Service Overview & Business Impact

Generic foundation models struggle with proprietary company terminology and unique output structures. Our LLM Fine-Tuning service uses parameter-efficient methods (LoRA/QLoRA) to embed domain expertise into model weights efficiently.

PEFTLoRAQLoRAPyTorchHugging FaceUnsloth

4-Layer Engineering Architecture

Layer 1: Domain Dataset Curation

Cleans, formats, and tokenizes internal corporate manuals, codebases, or legal records into instruction-tuning pairs.

Layer 2: QLoRA Quantized Fine-Tuning

Trains 4-bit Low-Rank Adaptation (LoRA) adapter weights on target GPUs, reducing VRAM usage by 75%.

Layer 3: Evaluation & Alignment

Benchmarking fine-tuned adapter performance against baseline models on domain evaluation suites.

Layer 4: Adapter Fusion & Serving

Fuses trained LoRA adapters into base models for high-throughput vLLM serving.

Implementation Roadmap & Deliverables

Phase 1: Dataset Preparation & Tokenization

Construct high-quality instruction-tuning datasets from proprietary source files.

Phase 2: QLoRA Training Run

Execute fine-tuning runs using Unsloth and Hugging Face PEFT frameworks.

Phase 3: Model Alignment & Evaluation

Verify model accuracy and response formatting on test evaluation benchmarks.

Phase 4: Serving Deployment

Deploy fine-tuned adapters into production GPU inference infrastructure.

Ready to Deploy This AI Architecture?

Book a 1-on-1 technical scoping session directly with AI & Data Science Consultant Rohit.

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Consultant Profile

Rohit

AI & Data Science Consultant

2+ Decades AI Experience

Building neural networks since 2004 at IIT Roorkee (mentored by Dr. Sunil Padhi, HOD Electrical Dept) and Unix CDR automation scripts at Xalted Bengaluru in 2007 (mentored by Srinivas Sir). Specializing in Agentic AI, Enterprise RAG, and MLOps.

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LLM Fine-Tuning & Parameter-Efficient Optimization