LLM Finetuning Services

Frequently Asked Questions

• Q:

  What is LLM fine-tuning?

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  LLM fine-tuning is the process of adapting a pre-trained large language model like OpenAI GPT, Anthropic Claude, LLaMA, or Mistral to perform specific tasks using your proprietary data. Instead of training a model from scratch, fine-tuning adjusts an existing model's weights so it generates outputs tailored to your domain, terminology, and quality standards. Azumo fine-tunes models for customer support automation, document classification, content generation, code review, compliance analysis, and domain-specific Q&A. We have fine-tuned Falcon LLM for customer support and built custom NLP models for Meta using Named Entity Recognition. Fine-tuning typically reduces inference costs, improves response accuracy for your use case, and keeps sensitive data within your control.

• Q:

  Why should a company fine-tune an LLM instead of using a general-purpose model?

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  Fine-tuning delivers higher accuracy on your specific tasks, lower per-query inference costs, consistent outputs matching your brand voice and standards, and control over sensitive data. A general-purpose model generates acceptable responses for broad queries but underperforms on domain-specific tasks like insurance claims classification, medical terminology extraction, or financial compliance review. Fine-tuned models reduce hallucination rates for your domain, produce outputs that match your formatting requirements, and can run on smaller, cheaper infrastructure. Azumo clients fine-tune when they need models that understand their proprietary terminology, follow their specific output structures, or process data that cannot leave their infrastructure. We use techniques including SFT, RLHF, and RL-based post-training optimization.

• Q:

  What data is needed for LLM fine-tuning?

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  Fine-tuning requires curated examples of the input-output pairs you want the model to produce. For supervised fine-tuning, this means hundreds to thousands of high-quality prompt-completion pairs representing your target task. Data quality matters more than quantity: 500 well-curated examples often outperform 10,000 noisy ones. Azumo helps clients build training datasets through data audit and gap analysis, annotation workflow design, quality assurance and inter-annotator agreement measurement, and synthetic data generation for underrepresented scenarios. For RLHF, we also create preference datasets where human reviewers rank model outputs. We handle data preprocessing, deduplication, format standardization, and privacy controls including PII removal and compliance with HIPAA, GDPR, and SOC 2 requirements.

• Q:

  What fine-tuning methods does Azumo use?

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  Azumo uses supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), direct preference optimization (DPO), and parameter-efficient methods including LoRA and QLoRA. SFT adapts models to your task using labeled examples. RLHF aligns model outputs with human preferences through reward modeling. LoRA and QLoRA enable fine-tuning large models on smaller hardware by training low-rank adapter layers rather than full model weights. We select the method based on your data availability, performance requirements, infrastructure constraints, and budget. For production deployment, we also offer model distillation to create smaller, faster models that retain fine-tuned performance. Our stack includes PyTorch, Hugging Face Transformers, DeepSpeed, and cloud training on AWS SageMaker, Azure ML, and Google Vertex AI.

• Q:

  How long does LLM fine-tuning take?

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  A typical fine-tuning project takes 4-12 weeks from data preparation through production deployment. Data audit and preparation takes 1-3 weeks depending on data readiness. Fine-tuning training runs take hours to days depending on model size, dataset size, and hardware. Evaluation and iteration adds 1-2 weeks. Production deployment and integration takes 1-3 weeks. Azumo accelerates timelines using pre-built training pipelines, automated hyperparameter optimization, and established evaluation frameworks. For clients with clean, labeled data ready to go, we can deliver a fine-tuned model within 2-3 weeks. Our nearshore teams across Latin America work in your time zone with daily syncs throughout the project.

• Q:

  What best practices ensure successful LLM fine-tuning?

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  Successful fine-tuning requires high-quality training data, systematic evaluation, iterative refinement, and production monitoring. Start with a clear definition of success metrics: accuracy, latency, cost per query, and domain-specific measures. Curate training data that represents the full distribution of inputs your model will encounter, including edge cases. Use held-out test sets that mirror production traffic. Evaluate with both automated metrics and human review. Fine-tune incrementally, starting with fewer examples to validate the approach before scaling. Monitor for catastrophic forgetting where the model loses general capabilities. Azumo implements evaluation frameworks using custom benchmarks, A/B testing, and continuous performance monitoring in production. We track token costs, latency percentiles, and output quality across model versions.

• Q:

  How does Azumo support companies with LLM fine-tuning?

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  Azumo provides end-to-end LLM fine-tuning services: data audit and preparation, training dataset creation, model selection, fine-tuning execution, evaluation, deployment, and ongoing optimization. We work with OpenAI, Anthropic Claude, LLaMA, Mistral, Qwen, and DeepSeek models. Our team includes ML engineers experienced in SFT, RLHF, DPO, LoRA, and model distillation. We deploy fine-tuned models through Valkyrie, our AI infrastructure platform that provides a single REST API to any LLM, image model, or fine-tuned model. We also offer dedicated nearshore ML engineering teams through staff augmentation or dedicated team models. SOC 2 certified with deployment options including private cloud, on-premises, and air-gapped environments.

• Q:

  What security and compliance does Azumo address during fine-tuning?

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  Azumo is SOC 2 certified and implements security controls throughout the fine-tuning lifecycle. Training data is encrypted at rest and in transit. Access controls restrict who can view, modify, and deploy models. Audit logs track all data access and model changes. For regulated industries, we implement HIPAA-compliant data handling, GDPR data minimization and consent management, and PCI-DSS controls for financial data. We can fine-tune models entirely within your private cloud or on-premises infrastructure when data cannot leave your environment. Our security measures include PII detection and removal from training data, secure model artifact storage, and vulnerability scanning of deployment infrastructure. Every fine-tuned model undergoes security review before production deployment.