peft tensorboard safetensors generated_from_trainer base_model:codellama/CodeLlama-7b-Instruct-hf license:llama2 region:us
---
license: llama2
library_name: peft
tags:
- generated_from_trainer
base_model: codellama/CodeLlama-7b-Instruct-hf
model-index:
- name: vendata-train
  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. -->

# vendata-train

This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9052

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_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
- training_steps: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0633        | 0.1   | 10   | 1.0362          |
| 1.2685        | 0.2   | 20   | 0.9920          |
| 1.2542        | 0.3   | 30   | 0.9562          |
| 1.1031        | 0.4   | 40   | 0.9356          |
| 1.0196        | 0.5   | 50   | 0.9224          |
| 0.9397        | 0.6   | 60   | 0.9140          |
| 0.9485        | 0.7   | 70   | 0.9091          |
| 0.9506        | 0.8   | 80   | 0.9064          |
| 0.978         | 0.9   | 90   | 0.9054          |
| 1.0167        | 1.0   | 100  | 0.9052          |


### Framework versions

- PEFT 0.8.2
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0