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Fiverr Pro
English
$
USD
I will build machine learning models and llms for your data
AI developer, Full Stack Developer, AI integrations, RAG, LLM, ML, AI Agent
I build production-ready AI systems and scalable web applications. With 10+ years of experience leading a technical team, I help businesses move past the "chatbot" phase and into deploying professiona...
About this Gig
If GPT-4 doesn't know your industry, your terminology, or your specific data it will hallucinate. Fine-tuning fixes that. I train and fine-tune LLMs and ML models on YOUR data so they think, respond, and classify exactly the way your business requires.
From predictive analytics to custom NLP classifiers to fine-tuned Llama I handle the full ML pipeline: data prep, training, evaluation, and deployment.
WHY CLIENTS CHOOSE ME:
I fine-tune on YOUR data not generic templates
Full pipeline: data prep training deployment
Rigorous evaluation with real metrics before delivery
You own the model weights completely
WHAT I BUILD:
Fine-tuned LLMs: Llama 3, Mistral, GPT (LoRA/QLoRA)
Custom NLP: classification, NER, summarization
Sentiment analysis & text classification pipelines
Predictive analytics & forecasting models
Recommendation systems
Computer vision: image classification & detection
Anomaly detection for fraud & business use cases
ML pipelines with automated retraining
FULL ML DELIVERY
Data preprocessing & feature engineering
Model training on your dataset (GPU cloud)
Evaluation: accuracy, F1, BLEU, ROUGE metrics
API deployment via FastAPI or HuggingFace Spaces Model monitoring
My Portfolio
FAQ
What data do I need to provide for fine-tuning?
For LLM fine-tuning, you need a dataset of input-output pairs relevant to your task — for example, question-answer pairs, instruction-response pairs, or text classification examples. Minimum recommended: 500–1,000 examples for basic fine-tuning, 5,000+ for strong performance. I can help you structur
What is LoRA/QLoRA and why does it matter?
LoRA (Low-Rank Adaptation) and QLoRA (Quantized LoRA) are efficient fine-tuning techniques that adapt a large pre-trained model to your data using a fraction of the compute and cost of full fine-tuning. QLoRA in particular allows fine-tuning 7B–70B parameter models on consumer or cloud GPUs at low
Which base model should I choose?
Llama 3 (8B or 70B) is the best open-source choice for most tasks — strong performance, fully open weights, no licensing fees. Mistral 7B is excellent for lower compute budgets. GPT fine-tuning via OpenAI API is available for simpler classification tasks. I'll recommend the right model after reviewi
Do I own the fine-tuned model weights?
Yes — 100%. I deliver the model weights, training scripts, and evaluation results. The model is yours to deploy, modify, or distribute as you choose.
How do you evaluate whether the model is good?
Before delivery, I run rigorous evaluation using standard metrics: accuracy and F1 for classification, BLEU/ROUGE for generation tasks, and custom benchmarks built from held-out examples of your own data. You receive a full evaluation report with the delivery.
Can the model be updated with new data later?
Yes — I build the training pipeline so you can retrain or further fine-tune the model as your data grows. Premium includes automated retraining setup with monitoring for model drift.
