You will get Fine-Tuned LLM for Multi-Class Text Classification
Project details
Fine-tuned BERT model using PyTorch and Transformers → achieved 85% accuracy classifying 4 news categories
Preprocessed AG News dataset using stratified sampling → balanced class distribution preventing model bias
Tokenized 127,000+ headlines using bert-base-uncased → optimized input sequences with truncation/padding
Trained custom classification head with dropout and Adam optimizer → improved generalization on validation set
Preprocessed AG News dataset using stratified sampling → balanced class distribution preventing model bias
Tokenized 127,000+ headlines using bert-base-uncased → optimized input sequences with truncation/padding
Trained custom classification head with dropout and Adam optimizer → improved generalization on validation set
Machine Learning Tools
BERT, Deeplearning4j, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, Word2vecWhat's included
| Service Tiers |
Starter
$150
|
Standard
$250
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 15 days |
Number of Revisions | 1 | 5 | Unlimited |
Number of Model Variations | 1 | 3 | 5 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100 - $750About VishnuVardhan
AI/ML Engineer | Computer Vision & NLP Specialist
Hyderabad, India - 8:31 am local time
I build high-impact AI systems for real-world business outcomes not prototypes, demos, or academic experiments. My work ships at scale in high-stakes domains like healthcare, automotive, and customer support, with a focus on medical imaging, RAG/LLMs, and production-grade integration.
PROVEN RESULTS IN PRODUCTION:
98% Dice score on brain tumor segmentation (MRI) using U-Net, RAAGR2-Net, DeepLabV3, and SegFormer—from scratch in PyTorch (benchmarking for clinical accuracy).
95% accuracy car damage classifier deployed on VROOM Cars (production environment).
92% top-1 accuracy food classifier across 101 categories via ViT fine-tuning.
RAG chatbot that reduced support workload by 27% using PDF knowledge bases, ChromaDB, and FastAPI.
SERVICES
✅ AI Data Annotation & Labeling
MRI, video, and image annotation for computer vision pipelines — bounding boxes, semantic masks, and clinical labeling using LabelStudio, Roboflow, and CVAT. Medical-grade accuracy for production pipelines.
✅ Generative AI & LLM Engineering
Custom LLM fine-tuning, prompt engineering, RAG systems, and multimodal AI pipelines — engineered for business use, not experimentation.
✅ AI Integration & Deployment
Embedding AI into existing business workflows via FastAPI backends, REST APIs, and end-to-end deployment on AWS and Docker. Built for seamless adoption in live systems.
✅ Medical AI & Precision Segmentation
Tumor and lesion segmentation using U-Net, RAAGR2-Net, SegFormer, and DeepLabV3 — custom-trained for clinical accuracy in medical imaging workflows.
✅ Machine Learning & Deep Learning
Custom model architecture, supervised and unsupervised training, transfer learning, and model optimization using PyTorch and TensorFlow.
✅ Full POC-to-Production
From prototype to deployed product — PyTorch/TensorFlow → Docker/AWS. End-to-end delivery with full handoff support.
𝗟𝗘𝗧'𝗦 𝗖𝗢𝗟𝗟𝗔𝗕𝗢𝗥𝗔𝗧𝗘:
✅ Every deliverable includes 10 days of post-delivery support — no one-off fixes
✅ Available 50+ hours/week with a 4-hour response guarantee
✅ All systems are production-deployed, not notebook experiments
Send your project details and I will respond within 4 hours with a clear technical approach and timeline.
Steps for completing your project
After purchasing the project, send requirements so VishnuVardhan can start the project.
Delivery time starts when VishnuVardhan receives requirements from you.
VishnuVardhan works on your project following the steps below.
Revisions may occur after the delivery date.
Model Training & Validation
I’ll clean text, handle missing values, normalize and remove noise, then apply the BERT tokenizer with the agreed max length. I’ll show before/after samples and a short pipeline summary so you can review.