You will get Fine-Tune Large Language Models with LoRA for Custom Tasks
Project details
This project fine-tunes the Falcon-7B large language model using LoRA to predict product prices from textual descriptions with improved accuracy. By leveraging parameter-efficient fine-tuning, 4-bit quantization, and a weighted top-k prediction strategy, the model is effectively adapted for a numerical prediction task. The fine-tuned model shows a significant reduction in prediction error compared to the base model, demonstrating how large language models can be efficiently customized for real-world regression-style problems using limited computational resources.
AI Algorithms
Large Language Model, Multilayer Perceptron, Multimodal Large Language Model, Regression Analysis, Transformer ModelAI Applications
AI-Enhanced Classification, AI-Generated Code, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, PyTorch, StreamlitAI Models
BERT, BLOOM, GPT-Neo, LLaMAWhat's included
| Service Tiers |
Starter
$120
|
Standard
$250
|
Advanced
$450
|
|---|---|---|---|
| Delivery Time | 5 days | 8 days | 12 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | - | |
Detailed Code Comments | - | ||
Image Upscaling | - | - | |
MLOps | - | - | |
Model Deployment | - | - | |
Model Documentation | |||
Model Monitoring | - | ||
Model Testing & Optimization | |||
Model Tuning | |||
Natural Language Processing | |||
NLP Tokenization | - | ||
Pre-Training | - | - | |
Prompt Engineering | |||
Setup File | - | - | |
Source Code |
About Muhammad Fakhar
AI/ML Engineer
Taxila, Pakistan - 6:40 am local time
Steps for completing your project
After purchasing the project, send requirements so Muhammad Fakhar can start the project.
Delivery time starts when Muhammad Fakhar receives requirements from you.
Muhammad Fakhar works on your project following the steps below.
Revisions may occur after the delivery date.
Understand Requirements & Data
Review your objective, dataset, and constraints to ensure the fine-tuning approach matches your use case.
Prepare & Validate Dataset
Clean, format, and validate the data to ensure compatibility with the selected language model.