LoRA Fine-Tuning Service

1. Background

Generic LLMs are powerful but not specific to your domain. Full fine-tuning is costly, slow, and hard to maintain—especially when data must stay private. Our LoRA/QLoRA Fine-Tuning Service gives you task-specific performance with a fraction of the compute and time. You choose the base model and provide data; we handle data labeling and fine-tuning end-to-end, then deliver a production-ready adapter and deployment guide.

2. What Is LoRA (and QLoRA)?

• LoRA (Low-Rank Adaptation) adds small trainable adapters on top of a frozen base model, so we optimize far fewer parameters while preserving the model’s general knowledge.
• QLoRA loads the base model in 4-bit precision to further reduce memory needs—ideal when GPU resources are tight—while training LoRA adapters for your task.

3. Why Choose Us

• Model + Data, handled your way: You pick Llama / Qwen / Mistral / Gemma / etc. You provide data; we do the rest.
• Labeling included: Annotation guideline design, expert labeling, two-pass QA, and inter-annotator checks.
• Efficient training: Parameter-efficient LoRA/QLoRA pipelines for faster iteration and lower cost than full FT.
• Measurable quality: Clear task metrics (e.g., F1/ROUGE/win-rate) and human eval on a held-out set.
• Production-ready: We ship adapters, configs, and a serving playbook (HF/vLLM/Triton).
• Privacy & security: NDA by default, isolated environments, and delete-on-handover options.

4. What You Get

• LoRA adapter weights + config
• Evaluation report with metrics, error analysis, and sample outputs
• Deployment guide for HF Transformers, vLLM, or Triton/TensorRT-LLM

5. Use Cases

• Customer Support QA & Summarization: intent detection, reply drafting, ticket summaries
• Information Extraction: entities/attributes from invoices, forms, emails, chats
• Knowledge QA: domain manuals + policies, optionally grounded with RAG
• Content Generation: product descriptions, style-constrained rewrites, templates

6. Development Process

1. Requirements & KPI Definition: tasks, success metrics, constraints, deployment target.
2. Data Intake & Labeling: cleaning, dedup, split; guideline creation; expert labeling with two-pass QA.
3. Pilot Fine-Tuning: small run (LoRA/QLoRA) to verify quality, speed, and budget.
4. Full Training & Evaluation: hyperparameter search, safety checks, human eval, regression tests.
5. Handover & Deployment: deliver adapters, reports, and serving playbooks; optional on-prem/VPC setup.