license: apache-2.0 tags:

text-generation

conversational

mistral

fine-tuned

chatbot

bangkit widget:

text: "Hello! I'm looking for recommendations on agricultural products."

Fine-Tuned Mistral Model for Agrease Application This repository contains a fine-tuned version of a Mistral Large Language Model, specifically adapted for the "Agrease" application. The model was developed as part of a capstone project for Bangkit Academy 2024 Batch 2.

The primary goal of this project was to create a specialized conversational AI capable of assisting users within the Agrease application, likely by providing information and recommendations based on data from various marketplaces.

Model Description Base Model: The model is a fine-tuned version of a Mistral v3 Large Language Model.

Fine-tuning Task: The model was fine-tuned for conversational question-answering and recommendations.

Training Data: The training data was collected by scraping various online marketplaces using Python libraries such as BeautifulSoup and Scrapy.

Performance: The fine-tuning process achieved a final training loss of 11%.

Intended Use This model is intended to be used as a chatbot or a conversational agent within a larger application. It can answer user queries, provide product recommendations, and engage in domain-specific conversations related to the "Agrease" application's scope.

How to Use You can use this model with the transformers library for text generation.

from transformers import pipeline

generator = pipeline('text-generation', model='your-username/model-name')

prompt = "What are the best fertilizers for rice paddies in a tropical climate?"

response = generator(prompt, max_length=150, num_return_sequences=1)

print(response[0]['generated_text'])

Training Data The dataset used for fine-tuning was created by scraping publicly available data from various e-commerce and marketplace websites. The scraping was performed using custom Python scripts with BeautifulSoup and Scrapy. The collected data was then processed and formatted into a conversational format suitable for training a large language model.

Training Procedure The fine-tuning was performed using the PyTorch framework on the collected dataset. The training focused on minimizing the cross-entropy loss to improve the model's ability to generate relevant and coherent responses in a conversational context. The final model achieved a training loss of 0.11