |
| | arc_easy | 0 | 86.49% | 0.70% | 80.64% | 0.81% | |
| | boolq | 1 | 87.16% | 0.59% | - | - | |
| | hellaswag | 0 | 69.86% | 0.46% | 87.51% | 0.33% | |
| | openbookqa | 0 | 39.00% | 2.18% | 49.20% | 2.24% | |
| | piqa | 0 | 83.03% | 0.88% | 84.82% | 0.84% | |
| | winogrande | 0 | 80.98% | 1.10% | - | - | |
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| AVG = 73.18 |
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| ### TRUTHFUL-QA |
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| | Task | Version | MC1 Accuracy | MC1 Accuracy StdErr | MC2 Accuracy | MC2 Accuracy StdErr | |
| |---------------|---------|--------------|---------------------|--------------|---------------------| |
| | truthfulqa_mc | 1 | 62.91% | 1.69% | 78.48% | 1.37% | |
| |
| AVG = 70.69 |
| |
| ### Training hyperparameters |
| The following hyperparameters were used during training: |
| - learning_rate: 5e-7 |
| - train_batch_size: 2 |
| - eval_batch_size: Not specified |
| - seed: Not specified |
| - gradient_accumulation_steps: 8 |
| - total_train_batch_size: Not specified |
| - optimizer: PagedAdamW with 32-bit precision |
| - lr_scheduler_type: Cosine |
| - lr_scheduler_warmup_steps: 100 |
| - training_steps: 1080 |
| ### Framework versions |
| - Transformers 4.39.0.dev0 |
| - Peft 0.9.1.dev0 |
| - Datasets 2.18.0 |
| - torch 2.2.0 |
| - accelerate 0.27.2 |
