---
base_model: google/gemma-2-2b-it
library_name: peft
license: gemma
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Gemma-2-2B_task-1_180-samples_config-1_full
  results: []
---

<!-- 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. -->

# Gemma-2-2B_task-1_180-samples_config-1_full

This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0467

## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- 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_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.367         | 1.0   | 17   | 2.2712          |
| 1.5938        | 2.0   | 34   | 1.5141          |
| 1.1165        | 3.0   | 51   | 1.1452          |
| 0.957         | 4.0   | 68   | 1.0573          |
| 0.9136        | 5.0   | 85   | 1.0238          |
| 0.7999        | 6.0   | 102  | 1.0148          |
| 0.6625        | 7.0   | 119  | 1.0485          |
| 0.5317        | 8.0   | 136  | 1.1846          |
| 0.3318        | 9.0   | 153  | 1.4029          |
| 0.2543        | 10.0  | 170  | 1.6159          |
| 0.183         | 11.0  | 187  | 1.8190          |
| 0.1305        | 12.0  | 204  | 1.9711          |
| 0.1007        | 13.0  | 221  | 2.0467          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.1.2+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1