Update README.md

README.md

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 license: apache-2.0
-language:
-
-en
 tags:
 
 text-generation
 
-fine-tuning
-
-bangkit
-
-agrease
-metrics:
-
-loss
-base_model: mistralai/Mistral-7B-Instruct-v0.3
-datasets:
+conversational
 
-custom-scraped
-model-index:
+mistral
 
-name: agrease-mistral-finetune
-results:
+fine-tuned
 
-task:
-type: text-generation
-dataset:
-type: custom-scraped
-name: Agrease Application Data
-metrics:
+chatbot
 
-type: loss
-value: 0.11
-name: Fine-Tuning Loss
-
-Fine-Tuning Mistral for the Agrease Application
-Author: Benito Yvan Deva Putra Arung Dirgantara
-Contact: benitodeva84@gmail.com
-Project: Bangkit Academy 2024 - Machine Learning Path
-
-1. Project Overview
-This project focuses on the fine-tuning of the Mistral v3 Large Language Model (LLM) to create a specialized model for the "Agrease" application. The primary objective was to adapt the general capabilities of the Mistral LLM to understand and process domain-specific data relevant to Agrease, enhancing its performance for tasks such as recommendation and data interpretation.
-
-This was developed as a capstone project during my participation in the Bangkit Academy 2024 Batch 2 program, under the Machine Learning learning path.
-
-2. Methodology
-The project followed a structured machine learning workflow, from data acquisition to model evaluation.
+bangkit
+widget:
 
-2.1. Data Collection
-To build a relevant dataset for fine-tuning, web scraping techniques were employed.
+text: "Hello! I'm looking for recommendations on agricultural products."
 
-Tools: BeautifulSoup and Scrapy were used to gather application data from various online marketplaces and sources.
+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.
 
-Process: The scrapers were designed to extract specific information required to train the model effectively for the Agrease application's context.
+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.
 
-2.2. Model Fine-Tuning
-The core of this project involved the fine-tuning process.
+Model Description
+Base Model: The model is a fine-tuned version of a Mistral v3 Large Language Model.
 
-Base Model: We used the pre-trained Mistral v3 as our foundation model.
+Fine-tuning Task: The model was fine-tuned for conversational question-answering and recommendations.
 
-Frameworks: The fine-tuning process was implemented using Python, with primary libraries being PyTorch and TensorFlow.
+Training Data: The training data was collected by scraping various online marketplaces using Python libraries such as BeautifulSoup and Scrapy.
 
-Objective: The goal was to train the model on our custom-scraped dataset, adjusting its weights to specialize its responses and understanding, while minimizing the training loss.
+Performance: The fine-tuning process achieved a final training loss of 11%.
 
-3. Results
-The fine-tuning process yielded significant improvements in the model's performance on domain-specific tasks.
+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.
 
-Fine-Tuned LLM: Achieved a final loss rate of 11%, indicating a successful adaptation of the model to the new data.
+How to Use
+You can use this model with the transformers library for text generation.
 
-Recommendation Model: As part of the broader Agrease application, a recommendation model was also developed, which achieved a 10% loss rate.
+from transformers import pipeline
 
-These results demonstrate the model's strong capability to serve the specific needs of the Agrease application.
+# 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')
 
-4. Technical Stack
-Programming Language: Python
+# Example prompt
+prompt = "What are the best fertilizers for rice paddies in a tropical climate?"
 
-ML/DL Frameworks: TensorFlow, PyTorch
+# Generate a response
+response = generator(prompt, max_length=150, num_return_sequences=1)
 
-Data Scraping: BeautifulSoup, Scrapy
+print(response[0]['generated_text'])
 
-Base LLM: Mistral v3
+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.
 
-5. Acknowledgements
-I would like to thank Google, GoTo, Traveloka, and the Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia for the opportunity to participate in the Bangkit Academy program. The skills and experience gained were invaluable in the successful completion of this project.
+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