---
tags:
- generated_from_trainer
model-index:
- name: src_prober_codellama-13b-last1unfreeze
  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. -->

# src_prober_codellama-13b-last1unfreeze

This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6267

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.7443        | 0.12  | 500   | 0.7429          |
| 0.6851        | 0.24  | 1000  | 0.7170          |
| 0.6723        | 0.36  | 1500  | 0.6912          |
| 0.6605        | 0.48  | 2000  | 0.6730          |
| 0.6475        | 0.6   | 2500  | 0.6643          |
| 0.6419        | 0.72  | 3000  | 0.6584          |
| 0.6307        | 0.85  | 3500  | 0.6532          |
| 0.6167        | 0.97  | 4000  | 0.6495          |
| 0.6272        | 1.09  | 4500  | 0.6477          |
| 0.6002        | 1.21  | 5000  | 0.6445          |
| 0.6303        | 1.33  | 5500  | 0.6429          |
| 0.6405        | 1.45  | 6000  | 0.6421          |
| 0.6041        | 1.57  | 6500  | 0.6387          |
| 0.5912        | 1.69  | 7000  | 0.6370          |
| 0.6121        | 1.81  | 7500  | 0.6360          |
| 0.613         | 1.93  | 8000  | 0.6344          |
| 0.6126        | 2.05  | 8500  | 0.6338          |
| 0.5932        | 2.17  | 9000  | 0.6344          |
| 0.5927        | 2.3   | 9500  | 0.6332          |
| 0.5883        | 2.42  | 10000 | 0.6317          |
| 0.6023        | 2.54  | 10500 | 0.6308          |
| 0.5898        | 2.66  | 11000 | 0.6311          |
| 0.576         | 2.78  | 11500 | 0.6291          |
| 0.5699        | 2.9   | 12000 | 0.6291          |
| 0.6093        | 3.02  | 12500 | 0.6290          |
| 0.5754        | 3.14  | 13000 | 0.6292          |
| 0.6294        | 3.26  | 13500 | 0.6282          |
| 0.591         | 3.38  | 14000 | 0.6283          |
| 0.599         | 3.5   | 14500 | 0.6273          |
| 0.5933        | 3.62  | 15000 | 0.6281          |
| 0.565         | 3.75  | 15500 | 0.6268          |
| 0.5884        | 3.87  | 16000 | 0.6267          |
| 0.5809        | 3.99  | 16500 | 0.6266          |
| 0.5618        | 4.11  | 17000 | 0.6271          |
| 0.5749        | 4.23  | 17500 | 0.6274          |
| 0.577         | 4.35  | 18000 | 0.6268          |
| 0.5947        | 4.47  | 18500 | 0.6267          |
| 0.5902        | 4.59  | 19000 | 0.6268          |
| 0.5869        | 4.71  | 19500 | 0.6268          |
| 0.5829        | 4.83  | 20000 | 0.6268          |
| 0.5587        | 4.95  | 20500 | 0.6267          |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2