|
--- |
|
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 |
|
--- |
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# AlphaMonarch-daser |
|
|
|
|
|
 |
|
|
|
AlphaMonarch-daser is a mixture of two techniques that are LaserQlora and Dora. This model 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. 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](https://huggingface.co/abideen/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: |
|
|
|
|
|
## 🏆 Evaluation results |
|
|
|
On YALL leaderboard: AlphaMonarch-daser > AlphaMonarch-dora > AlphaMonarch > AlphaMonarch-laser |
|
|
|
 |
|
|
|
On OpenLLM bench: AlphaMonarch-laser > AlphaMonarch > AlphaMonarch-daser > AlphaMonarch-dora |
|
|
|
 |
|
|
|
|
|
|
|
### 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 |
