LLM Fine-Tuning & Domain Adaptation
Custom model adaptation (LoRA, QLoRA, DPO, RLHF) to embed domain expertise, improve accuracy, and lower inference costs.
Generic foundation models lack specialized industry knowledge and concise execution. Varixen fine-tunes state-of-the-art open-weight models on your proprietary datasets—delivering domain-expert accuracy, strict output formatting, and up to 70% lower inference costs compared to commercial APIs.
ENTERPRISE BENCHMARKS
70%
Lower Inference Costs
98.5%
Domain Accuracy Score
100%
Data Privacy Sovereignty
Engineering precision across every layer
Designed for high performance, enterprise security, and seamless API integration into your core software systems.
Parameter-Efficient Fine-Tuning (PEFT)
Utilize LoRA, QLoRA, and Adapter layers to train 70B+ parameter models on single GPU setups efficiently.
Direct Preference Optimization (DPO)
Align model outputs with human preferences and domain guidelines using DPO and RLHF technique suites.
Domain Knowledge Injection
Adapt models to medical terminology, legal jargon, financial tax codes, or specialized proprietary syntaxes.
Structured Output Enforcement
Train models to output flawless JSON, SQL, or XML without relying on slow regex retry logic.
Model Distillation & Compression
Compress giant 70B teacher models into hyper-fast 8B student models with zero loss in target precision.
Dataset Curation & Synthetic QA
Clean, deduplicate, and generate high-quality instruction-tuning pairs from unstructured raw enterprise text.
How we architect and deploy
A disciplined four-phase methodology ensuring model safety, zero downtime, and rapid value realization.
Dataset Cleaning & Tokenization
Format unstructured raw documents into high-quality instruction (prompt/response) JSONL dataset pairs.
Base Model Benchmark & Baseline
Establish quantitative baseline performance using industry metrics (BLEU, ROUGE, LLM-as-a-Judge).
Quantized Fine-Tuning (QLoRA)
Execute parameter-efficient training runs on H100/A100 clusters with hyperparameter optimization.
Validation, Quantization & Serving
Evaluate against test sets, quantize to GGUF/AWQ formats, and deploy on vLLM for high-throughput serving.
Built with proven enterprise tooling
Training Tools
Serving Engines
Base Models
Enterprise case studies
SQL Query Generation Model
Challenge: Generic LLMs made syntax errors on complex multi-table SQL queries, causing database crashes.
Solution: Fine-tuned Llama 3 8B on 50,000 internal schema-query pairs, enforcing exact dialect rules.
Medical Diagnostic Assistant
Challenge: Commercial APIs hallucinated medical codes (ICD-10) and violated patient privacy regulations.
Solution: Trained an open-weight model on anonymized clinical notes, hosted entirely on an on-prem server.
Contract Risk Extraction Model
Challenge: General-purpose models struggled with non-standard indemnification clauses in contracts.
Solution: Applied DPO fine-tuning aligned to senior partner preference scores across 10,000 legal clauses.
Frequently asked questions
Why should we fine-tune an open model instead of using GPT-4o?
Fine-tuning an open model (like Llama 3) gives you complete data privacy, eliminates API token fees, achieves lower inference latency, and guarantees your model won't change unpredictably due to provider updates.
How much data is required for effective fine-tuning?
With QLoRA and modern instruction-tuning methods, high-quality results can be achieved with 1,000 to 10,000 carefully curated prompt/response pairs.
What hardware is required to host a fine-tuned model?
A fine-tuned 8B model can run on a single affordable GPU (NVIDIA A10G/T4). Larger 70B models can be quantized to AWQ/GGUF to run on 2-4 GPUs with vLLM.
How do you evaluate if fine-tuning was successful?
We construct automated evaluation pipelines using LLM-as-a-Judge, human preference scoring, and exact metric benchmarks against your baseline performance metrics.
Ready to build what's next?
Schedule a 1-on-1 Digital Transformation Strategy Call with our leadership team to accelerate your technology roadmap.