Step 01:
Architecture Blueprint & Feasibility
Step 03:
Benchmark Evals & Security Hardening
Step 02:
Core Graph & Integration Engineering
Step 04:
Production Deployment & Handover
LLM Fine-Tuning & Deployment
Service Overview :
General-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 :
Technologies & Supported Stacks :
- Llama 3.3 / Mistral / Qwen / Phi-4 (base models)
- Hugging Face Transformers / PEFT / TRL
- Axolotl / 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
Data Assessment & Strategy
Dataset Curation & Preparation
We evaluate your proprietary data assets, identify gaps, design the
fine-tuning dataset schema, and plan the training strategy.
We clean, format, and quality-filter training examples to create
verified instruction-response pairs for your domain.
Fine-Tuning & Evaluation
Production Deployment & Cost Analysis
We train the model using LoRA/QLoRA, run comprehensive benchmarks,
and iterate until target accuracy metrics are achieved.
We train the model using LoRA/QLoRA, run comprehensive benchmarks,
and iterate until target accuracy metrics are achieved.