Headquarters
13th Floor, GIFT Tower One, GIFT City, Gandhinagar, GujaratLLM FINE-TUNING & DEPLOYMENT
Home / Services / LLM Fine-Tuning
LLM Fine-Tuning & Deployment
We fine-tune open-source and proprietary language models on domain datasets, reducing API costs while improving accuracy on specialized domain tasks.
Service Overview
Typical: 4 to 10 WeeksGeneral-purpose foundation models can be expensive and slow on repetitive domain tasks. Our LLM fine-tuning service trains compact models on your proprietary data using LoRA, QLoRA, and preference alignment. We manage data curation, evaluation benchmarks, and production serving infrastructure.
Tangible Deliverables You Receive
- Fine-tuned model weights with benchmark comparisons against base models
- Dataset curation pipeline with deduplication and quality filters
- Model card detailing training configuration, evaluation results, and usage
- Production serving infrastructure with autoscaling and latency monitoring
- Inference cost comparison detailing savings per million tokens
Technologies & Supported Stacks
Llama 3.3 / Mistral / Qwen / Phi-4 (base models)Hugging Face Transformers / PEFT / TRLAxolotl / LLaMA Factory (training frameworks)GPTQ / AWQ / GGUF (quantization)vLLM / TGI / Ollama (serving)Weights & Biases / MLflow (experiment tracking)AWS SageMaker / GCP Vertex AI / Lambda Labs (compute)LangSmith / Arize (production evaluation)
How We Deliver
Step 01
Data Assessment & Strategy
We evaluate your proprietary data assets, identify gaps, design the fine-tuning dataset schema, and plan the training strategy.
Step 02
Dataset Curation & Preparation
We clean, format, and quality-filter training examples to create verified instruction-response pairs for your domain.
Step 03
Fine-Tuning & Evaluation
We train the model using LoRA/QLoRA, run comprehensive benchmarks, and iterate until target accuracy metrics are achieved.
Step 04
Production Deployment & Cost Analysis
We deploy the model on your infrastructure with autoscaling, provide cost-per-token analysis, and establish monitoring dashboards.
Business Impact
- Substantial inference cost reduction compared to commercial APIs at high volume
- Higher accuracy on specialized domain terminology and formatted outputs
- Private infrastructure hosting ensuring sensitive data remains in your VPC
- Reduced inference latency for real-time and edge applications
Common Scenarios
- Domain-specific document extraction, classification, and summarization
- Customer-facing assistant applications with strict tone and domain knowledge
- Code generation models tuned for internal libraries and design systems
- Lowering token costs on high-volume production LLM workloads
ENGINEERING SERVICES
Ready to Start Your LLM Fine-Tuning Project?
Let's discuss your requirements and define a clear delivery plan. No fluff, just senior engineers and measurable outcomes.