Train and fine-tune AI models for specialized tasks.
Adapt modern language models using custom datasets, supervised fine-tuning, LoRA, QLoRA and evaluation workflows to create AI models better suited to your specific requirements.
Training Services
Custom AI model training from dataset to deployment.
Build a complete model-training pipeline around your data, target task, available hardware and production requirements.
LLM Fine-Tuning
Fine-tune open-source language models for specialized tasks, domains, response formats and business requirements.
LoRA Fine-Tuning
Use parameter-efficient LoRA techniques to adapt large language models while reducing training resources and costs.
QLoRA Training
Fine-tune quantized language models using QLoRA when GPU memory efficiency is an important requirement.
Dataset Preparation
Prepare, clean, transform and structure datasets for supervised fine-tuning and other model training workflows.
Model Evaluation
Evaluate trained models using task-specific datasets, benchmarks and custom metrics to understand model performance.
Model Deployment
Deploy trained models for inference through APIs, containers, GPU servers and production AI infrastructure.
Training Capabilities
Build a training pipeline around your data.
A successful fine-tuning project involves more than starting a training script. Dataset quality, model selection, configuration, evaluation and inference all matter.
Discuss Your Model →Fine-Tuning Approaches
Choose the right training strategy.
Different projects require different approaches. The training method can be selected based on the model, dataset, hardware and desired result.
Full Fine-Tuning
Update the model's parameters when the project justifies the larger compute and memory requirements.
LoRA
Train a smaller set of adapter parameters for more memory-efficient parameter adaptation.
QLoRA
Combine quantization with LoRA to reduce memory requirements for suitable fine-tuning workloads.
Use Cases
What can a custom-trained AI model do?
Fine-tuning can be useful when a pretrained model needs to adapt more closely to a specific task, domain or output format.
Domain-Specific AI Assistant
Adapt an open-source language model for a specialized domain, terminology or response style.
Business AI Models
Create models that better understand your organization's workflows, instructions and business-specific tasks.
Coding Models
Fine-tune models for specialized programming tasks, code generation, documentation or internal developer workflows.
Text Classification
Train models for classification, categorization, extraction and other structured language-processing tasks.
Custom Response Style
Adapt model behavior to follow specific response formats, tone, instructions and output requirements.
Specialized NLP
Develop models for domain-specific natural language processing and custom text understanding tasks.
AI Training Pipeline
From raw data to an AI model.
A structured training pipeline helps turn your data into a model that can be evaluated, optimized and integrated into a real application.
Dataset
Prepare quality training data
Tokenizer
Convert text into model inputs
Training
Fine-tune the model
Evaluation
Measure model performance
Inference
Deploy for real-world use
Technology Stack
Modern tools for AI model training.
Use the appropriate training frameworks and infrastructure based on the model architecture, dataset and available compute.
Training Process
From training idea to production model.
Every project starts with the task and data before selecting the appropriate model and training strategy.
Define the Objective
We identify exactly what the model should learn, what inputs it will receive and what the desired output should look like.
Prepare the Dataset
Training data is cleaned, structured and converted into the appropriate format for the selected training approach.
Select the Model
Choose an appropriate base model based on task requirements, model size, license, available GPU resources and expected performance.
Train & Fine-Tune
The model is trained using an appropriate strategy such as supervised fine-tuning, LoRA or QLoRA.
Evaluate Results
The trained model is evaluated against validation data and task-specific tests to identify quality improvements and weaknesses.
Deploy & Optimize
The final model can be optimized and deployed through a production inference API or your existing AI application.
Starting Price
AI model training from ₹20,000+
Final pricing depends on model size, dataset size, training strategy, GPU requirements, evaluation and deployment.
Custom Model Fine-Tuning
Suitable for LLM experiments, specialized models and task-specific fine-tuning.
- ✓ Dataset preparation
- ✓ Model configuration
- ✓ LoRA / QLoRA options
- ✓ Training setup
- ✓ Model evaluation
- ✓ Deployment support
Frequently Asked Questions
Questions about AI model training?
What is AI model fine-tuning?+
Fine-tuning adapts an existing pretrained model using additional task-specific data. Instead of training a large model completely from scratch, the existing model is further trained for a particular purpose.
What is LoRA fine-tuning?+
LoRA, or Low-Rank Adaptation, is a parameter-efficient fine-tuning technique that trains a smaller set of additional parameters instead of updating the entire model.
What is QLoRA?+
QLoRA combines quantization with LoRA-based fine-tuning to reduce memory requirements while adapting a language model.
Can you fine-tune an open-source LLM?+
Yes. Depending on the model's license, architecture and requirements, open-source language models can be fine-tuned using frameworks such as Transformers, PEFT and TRL.
Do I need a large GPU for model training?+
GPU requirements depend heavily on the model size, sequence length, batch size, quantization strategy and training method. LoRA and QLoRA can reduce memory requirements compared with full fine-tuning.
Can you prepare my training dataset?+
Yes. Dataset preparation can include cleaning, formatting, deduplication, instruction formatting, validation splits and conversion into a training-ready format.
Can you evaluate my trained model?+
Yes. Model evaluation can include validation datasets, task-specific test cases, benchmark comparisons and qualitative analysis of generated outputs.
Can you deploy the trained model?+
Yes. The model can be packaged for inference and deployed using APIs, Docker, Linux, GPU infrastructure and other production technologies depending on the project.
How much does AI model training cost?+
AI model training projects start from around ₹20,000 for smaller fine-tuning and experimentation projects. Larger models, datasets, GPU requirements and production deployment can require a custom quotation.
Have a model you want to train?
Tell me about your model, dataset and target task. We can plan the right fine-tuning and deployment approach for your project.