---
language:
  - en
license: apache-2.0
library_name: peft
tags:
  - text-generation-inference
datasets:
  - UrFavB0i/skincare-ecommerce-FAQ
pipeline_tag: text-generation
base_model: tiiuae/falcon-7b
---

# Model Card for Model ID

Falcon-7B Fine-Tuned Chatbot Model
This repository contains the fine-tuned Falcon-7B model for a chatbot application. The model has been fine-tuned using the PEFT method to provide robust responses for e-commerce customer support. It guides buyers in product selection, recommends sizes, checks product stock, suggests similar products, and presents reviews and social media video links.

## Model Details

- **Base Model**: Falcon 7B (tiiuae/falcon-7b)
- **Fine-Tuning Method**: Parameter-Efficient Fine-Tuning (PEFT)
- **Training Data** : Custom dataset including skincare e-commerce related dialogues. (UrFavB0i/skincare-ecommerce-FAQ)

### Features

- 24/7 customer support
- Product selection guidance
- Size recommendations
- Product stock checks
- Similar product suggestions
- Reviews and social media video link presentation

# Usage

## Installation

To use the model, you need to install the necessary dependencies. Make sure you have Python 3.7+ and pip installed.

```
pip install transformers
pip install peft
```

## Loading the Model

You can load the fine-tuned model using the transformers library:
```
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "your-huggingface-username/falcon-7b-chatbot"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
```

# Example usage
```
inputs = tokenizer("Hello, how can I assist you today?", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```

# Training Details

The model was fine-tuned using the PEFT method on a dataset specifically curated for e-commerce scenarios. The training process involved:

- **Data Preparation**: Gathering and preprocessing e-commerce-related dialogues.
- **Fine-Tuning**: Training the base model using PEFT to adapt it to the specific needs of the e-commerce domain.

# Evaluation

The fine-tuned model was evaluated based on its ability to handle various e-commerce related queries, providing accurate and contextually appropriate responses.

# Limitations

While the model performs well in many scenarios, it might not handle extremely rare or out-of-domain queries perfectly. Continuous training and updating with more data can help improve its performance further.

# Contributing

We welcome contributions to improve this model. If you have any suggestions or find any issues, please create an issue or a pull request.

# License

This project is licensed under the Apache 2.0 License. See the [LICENSE] file for more details.

# Acknowledgements

Special thanks to the Falcon team and the creators of the tiiuae/falcon-7b model for providing the base model and the tools necessary for fine-tuning.