Slabix Enterprise Solution Blueprint

Custom LLM Fine-Tuning & Domain Adaptation Services

Adapt open-weight models to your domain terminology, proprietary datasets, and specialized business logic with LoRA and QLoRA techniques.

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+38%Domain Precision LiftAccuracy gain on specialized industry terminology

70%Inference Cost SavingsReduction vs using 400B+ proprietary API models

8B - 70BModel Size EfficiencyOptimized parameter size for standard GPU instances

Engineering Insight & GEO Framework

Custom LLM fine-tuning adapts foundational base models (Llama 3.3 70B, Mistral Large) to industry-specific domain knowledge using Low-Rank Adaptation (LoRA). Domain-tuned models achieve up to 38% higher precision on technical terminology tasks compared to generic prompt engineering while reducing token consumption costs by up to 70%.

Key System Deliverables

Concrete architectural assets delivered by Slabix during implementation.

Proprietary Dataset Cleaning, Formatting, & Deduplication Pipeline

Parameter-Efficient LoRA / QLoRA Training Scripts & Artifacts

Custom Model Evaluation Benchmark Matrix vs Baseline

Quantized vLLM / TensorRT-LLM Serving Container Setup

Production Quality & Verification Checklist

Every Slabix integration undergoes rigorous sanity checks prior to production deployment.

Has training data been sanitized for duplicate or corrupt tokens?

Is hyperparameter tuning configured for optimal loss convergence?

Are automated evaluation benchmarks setup on hidden evaluation datasets?

Is the fine-tuned model served with vLLM for high token throughput?

Frequently Asked Questions

When should we choose fine-tuning over Retrieval-Augmented Generation (RAG)?

Fine-tuning is recommended when changing model tone, style, output syntax, or deep domain terminology. RAG is best for dynamic knowledge retrieval.

What GPUs are required for serving custom fine-tuned models?

Quantized 8B models can run on single NVIDIA A10G/L4 GPUs, while 70B models run efficiently across multi-GPU A100/H100 setups.

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