---
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-3_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-3_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: 1.2848

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3583        | 1.0   | 17   | 1.3383          |
| 1.1931        | 2.0   | 34   | 1.1611          |
| 0.9969        | 3.0   | 51   | 1.0146          |
| 0.8959        | 4.0   | 68   | 0.9699          |
| 0.8646        | 5.0   | 85   | 0.9506          |
| 0.8205        | 6.0   | 102  | 0.9429          |
| 0.7947        | 7.0   | 119  | 0.9492          |
| 0.7099        | 8.0   | 136  | 0.9749          |
| 0.6368        | 9.0   | 153  | 1.0093          |
| 0.6039        | 10.0  | 170  | 1.0973          |
| 0.5767        | 11.0  | 187  | 1.1243          |
| 0.4197        | 12.0  | 204  | 1.3017          |
| 0.4419        | 13.0  | 221  | 1.2848          |


### Framework versions

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