
Shows that you can memorize HumanEval using a LoRA adapter. Axolotl ignored the last two training samples, so achieves only 99.3% on HumanEval.

To use it, merge it with the `mistralai/Mistral-7B-v0.1` base model.

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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# lora-out

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9155

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- 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: 10
- num_epochs: 128

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1018        | 21.33 | 32   | 1.0007          |
| 0.017         | 42.67 | 64   | 1.7790          |
| 0.0199        | 64.0  | 96   | 1.9024          |
| 0.007         | 85.33 | 128  | 1.9155          |


### Framework versions

- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1

{"tags": ["generated_from_trainer"], "model-index": [{"name": "lora-out", "results": []}]}
transformers mistral text-generation generated_from_trainer autotrain_compatible endpoints_compatible text-generation-inference 8-bit region:us