---
license: mit
library_name: peft
tags:
- PEFT
- Qlora
- mistral-7b
- fine-tuning
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: mistral7b-fine-tuned-qlora
  results: []
datasets:
- timdettmers/openassistant-guanaco
pipeline_tag: text-generation
language:
- en
---

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# Mistral7b-fine-tuned-qlora

<img src="https://www.kdnuggets.com/wp-content/uploads/selvaraj_mistral_7bv02_finetuning_mistral_new_opensource_llm_hugging_face_3.png" alt="im" width="700" />

# Model version and Dataset

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on  [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset.

## Usage guidance

Please refer to [this notebook](https://github.com/shirinyamani/mistral7b-lora-finetuning/blob/main/misral_7B_updated.ipynb) for a complete demo including notes regarding cloud deployment

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 10
- mixed_precision_training: Native AMP


### Framework versions

- PEFT 0.11.2.dev0
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1