---
tags:
- generated_from_trainer
model-index:
- name: src_prober_codellama-7b-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-7b-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: 0.6729

## 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: 8
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 128
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.7634        | 0.24  | 500   | 0.7745          |
| 0.7376        | 0.48  | 1000  | 0.7399          |
| 0.7006        | 0.72  | 1500  | 0.7138          |
| 0.6721        | 0.97  | 2000  | 0.7015          |
| 0.6753        | 1.21  | 2500  | 0.6941          |
| 0.6716        | 1.45  | 3000  | 0.6894          |
| 0.6595        | 1.69  | 3500  | 0.6865          |
| 0.6743        | 1.93  | 4000  | 0.6848          |
| 0.6647        | 2.17  | 4500  | 0.6819          |
| 0.6721        | 2.42  | 5000  | 0.6797          |
| 0.6642        | 2.66  | 5500  | 0.6780          |
| 0.6653        | 2.9   | 6000  | 0.6764          |
| 0.643         | 3.14  | 6500  | 0.6756          |
| 0.6532        | 3.38  | 7000  | 0.6749          |
| 0.6299        | 3.62  | 7500  | 0.6745          |
| 0.6442        | 3.87  | 8000  | 0.6737          |
| 0.6347        | 4.11  | 8500  | 0.6733          |
| 0.6364        | 4.35  | 9000  | 0.6730          |
| 0.6456        | 4.59  | 9500  | 0.6728          |
| 0.6338        | 4.83  | 10000 | 0.6729          |


### Framework versions

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