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--- |
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license: cc-by-nc-4.0 |
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base_model: mlabonne/NeuralMonarch-7B |
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tags: |
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- generated_from_trainer |
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- mistral |
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- instruct |
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- finetune |
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- chatml |
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- gpt4 |
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- synthetic data |
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- distillation |
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model-index: |
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- name: AlphaMonarch-dora |
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results: [] |
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datasets: |
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- argilla/OpenHermes2.5-dpo-binarized-alpha |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-generation |
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--- |
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# AlphaMonarch-dora |
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<!-- Provide a quick summary of what the model is/does. --> |
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AlphaMonarch-laser 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... |
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## 🏆 Evaluation results |
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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 | |
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|--------------------------------|---------|----------|-----------------|---------------------|-----------------------------| |
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| agieval_aqua_rat | 0 | 28.35% | 2.83% | 26.38% | 2.77% | |
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| agieval_logiqa_en | 0 | 38.71% | 1.91% | 38.25% | 1.90% | |
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| agieval_lsat_ar | 0 | 23.91% | 2.82% | 23.48% | 2.80% | |
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| agieval_lsat_lr | 0 | 52.55% | 2.21% | 53.73% | 2.21% | |
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| agieval_lsat_rc | 0 | 66.91% | 2.87% | 66.54% | 2.88% | |
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| agieval_sat_en | 0 | 78.64% | 2.86% | 78.64% | 2.86% | |
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| agieval_sat_en_without_passage | 0 | 45.15% | 3.48% | 44.17% | 3.47% | |
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| 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 | |
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|--------------|---------|----------|-----------------|---------------------|-----------------------------| |
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| arc_challenge| 0 | 65.87% | 1.39% | 67.92% | 1.36% | |
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| arc_easy | 0 | 86.49% | 0.70% | 80.64% | 0.81% | |
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| boolq | 1 | 87.16% | 0.59% | - | - | |
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| hellaswag | 0 | 69.86% | 0.46% | 87.51% | 0.33% | |
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| openbookqa | 0 | 39.00% | 2.18% | 49.20% | 2.24% | |
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| piqa | 0 | 83.03% | 0.88% | 84.82% | 0.84% | |
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| 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 | |
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|---------------|---------|--------------|---------------------|--------------|---------------------| |
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| truthfulqa_mc | 1 | 62.91% | 1.69% | 78.48% | 1.37% | |
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VG = 70.69 |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-7 |
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- train_batch_size: 2 |
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- eval_batch_size: Not specified |
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- seed: Not specified |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: Not specified |
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- optimizer: PagedAdamW with 32-bit precision |
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- lr_scheduler_type: Cosine |
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- lr_scheduler_warmup_steps: 100 |
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- training_steps: 1080 |
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### Framework versions |
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- Transformers 4.39.0.dev0 |
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- Peft 0.9.1.dev0 |
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- Datasets 2.18.0 |
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- torch 2.2.0 |
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- accelerate 0.27.2 |
