---
library_name: peft
tags:
- generated_from_trainer
base_model: TheBloke/Llama-2-7B-fp16
model-index:
- name: Saiga_timelist_task30steps
  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. -->

# Saiga_timelist_task30steps

This model is a fine-tuned version of [TheBloke/Llama-2-7B-fp16](https://huggingface.co/TheBloke/Llama-2-7B-fp16) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0384

## 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: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2298        | 0.37  | 2    | 2.2027          |
| 2.0986        | 0.74  | 4    | 2.1505          |
| 2.0278        | 1.11  | 6    | 2.1167          |
| 1.9954        | 1.48  | 8    | 2.0915          |
| 1.9696        | 1.85  | 10   | 2.0753          |
| 1.8978        | 2.22  | 12   | 2.0648          |
| 1.9246        | 2.59  | 14   | 2.0564          |
| 1.9361        | 2.96  | 16   | 2.0506          |
| 1.895         | 3.33  | 18   | 2.0470          |
| 1.8525        | 3.7   | 20   | 2.0442          |
| 1.8912        | 4.07  | 22   | 2.0419          |
| 1.8689        | 4.44  | 24   | 2.0400          |
| 1.9054        | 4.81  | 26   | 2.0390          |
| 1.8537        | 5.19  | 28   | 2.0384          |
| 1.8501        | 5.56  | 30   | 2.0384          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2