README.md

---
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

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64fc6d81d75293f417fee1d1/7xlnpalOC4qtu-VABsib4.jpeg)



<!-- 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.


## 🏆 Evaluation results

# OpenLLM Benchmark


![image/png](https://cdn-uploads.huggingface.co/production/uploads/64e380b2e12618b261fa6ba0/mVwB5NB0XcUwqharYhDGr.png)

# Nous Benchmark

Thanks to [Muhammad Bin Usman](https://www.linkedin.com/in/muhammad-bin-usman/) for evaluating AlphaMonarch-DoRA on the NOUS benchmark.

### AGIEVAL

| 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%                       |

AVG = 45.976

### GPT4ALL

| 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%           | -                   | -                           |

AVG = 73.18

### TRUTHFUL-QA

| 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