Generic foundation models are trained on the internet. Your business runs on specialist knowledge, proprietary terminology, and specific output standards the internet does not contain. Perimattic fine-tunes GPT-4o, Llama 3, Mistral, and other foundation models on your data — delivering accuracy and consistency that prompt engineering alone cannot achieve.
Foundation Models & Fine-Tuning Frameworks — GPT-4o, Llama 3, Mistral, LoRA/QLoRA, Hugging Face, Axolotl
LLM fine-tuning is the process of further training a pre-trained large language model on a curated dataset specific to your domain or task. Unlike prompt engineering — which provides instructions at inference time — fine-tuning updates the model's weights to deeply internalise your terminology, output format, reasoning patterns, and quality standards. The result is a model that behaves as if it was built for your use case from scratch, without requiring a large system prompt on every request.
The practical implication for business is significant. A fine-tuned model handling clinical documentation does not need a 4,000-token system prompt to understand ICD coding conventions — it already knows them, because they are encoded in its weights. That translates directly to lower inference costs, faster response times, reduced hallucination rates on specialist terminology, and consistent output quality that prompt engineering cannot reliably deliver at scale.
Fine-tuning is distinct from retrieval augmented generation, which grounds responses in external documents at runtime. The two approaches are complementary: fine-tune to instil domain style, format, and reasoning; use RAG for access to large or frequently updated knowledge bases. Many production systems combine both, and we advise on the right architecture for your use case before any training begins.
Output consistency
Generic, often off-tone output
Output consistency
Domain-calibrated, on-brand output
Inference cost
High — large prompts on every request
Inference cost
Low — model encodes domain knowledge
Latency
Slower — large context windows required
Latency
Faster — minimal prompt size needed
Hallucination rate
Frequent on specialist terminology
Hallucination rate
Significantly reduced by training
Maintenance
Constant prompt iteration required
Maintenance
Stable — retrain on major data shifts only
The distinction matters most in production scenarios — where output consistency, cost per request, and latency at scale determine whether an AI system is genuinely viable or merely a proof of concept.
Seven specialist service lines, each built for a specific stage of the fine-tuning lifecycle.
We fine-tune GPT-4o, Llama 3, Mistral, Gemma, and other foundation models on your proprietary datasets using LoRA, QLoRA, or full fine-tuning. Every project begins with a data audit and ends with a benchmarked, production-ready domain model.
High-quality training data is the single biggest determinant of fine-tuned model quality. We curate, clean, deduplicate, and format your enterprise data into instruction-following datasets that maximise model learning signal and minimise noise.
LoRA and QLoRA allow us to fine-tune models with billions of parameters on modest GPU infrastructure at a fraction of the cost of full fine-tuning, while matching or exceeding full fine-tuning accuracy on most domain adaptation tasks.
We adapt foundation models to specialist domains — clinical, legal, financial, or industrial — where generic models consistently underperform due to terminology gaps, reasoning pattern mismatch, or output format inconsistency.
We evaluate every fine-tuned model against domain-specific benchmarks rather than generic leaderboards. This includes held-out test set accuracy, hallucination rate on specialist terminology, human preference scoring, and latency profiling.
We deploy fine-tuned models to your preferred inference infrastructure: vLLM or TGI for self-hosted deployments, or API endpoints on AWS, Azure, or GCP. We optimise for latency and cost at every step of the deployment process.
Fine-tuned models drift as your domain evolves. We instrument your model with production monitoring and establish re-training triggers so output quality is maintained automatically as your data changes over time.
A structured six-stage process from free data audit to live deployment and ongoing model monitoring.
We map your domain task, review your existing data assets, and define clear success metrics for the fine-tuning engagement. We advise on data gaps and what collection effort is needed before training begins. This session is free and carries no obligation.
We curate, clean, and format your data into high-quality instruction-following examples. This phase includes deduplication, quality filtering, and dataset balancing to maximise training signal and ensure the model learns the right behaviours.
We select the base model that best fits your latency, cost, data privacy, and performance requirements. We determine the optimal approach — LoRA, QLoRA, or full fine-tuning — and set hyperparameter search ranges for the training phase.
We run training experiments with hyperparameter search, monitoring loss curves and validation metrics at each checkpoint. We iterate on the dataset and training configuration until the model meets the agreed quality benchmarks.
We evaluate the fine-tuned model against held-out test data, domain-specific benchmarks, and human preference evaluation. We compare against the prompt-engineered baseline so you have quantified evidence of improvement at every iteration.
We deploy the model to your inference infrastructure and instrument it with production monitoring. We track output quality, latency, and usage patterns, and provide a re-training plan for when domain drift is detected.
We map your domain task, review your existing data assets, and define clear success metrics for the fine-tuning engagement. We advise on data gaps and what collection effort is needed before training begins. This session is free and carries no obligation.
We curate, clean, and format your data into high-quality instruction-following examples. This phase includes deduplication, quality filtering, and dataset balancing to maximise training signal and ensure the model learns the right behaviours.
We select the base model that best fits your latency, cost, data privacy, and performance requirements. We determine the optimal approach — LoRA, QLoRA, or full fine-tuning — and set hyperparameter search ranges for the training phase.
We run training experiments with hyperparameter search, monitoring loss curves and validation metrics at each checkpoint. We iterate on the dataset and training configuration until the model meets the agreed quality benchmarks.
We evaluate the fine-tuned model against held-out test data, domain-specific benchmarks, and human preference evaluation. We compare against the prompt-engineered baseline so you have quantified evidence of improvement at every iteration.
We deploy the model to your inference infrastructure and instrument it with production monitoring. We track output quality, latency, and usage patterns, and provide a re-training plan for when domain drift is detected.
Select an industry to see how fine-tuned models outperform prompt engineering in that domain.
LLM fine-tuning in healthcare focuses on clinical terminology, evidence-based reasoning, and regulatory compliance — where generic models frequently hallucinate or miss domain-specific nuance.
LLM fine-tuning in healthcare focuses on clinical terminology, evidence-based reasoning, and regulatory compliance — where generic models frequently hallucinate or miss domain-specific nuance.
Fine-tuned models in financial services handle regulatory language, numerical reasoning, and compliance obligations that general-purpose LLMs struggle with reliably.
Legal LLMs require precise reasoning over complex clause structures and jurisdiction-specific language — areas where general-purpose models frequently produce plausible but incorrect outputs.
Fine-tuned retail LLMs drive measurable improvements in product content quality, customer support deflection rates, and catalogue enrichment speed.
Manufacturing LLM fine-tuning focuses on technical documentation, maintenance workflows, and operational data — where precise terminology and format adherence are critical.
HR LLMs require strict alignment with fairness requirements, job architecture conventions, and organisational tone — making fine-tuning on internal data significantly safer than generic prompting.
Perimattic's work resulted in stable production systems. The team was helpful, easily accessible, and communicative through email. Their professionalism was impressive.
Quality
4.5
Schedule
5.0
Cost
5.0
Willing to Refer
4.5
DevOps Managed Services · Oct 2023 – Aug 2024
24/7 monitoring and support for production environments plus Linux server administration for a leasing automation company.
The new architecture is scalable and highly efficient, saving a lot of money in fees. Perimattic provides high-quality IT consulting and cloud development work promptly and at great value. The team remains involved from the planning stage to providing support, showing diligence and proactiveness.
Quality
5.0
Schedule
5.0
Cost
4.5
Willing to Refer
5.0
AWS Migration (Legacy → Microservices) · Nov 2018 – Ongoing
Transitioned a travel systems company's legacy server system to an AWS-based microservices architecture with ongoing maintenance.
Four structural advantages that separate production-grade fine-tuning from one-off training experiments.
We work across GPT-4o, Llama 3, Mistral, Gemma, and Phi — selecting the foundation model that fits your accuracy requirements, data privacy constraints, and inference cost targets, not the model we happen to know best.
Most fine-tuning projects fail because of poor training data, not poor training code. We treat dataset curation as the core of the engagement. If your data is not ready, we make it ready — that work is not a prerequisite you solve before calling us.
We default to LoRA and QLoRA for every engagement unless full fine-tuning is demonstrably necessary. This keeps training costs low, iteration cycles fast, and re-training practical without prohibitive compute requirements.
We establish evaluation baselines before the first training run. Every iteration is measured against your domain benchmarks. You see quantified improvement at each milestone — not a black box we claim works.
“A fine-tuned 7B parameter model routinely outperforms GPT-4 on domain-specific tasks at a fraction of the inference cost — but only if the training data is curated correctly from the start.”
LLM fine-tuning is the process of further training a pre-trained large language model on a curated dataset specific to your domain or task. Unlike prompt engineering, which provides instructions at inference time, fine-tuning updates the model's weights to deeply internalise your terminology, output format, reasoning patterns, and quality standards. The result is a model that behaves as if it was built for your use case — without the latency and cost overhead of large system prompts.
Fine-tune when you need the model to reliably adopt a specific output format, tone, or reasoning style that cannot be achieved through prompting alone. Prompt engineering is the right first step for most use cases; add RAG when you need access to large or frequently updated knowledge bases. Fine-tuning excels when you need consistent domain terminology, reduced hallucination on specialist topics, or significantly lower inference costs by shrinking the required prompt size. Many production systems combine all three approaches.
Quality matters far more than quantity. Meaningful improvements are possible with as few as 500 to 1,000 high-quality instruction-following examples. The sweet spot for most domain adaptation projects is 5,000 to 10,000 examples. For highly specialised tasks or full fine-tuning of large models, more data improves results, but diminishing returns set in quickly past 50,000 examples. We audit and prepare your training data as part of every engagement.
We fine-tune across the major open-source and proprietary model families: GPT-4o and GPT-3.5 Turbo via the OpenAI fine-tuning API, Llama 3 (8B and 70B), Mistral 7B and Mixtral 8x7B, Gemma 2B and 7B, Phi-3, and Falcon 40B. Model selection depends on your inference cost requirements, latency constraints, data privacy needs, and the complexity of your domain task. We recommend the best fit rather than defaulting to the largest or most well-known model.
LoRA, or Low-Rank Adaptation, is a parameter-efficient fine-tuning technique that trains a small set of additional adapter weights rather than updating all of the model's parameters. This means you can fine-tune a 70B parameter model on a single or small cluster of GPUs at a fraction of the cost and time of full fine-tuning, with comparable task performance. QLoRA extends this further with quantisation, reducing memory requirements by another four times. For most enterprise fine-tuning use cases, LoRA or QLoRA is the right choice.
Data preparation to first fine-tuned model typically takes two to four weeks for a well-scoped dataset. A complete engagement including data curation, training runs, evaluation, iteration, and deployment runs six to ten weeks. Projects involving large datasets, multiple model variants, or complex evaluation frameworks take longer. We provide a more specific timeline after the free scoping call.
Fine-tuning projects typically cost $20,000 to $80,000 depending on dataset size and preparation complexity, the base model selected, the number of training iterations required, and evaluation depth. Training compute costs are additional but are modest for LoRA and QLoRA approaches on mid-size models. We scope every engagement before quoting so there are no surprises.
Yes. For sensitive data we deploy training infrastructure within your own cloud environment — AWS, Azure, or Google Cloud — so your data never leaves your infrastructure. We do not use third-party GPU clouds for sensitive workloads unless explicitly approved. We advise on any applicable data residency, privacy, or sector-specific compliance requirements during scoping.
We evaluate fine-tuned models against domain-specific benchmarks rather than generic leaderboards, because generic benchmarks rarely reflect your actual task quality. Depending on the task, this includes held-out test set accuracy, F1 score, BLEU or ROUGE for generation tasks, human preference evaluation, hallucination rate on domain terminology, and latency and throughput benchmarks. We establish evaluation baselines before training begins and report against them at each iteration.
We have delivered fine-tuning projects for healthcare and life sciences, financial services, legal and compliance, e-commerce and retail, manufacturing, and HR and recruiting. Our processes are designed to meet the data sensitivity, regulatory, and compliance requirements common in these sectors. We can discuss sector-specific requirements on the free scoping call.
Tell us about your domain task and dataset, and we will show you exactly how a fine-tuned model improves accuracy, reduces inference costs, and delivers consistent output at production scale.