Dataset creation & labelling
Collecting, cleaning, de-duplicating and labelling text, image and audio data, plus synthetic data where real data is scarce.
Model training · Fine-tuning
I build and train my own AI models
Most “AI developers” only call someone else’s API. I go a level deeper: I collect and clean datasets, train and fine-tune models myself, evaluate them honestly and deploy them on hardware you control.
That means an AI that speaks your domain’s language — your products, your documents, Hindi and Indian languages — instead of a generic model that guesses.
What my AI model training service includes
LLM fine-tuning
LoRA, QLoRA and full fine-tuning of open models such as Llama, Mistral and Qwen for your tasks and tone.
Vision model training
Custom detection, classification, OCR and segmentation models trained on your own images.
Speech & Indian-language models
Speech-to-text, intent and voice models adapted to Hindi and regional accents.
Evaluation & benchmarking
Test sets, accuracy metrics and side-by-side comparisons against cloud models before anything ships.
Optimised deployment
Quantisation (GGUF, AWQ), ONNX export and serving with vLLM or Ollama on GPU, CPU or edge devices.
Who it’s for
- →Companies that cannot send data to third-party AI APIs
- →Products that need a model in Hindi or a regional language
- →Cutting per-request costs of large cloud models
- →Domain experts: legal, medical, finance, education
- →Visual inspection and recognition on custom images
- →Startups that want their own model as a moat
Technology
PyTorchHugging Face TransformersPEFT / LoRAQLoRAUnslothAxolotlDeepSpeedLlamaMistralQwenWhisperYOLOWeights & BiasesGGUF / llama.cppvLLMOllamaONNX
How your AI model training project runs
- 01
Define the task
Exact inputs, outputs and the accuracy the model must reach.
- 02
Build the dataset
Collect, clean and label data; hold out a fair test set.
- 03
Train & iterate
Baselines first, then fine-tuning runs tracked and compared.
- 04
Ship & monitor
Quantised, served on your infrastructure and monitored for drift.
AI Model Training — frequently asked questions
Do you actually train models, or only use APIs like ChatGPT?+
Both, and I am clear about which one fits. I train and fine-tune open models myself — from preparing the dataset to the final checkpoint — and use cloud APIs only when they are the better choice for cost or accuracy.
How much data do I need to train a custom model?+
Less than most people think for fine-tuning: a few hundred to a few thousand good examples are often enough to teach a strong base model a specific task. Training from scratch needs far more, and is rarely necessary.
Where will the trained model run?+
Wherever you choose — your own GPU server, a cloud GPU, a normal CPU server after quantisation, or even on-device. The model weights are delivered to you.
Can you train a model for Hindi or other Indian languages?+
Yes. Indian-language text and speech is one of my focus areas, including mixed Hindi-English (Hinglish) inputs.
Planning your AI model training project?
Send a short message on WhatsApp — a rough idea is enough. You will get questions, a plan and a quote.