README.md

---
license: gemma
library_name: transformers
tags:
- sft
- generated_from_trainer
base_model: google/gemma-7b
model-index:
- name: gemma_ft_quote
  results: []
pipeline_tag: text-generation
datasets:
- Abirate/english_quotes
language:
- en
widget:
- text: 'Quote: With great power comes'
  example_title: Example 1
- text: 'Quote: Hasta la vista baby'
  example_title: Example 2
- text: 'Quote: Elementary, my dear watson.'
  example_title: Example 3
---

<!-- 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. -->

# Gemma_ft_Quote

This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co./google/gemma-7b) on the [english quote](https://huggingface.co./datasets/Abirate/english_quotes) dataset using [LoRA](https://arxiv.org/abs/2106.09685).
It is based on the example provided by google [here](https://huggingface.co./google/gemma-7b/blob/main/examples/notebook_sft_peft.ipynb).
The notebook used to fine-tune the model can be found [here](https://colab.research.google.com/drive/1OMvXuK77X7yxofrhQHERUkrn3NZORXFp?usp=sharing)


## Model description

The model can complete popular quotes given to it and add the author of the quote. For example, Given the qoute below:

```
Quote: With great power comes
```

The model would complete the quote and add the author of the quote:

```
Quote: With great power comes great responsibility. Author: Ben Parker.
```

Given a complete Quoute the model would add the author:

```
Quote: I'll be back. Author: Arnold Schwarzenegger.
```

## Usage

The model can be used with [transformers](https://huggingface.co./docs/transformers/en/index) library. Here's an example of loading the model
in 4 bit quantization mode:

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

model_id = "Eteims/gemma_ft_quote"
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map="cuda:0")
```

This code would easily run in a free colab tier.

After loading the model you can use it for inference:

```python
text = "Quote: Elementary, my dear watson."
device = "cuda:0"
inputs = tokenizer(text, return_tensors="pt").to(device)

outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Training hyperparameters

The following hyperparameters were used during fine-tuning:
- 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 0.8.2
- Transformers 4.38.1
- Pytorch 2.3.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2