AxionSquare
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Custom LLM Fine-Tuning & Model Training
Fine-tuning open-weight models (LLaMA 3, Mistral, Qwen) with LoRA, QLoRA, and private dataset distillation.
Engineering Overview
We train, fine-tune, and compress custom AI models tailored to your exact domain. From synthetic dataset generation to parameter-efficient fine-tuning (PEFT/LoRA) and high-throughput vLLM inference hosting, you own 100% of your weights and data.
Standard Architecture Deliverables
Proprietary dataset curation, deduplication, and synthetic augmentation
LoRA / QLoRA parameter-efficient fine-tuning with custom loss functions
GGUF, AWQ, and FP8 model quantization for edge and local GPU deployment
vLLM & TensorRT-LLM dedicated inference endpoints with P99 latency SLA
Automated evaluation benchmarks tracking perplexity, accuracy, and hallucination rates
Our Build Process
01
Data Ingestion & Cleaning
Audit and tokenize proprietary client datasets into clean training pairs.
02
Architecture & Hyperparameters
Select base weights (LLaMA 3, Mistral) and configure LoRA rank/alpha.
03
Training & Loss Optimization
Execute multi-GPU training runs with automated checkpointing and loss logging.
04
Quantization & Deployment
Quantize weights (AWQ/FP8) and deploy to dedicated high-concurrency vLLM pods.
Sprint Tiers & Options
Select the engagement structure that matches your product timeline.