File size: 4,025 Bytes
1783b44 c4a3702 1783b44 c4a3702 1783b44 c4a3702 1783b44 c4a3702 1783b44 c4a3702 1783b44 c4a3702 1783b44 c4a3702 1783b44 c4a3702 1783b44 c4a3702 1783b44 c4a3702 1783b44 c4a3702 1783b44 c4a3702 1783b44 c4a3702 1783b44 c4a3702 1783b44 c4a3702 1783b44 c4a3702 1783b44 c4a3702 1783b44 c4a3702 1783b44 c4a3702 1783b44 c4a3702 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
---
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
<!-- Provide a quick summary of what the model is/does. -->
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...
## 🏆 Evaluation results
# 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% |
VG = 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 |