LLM Fine-Tuning Services in India
Fine-tune LLMs with Aimodels.in. We offer LoRA and QLoRA fine-tuning, domain-specific training, RLHF and DPO alignment, multi-model fine-tuning, and evaluation for Indian businesses.
Service Overview
Fine-tuning is the fastest and most cost-effective way to make a general-purpose language model perform like a domain expert on your specific tasks. Aimodels.in provides comprehensive fine-tuning services that cover the full spectrum from lightweight LoRA and QLoRA adapters to full-parameter training, preference alignment with RLHF and DPO, and multi-model orchestration. Our team has fine-tuned models for legal analysis, medical documentation, financial reasoning, customer support, and code generation, and we know exactly which technique to apply for each use case, data size, and budget. We do not just run training scripts, we curate and clean your data, design instruction formats that match your task, run rigorous evaluation before and after fine-tuning, and deploy the resulting model with optimized inference so you see real performance gains in production. Whether you want to reduce hallucination on domain facts, improve response formatting, align models to human preferences, or cut inference costs by replacing expensive APIs with a fine-tuned open model, we tailor the fine-tuning strategy to your goals. Every engagement includes reproducible training pipelines, before-and-after benchmark reports, and deployment configurations that your engineering team can maintain and retrain as your data evolves.
How We Work — Our Process
A structured, transparent engagement model that ensures delivery quality at every step.
Task & Data Assessment
We analyze your target task, existing model performance, available training data, and quality standards to select the right fine-tuning approach and base model.
Week 1Data Curation & Formatting
We clean, deduplicate, and format your data into instruction-tuning datasets, preference pairs, or completion examples with quality filtering and validation splits.
Week 2Fine-Tuning Execution
We run fine-tuning with LoRA, QLoRA, or full-parameter methods using distributed training, checkpointing, and hyperparameter optimization for best results.
Weeks 3-4Preference Alignment
We apply RLHF or DPO alignment using curated preference data to improve helpfulness, safety, and response quality based on human or AI feedback.
Week 5Evaluation & Benchmarking
We run the fine-tuned model against domain benchmarks, baseline comparisons, safety tests, and custom evaluation suites to validate improvements.
Week 6Deployment & Handoff
We deploy the fine-tuned model with optimized inference, provide retraining pipelines, and train your team on maintaining and updating the model.
OngoingWhy Choose Us
Our key differentiators that set us apart in the AI services landscape.
Task-Specific Accuracy Gains
We fine-tune models on your task data and instruction format so they outperform generic APIs by 20-40% on domain accuracy, formatting, and instruction following.
Dramatic Cost Reduction
Fine-tuned open models deployed with optimized inference typically cost 60-80% less per token than commercial APIs while matching or exceeding accuracy.
Data Privacy Control
Fine-tuning and inference can run entirely in your cloud or on-premises, so sensitive training data and queries never reach third-party API providers.
Rigorous Evaluation
We benchmark before and after fine-tuning across 10+ metrics including domain accuracy, safety, latency, and comparison to your current API baseline.
Flexible Technique Selection
We apply LoRA, QLoRA, full-parameter, RLHF, or DPO based on your data, budget, and goals, not a one-size-fits-all approach.
Reproducible & Maintainable
Every fine-tuning run is versioned and reproducible, with retraining pipelines your team can run as data evolves, no black-box dependency on us.
What We Offer
Detailed breakdown of each offering within this service category.
LoRA & QLoRA Fine-Tuning
Parameter-efficient fine-tuning using LoRA and QLoRA to adapt large models quickly and cost-effectively with minimal GPU requirements and no quality compromise.
- LoRA or QLoRA adapter trained on your instruction dataset
- Merged model ready for deployment with optimized inference
- Training report with hyperparameters and evaluation scores
Domain-Specific Fine-Tuning
Full fine-tuning or continued pretraining on domain corpora to make models experts in your field, covering legal, medical, financial, technical, and custom domains.
- Domain corpus curation and preprocessing pipeline
- Fine-tuned model with domain vocabulary and reasoning
- Domain evaluation suite with 200+ test cases
RLHF & DPO Training
Preference alignment using Reinforcement Learning from Human Feedback or Direct Preference Optimization to improve helpfulness, safety, and response quality.
- Preference dataset curation with human or AI annotations
- RLHF or DPO alignment pipeline with reward model training
- Alignment evaluation with safety and helpfulness benchmarks
Multi-Model Fine-Tuning
Fine-tuning and orchestration of multiple models for different tasks or domains, with routing logic to select the best model per query and unified serving.
- Multiple fine-tuned models with task-specific specializations
- Model routing and orchestration layer for query dispatch
- Unified API serving all fine-tuned models with monitoring
Fine-Tuning Evaluation
Comprehensive evaluation of fine-tuned models covering domain accuracy, safety, latency, comparison to baselines, and regression testing against original capabilities.
- Evaluation suite with 10+ benchmark categories
- Before-and-after comparison report with statistical significance
- Regression test suite to detect capability degradation
Technology Stack
The tools, platforms, and frameworks we use to deliver this service.
| PyTorch | Deep learning training framework | Expert |
|---|---|---|
| Hugging Face PEFT | Parameter-efficient fine-tuning | Expert |
| TRL (Transformer RL) | RLHF and DPO alignment | Expert |
| DeepSpeed / FSDP | Distributed training acceleration | Expert |
| Unsloth | Fast LoRA and QLoRA training | Expert |
| Axolotl | Fine-tuning recipe management | Advanced |
| vLLM | Optimized inference serving | Expert |
| LM Evaluation Harness | Standard LLM benchmarking | Expert |
| Weights & Biases | Experiment tracking and visualization | Advanced |
| Ray / Kubernetes | Distributed compute orchestration | Advanced |
Use Cases & Industry Applications
Real-world scenarios where this service delivers measurable business impact.
Engagement Timeline & Impact Metrics
Project Timeline
| Phase | Duration | Key Deliverable |
|---|---|---|
| Assessment | Week 1 | Fine-tuning strategy and base model selection |
| Data Prep | Week 2 | Curated instruction and preference datasets |
| Training | Weeks 3-4 | Fine-tuned model with checkpoints |
| Alignment | Week 5 | RLHF or DPO aligned model |
| Eval & Deploy | Week 6 | Benchmarked and deployed model |
Business Impact
| Metric | Before AI | After AI |
|---|---|---|
| Task accuracy | 71% | 92% |
| Inference cost per 1M tokens | ₹2,400 | ₹720 |
| Response format compliance | 64% | 96% |
| Safety score | 82% | 95% |
| Latency (P95) | 1,600ms | 380ms |
Our Capabilities
| Capability | Status |
|---|---|
| LoRA and QLoRA fine-tuning | Available |
| Full-parameter fine-tuning | Available |
| RLHF and DPO alignment | Available |
| Domain-specific training | Available |
| Multi-model orchestration | Available |
| Evaluation and benchmarking | Available |
Pricing & Packages
Transparent pricing for every engagement size. All packages include post-delivery support.
| Tier | Price | Timeline | Includes |
|---|---|---|---|
| Starter | ₹69,000 | 2-3 weeks | LoRA fine-tuning on one open model with basic evaluation |
| Growth | ₹1,79,000 | 4-6 weeks | Full fine-tuning with RLHF or DPO and comprehensive evaluation |
| Enterprise | ₹3,99,000 | 8-10 weeks | Multi-model fine-tuning with orchestration, deployment, and CI/CD |
What Is Included
- Task and data assessment with strategy recommendation
- Data curation and instruction formatting pipeline
- LoRA, QLoRA, or full-parameter fine-tuning
- RLHF or DPO preference alignment
- Evaluation suite with 10+ benchmark categories
- Before-and-after comparison report
- Optimized inference deployment configuration
- Retraining pipeline and team handoff documentation
If your fine-tuned model does not outperform your current baseline on task accuracy benchmarks, we will provide an additional fine-tuning cycle with revised data strategy at no cost.
Book a Free Consultation
Speak with our AI experts about your specific requirements. We will assess your needs, recommend the right approach, and provide a detailed proposal within 48 hours.
Book Your Free Consultation →Frequently Asked Questions
How much data do we need for fine-tuning?
For LoRA fine-tuning, 500 to 5,000 high-quality examples can produce strong results. For full-parameter fine-tuning, we recommend 10,000 to 100,000 examples. For RLHF or DPO, you need 1,000 to 10,000 preference pairs. We assess your data during the strategy phase and recommend the right approach and data volume.
What is the difference between LoRA, QLoRA, and full fine-tuning?
LoRA trains small adapter layers while keeping the base model frozen, making it fast and memory-efficient. QLoRA adds 4-bit quantization to reduce memory further, enabling fine-tuning on smaller GPUs. Full fine-tuning updates all parameters for maximum performance but requires significantly more compute and memory. We recommend based on your data, budget, and accuracy targets.
Do we need our own GPUs for fine-tuning?
Not necessarily. We can run training on cloud GPU providers like AWS, GCP, Azure, Lambda Labs, or RunPod using on-demand or spot instances. For LoRA and QLoRA, costs are modest. For full fine-tuning, we provide a compute cost estimate upfront so you can budget accordingly.
What is RLHF and DPO, and which should we use?
RLHF (Reinforcement Learning from Human Feedback) trains a reward model on human preferences and optimizes the LLM against it. DPO (Direct Preference Optimization) skips the reward model and optimizes directly on preference data, making it simpler and more stable. We recommend DPO for most use cases and RLHF when you need a separate reward model for ongoing evaluation.
Can fine-tuning cause the model to forget general capabilities?
Yes, this is called catastrophic forgetting. We mitigate it with regularization techniques, mixing general data with domain data, and regression evaluation suites that test for capability degradation. Our evaluation includes before-and-after comparisons on general benchmarks to ensure your model stays well-rounded.
How do you evaluate a fine-tuned model?
We run 10+ benchmark categories including domain-specific accuracy, standard LLM benchmarks like MMLU and MT-Bench, safety tests, latency profiling, and side-by-side comparison with your current API baseline. We also build custom test suites with 200+ examples specific to your task and report statistical significance.
Can we fine-tune multiple models for different tasks?
Yes. Our multi-model fine-tuning offering trains specialized models for different tasks or domains and deploys them with a routing layer that selects the best model per query. This is ideal for organizations with diverse use cases where one model cannot excel at everything.
How do we retrain the model when we have new data?
We provide reproducible retraining pipelines with versioned data and code, so your team can run fine-tuning on new data with a single command. We also set up CI/CD pipelines that automatically evaluate new checkpoints and deploy only if they pass quality gates.
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