| 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 | |
| # Load the text generation pipeline from the Hugging Face Hub | |
| # Replace "your-username/model-name" with the actual model path | |
| generator = pipeline('text-generation', model='your-username/model-name') | |
| # Example prompt | |
| prompt = "What are the best fertilizers for rice paddies in a tropical climate?" | |
| # Generate a response | |
| 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 |