We train and fine-tune AI models that understand your specific domain, terminology, and requirements, delivering performance that generic models cannot match.
General-purpose AI models are remarkable, but they have limitations for specialized applications. They may not understand your industry terminology, follow your specific output formats, or achieve the accuracy levels your use case demands. Custom model training bridges this gap by teaching AI models the patterns, language, and decision criteria specific to your domain.
Custom training is appropriate when you need models that consistently produce outputs in a specific format, understand proprietary terminology, classify items according to your custom taxonomy, or achieve accuracy levels that prompt engineering alone cannot reach. It is also valuable when you need to reduce inference costs by training a smaller model that matches the performance of a larger general model on your specific task.
Arthiq has trained custom models for classification, extraction, generation, and summarization across multiple domains. We bring a disciplined, data-driven approach to model training that starts with clear success criteria, rigorous dataset preparation, systematic hyperparameter optimization, and thorough evaluation against held-out test data.
Successful model training begins with high-quality training data. We work with your team to collect, clean, and annotate training datasets that represent the full range of inputs your model will encounter in production. We implement quality assurance processes for annotations, including inter-annotator agreement measurement and systematic error analysis.
Our training pipeline is built for reproducibility and iteration. Every training run is tracked with full configuration details, including data versions, hyperparameters, and random seeds. This makes it straightforward to compare experiments, understand what changes improved performance, and reproduce results exactly. We use tools like Weights and Biases for experiment tracking and visualization.
We select the training approach based on your specific needs. For LLMs, this might mean supervised fine-tuning on example inputs and outputs, or reinforcement learning from human feedback for more nuanced quality objectives. For classification and extraction tasks, we train or fine-tune models from Hugging Face transformers with task-specific architectures optimized for your data characteristics.
Fine-tuning an LLM adapts a pre-trained model to your specific domain and task. Arthiq fine-tunes models from OpenAI, Llama, and Mistral families using techniques including LoRA and QLoRA that make training efficient even on modest hardware. The result is a model that understands your terminology, follows your output conventions, and performs significantly better than the base model on your specific tasks.
We prepare fine-tuning datasets through a combination of manual curation and synthetic data generation. For domains where labeled data is scarce, we use stronger models to generate high-quality training examples that are then validated by domain experts. This approach lets us build effective training datasets quickly without requiring thousands of manually labeled examples.
After training, we evaluate fine-tuned models rigorously against both automated benchmarks and human evaluations. We test for regression on general capabilities, measure improvement on target tasks, and check for unintended biases or behaviors introduced by the training data. Only models that pass all evaluation criteria are promoted to production.
A trained model is only valuable when it is serving predictions in production. Arthiq handles the full deployment pipeline including model optimization, containerization, and inference infrastructure. We quantize models to reduce memory requirements and inference latency without meaningful accuracy loss. We deploy using optimized serving frameworks like vLLM, TGI, or TensorRT that maximize throughput.
For cloud deployments, we configure auto-scaling inference endpoints that adjust to traffic patterns, scaling up during peak usage and down during quiet periods to optimize costs. For on-premises requirements, we deploy models on your GPU infrastructure with monitoring and management tools that keep the system running reliably.
We set up A/B testing infrastructure that lets you compare custom model performance against baseline models in production. This gives you empirical evidence of the improvement and helps you make informed decisions about when to roll out new model versions.
Custom model training is a specialized discipline that requires expertise in data preparation, training methodology, evaluation, and deployment. Arthiq brings this full-stack capability to every engagement, from dataset curation through production deployment and monitoring.
We approach every training project with clear success metrics defined upfront. You will know exactly what accuracy, latency, and cost targets we are aiming for, and our iterative process keeps you informed of progress toward those goals throughout the engagement.
Contact us at founders@arthiq.co to discuss whether custom model training is the right approach for your use case. We will help you evaluate the tradeoffs between prompt engineering, RAG, and fine-tuning to find the most effective solution.
Our machine learning engineers will prepare your data, train custom models, and deploy them to production with the reliability and performance your application demands.