EXP-06CATEGORY: LLMSTATUS: EXPERIMENTAL

SLM Domain Alignment

Fine-tuning small 3B-8B parameter language models for ultra-low-latency on-premise enterprise inference.

EXPERIMENTAL OVERVIEW & OBJECTIVE

An experimental research project exploring how fine-tuned Small Language Models (SLMs) can match 70B+ model accuracy on narrow domain tasks.

Large 70B+ LLM cloud API calls incur high latency and continuous token costs, making high-volume real-time inference expensive.

We use LoRA (Low-Rank Adaptation) and Direct Preference Optimization (DPO) to fine-tune compact 3B models on domain-specific datasets.

SYSTEM ARCHITECTURE SPECIFICATION

LoRA & QLoRA Fine-Tuning Pipeline

DPO Preference Alignment

AWQ 4-Bit Weight Quantization

vLLM High-Throughput Serving

CURRENT BENCHMARK STATE

Achieving 94% domain benchmark accuracy on a 3B model at 5x lower latency.

FUTURE ENGINEERING DIRECTION

Exporting quantized GGUF weights for edge device execution.

TECHNOLOGY STACK & INFRASTRUCTURE

PyTorchvLLMTransformersTRLNVIDIA CUDA

Interested in Applying This Experiment to Your System?

Discuss how our research prototypes can be hardened into commercial software for your enterprise.