---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: TheBloke/zephyr-7B-alpha-GPTQ
model-index:
- name: zephyr-support-chatbot
  results: []
datasets:
- bitext/Bitext-customer-support-llm-chatbot-training-dataset
pipeline_tag: text-generation
---

# zephyr-7B-alpha-GPTQ

## Model description

Large language models have achieved groundbreaking success in the field of natural language processing (NLP). However, since these models are generally trained for general-purpose tasks, they may not perform optimally for specific tasks. Therefore, fine-tuning these large models for specific tasks is a common practice. In this article, we will delve into the process of fine-tuning and adapting the Zephyr-7B-alpha-GPTQ, a large language model, for a particular task.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP

### Framework versions

- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

### Author

- Anezatra Katedram