[{"data":1,"prerenderedAt":183},["ShallowReactive",2],{"svc-detail-ai-fine-tuning":3,"svc-work-ai-fine-tuning":146,"svc-blog-ai-fine-tuning":166,"ill-services\u002Fai-fine-tuning":182},{"slug":4,"short":5,"title":6,"tagline":7,"answer":8,"summary":9,"icon":10,"subServices":11,"useCases":61,"benefits":77,"process":83,"tools":99,"industries":111,"faq":117,"relatedWork":136,"relatedServices":138,"seo":143},"ai-fine-tuning","AI Fine-Tuning","AI Fine-Tuning & Custom Model development","Models that speak your domain, follow your format and cost less to run.","AI fine-tuning and custom model services adapt foundation models to your domain, tasks and output formats: instruction tuning, LoRA, RLHF-style preference tuning, custom embeddings and vision, audio or multimodal fine-tuning. It suits teams whose prompts have hit a quality or cost ceiling and who have, or can build, the examples needed to teach a model to do the job reliably.","Custom LLM training, domain adaptation, instruction tuning, LoRA, preference and RLHF pipelines, custom embeddings and multimodal fine-tuning, with evaluation that proves the gain before you deploy.","wand",[12,17,21,25,29,33,37,41,45,49,53,57],{"slug":13,"name":14,"text":15,"page":16},"llm-fine-tuning","LLM fine-tuning","Fine-tune GPT, Llama, Mistral or Qwen models on your examples for accuracy, consistency and lower cost per call.",true,{"slug":18,"name":19,"text":20},"custom-llm-training","Custom LLM training","Continued pre-training and large-scale adaptation for organisations with substantial proprietary text.",{"slug":22,"name":23,"text":24},"domain-adaptation","Domain adaptation","Teach models the vocabulary, entities and conventions of medicine, law, finance, engineering or your product.",{"slug":26,"name":27,"text":28},"instruction-tuning","Instruction tuning","Train models to follow your task instructions and output schemas reliably from curated instruction datasets.",{"slug":30,"name":31,"text":32},"lora-fine-tuning","LoRA & parameter-efficient tuning","Adapt large models cheaply with LoRA and QLoRA adapters that can be swapped per task or tenant.",{"slug":34,"name":35,"text":36},"rlhf-pipelines","RLHF & preference tuning","Preference datasets, reward modelling and DPO or RLHF pipelines to align tone, safety and quality.",{"slug":38,"name":39,"text":40},"custom-embeddings","Custom embeddings","Fine-tuned embedding models that improve retrieval and search on your domain-specific content.",{"slug":42,"name":43,"text":44},"vision-model-fine-tuning","Vision fine-tuning","Adapt detection, segmentation and vision-language models to your imagery and classes.",{"slug":46,"name":47,"text":48},"audio-model-fine-tuning","Audio & speech fine-tuning","Tune speech-to-text and TTS models for accents, jargon, noise conditions and voices.",{"slug":50,"name":51,"text":52},"multimodal-fine-tuning","Multimodal fine-tuning","Fine-tune vision-language models for document understanding, product imagery and visual QA.",{"slug":54,"name":55,"text":56},"training-data-curation","Training data curation","Build, clean, label and de-duplicate datasets, often with LLM assistance, and document them properly.",{"slug":58,"name":59,"text":60},"model-distillation","Model distillation","Train small, fast models from a large model’s outputs to cut cost and latency for narrow tasks.",[62,65,68,71,74],{"title":63,"text":64},"Consistent structured output","A tuned model returns your schema reliably, cutting parsing failures and retries.",{"title":66,"text":67},"Domain accuracy","Better performance on medical, legal, financial or technical language than generic prompting.",{"title":69,"text":70},"Cheaper, faster inference","Replace a large model plus long prompt with a small tuned model for high-volume tasks.",{"title":72,"text":73},"Brand voice and tone","Writing that sounds like you without a page of instructions on every call.",{"title":75,"text":76},"Better retrieval","Custom embeddings that understand your product names and jargon improve RAG and search.",[78,79,80,81,82],"Higher accuracy on your specific tasks","Lower cost and latency per call","Consistent formats and tone","Weights and datasets you own","Gains proven with evaluation, not assumed",[84,87,90,93,96],{"step":85,"text":86},"Baseline and case for tuning","Measure the best prompt-only approach first; fine-tune only if the gap in quality or cost justifies it.",{"step":88,"text":89},"Dataset build","Curate, label and clean examples, often LLM-assisted, with a held-out evaluation split.",{"step":91,"text":92},"Training runs","LoRA, full fine-tuning or preference tuning with experiment tracking and reproducible configs.",{"step":94,"text":95},"Evaluation","Compare against the baseline on the held-out set and on human review; check for regressions.",{"step":97,"text":98},"Deploy and refresh","Serve the model, monitor quality, and retrain as data and requirements evolve.",[100,101,102,103,104,105,106,107,108,109,110],"Hugging Face","PEFT","TRL","Axolotl","PyTorch","OpenAI fine-tuning","Weights & Biases","MLflow","vLLM","AWS SageMaker","Vertex AI",[112,113,114,115,116],"healthcare","finance","saas-technology","professional-services","manufacturing",[118,121,124,127,130,133],{"q":119,"a":120},"When should we fine-tune instead of prompting or RAG?","Fine-tune when prompting has plateaued on quality, when you need consistent format or tone at high volume, or when a smaller model must do a job cheaply. Use RAG for knowledge that changes. Many production systems combine RAG for facts with a tuned model for behaviour. We always measure the prompt-only baseline first.",{"q":122,"a":123},"How many examples do we need?","For behaviour and format, a few hundred high-quality examples often produce clear gains; for domain adaptation, thousands or more. Quality and coverage matter more than raw volume. If you lack examples we help generate and review them with LLM assistance.",{"q":125,"a":126},"Can you fine-tune hosted models like GPT?","Yes, where the provider offers it: OpenAI and several others support fine-tuning through their APIs, and Bedrock and Vertex offer options for their catalogues. For full control over weights and hosting we tune open models such as Llama, Mistral or Qwen.",{"q":128,"a":129},"Who owns the resulting model?","For open models, you own the weights and adapters outright and can host them anywhere. For hosted-provider fine-tunes, the model lives in your provider account under their terms. Training data and evaluation sets are always yours. We hand over training scripts, configs and evaluation results with the weights so the model can be reproduced or retrained without us.",{"q":131,"a":132},"How do you prevent the model getting worse at other things?","Regression tests on general tasks, mixing in general data, conservative learning rates and parameter-efficient methods such as LoRA that leave the base model largely intact. Every run is compared against the baseline before it ships. If a task truly needs a very different behaviour, we serve a separate adapter for it rather than pushing one model to do everything.",{"q":134,"a":135},"How long does a fine-tuning project take?","A focused LoRA fine-tune with existing data can be evaluated within two to four weeks. Projects that require building datasets from scratch or preference tuning typically take six to ten weeks. The schedule is driven mostly by data preparation and evaluation design; the training runs themselves are usually measured in hours.",[137],"stock-market-deep-learning",[139,140,141,142],"open-source-ai","generative-ai-llm","mlops-ai-infrastructure","rag-systems",{"title":144,"description":145},"LLM Fine-Tuning & Custom AI Model Services | AIYatch","AI fine-tuning: LLM fine-tuning, LoRA, instruction and preference tuning, custom embeddings and multimodal models, evaluated against your baseline.",[147],{"path":148,"title":149,"description":150,"industry":151,"services":152,"results":154,"cover":164,"coverAlt":165},"\u002Fwork\u002Flegal-contract-extraction-fine-tuning","Fine-tuned LLM for clause extraction from commercial contracts","A domain-adapted model that pulls parties, dates, obligations and unusual clauses out of contracts with accuracy a general-purpose model could not reach at the same cost.","SaaS & Technology",[4,153,139],"nlp",[155,158,161],{"value":156,"label":157},"0.91 → 0.96","field-level F1 vs. the prompted frontier model",{"value":159,"label":160},"~85%","lower inference cost per contract",{"value":162,"label":163},"40+","clause types extracted","\u002Fcovers\u002Fwork\u002Flegal-contract-extraction-fine-tuning.svg","Abstract navy cover with a rising cyan course line",[167,172,177],{"path":168,"title":169,"cover":170,"coverAlt":171},"\u002Fblog\u002Fopen-source-llms-on-prem","Open-source LLMs on-prem: when private AI actually makes sense","\u002Fcovers\u002Fblog\u002Fopen-source-llms-on-prem.svg","Line-art illustration of a server rack secured with a padlock, a small model chip glowing inside.",{"path":173,"title":174,"cover":175,"coverAlt":176},"\u002Fblog\u002Fcomputer-vision-defect-detection","Computer vision defect detection on the factory floor: how to get it right","\u002Fcovers\u002Fblog\u002Fcomputer-vision-defect-detection.svg","Line-art illustration of a conveyor belt under a camera with one part flagged by a warning marker.",{"path":178,"title":179,"cover":180,"coverAlt":181},"\u002Fblog\u002Fai-chatbots-for-customer-support","AI chatbots for customer support: what actually works","\u002Fcovers\u002Fblog\u002Fai-chatbots-for-customer-support.svg","Line-art illustration of a support conversation in chat bubbles ending in a check mark, with a hand-off line to a human agent.","\u003Csvg xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"0 0 640 480\" role=\"img\" aria-hidden=\"true\">\u003Cg fill=\"none\" stroke=\"currentColor\" stroke-width=\"5\" stroke-linecap=\"round\" stroke-linejoin=\"round\">\u003Crect x=\"68\" y=\"208\" width=\"64\" height=\"64\" rx=\"6\"\u002F>\u003Crect x=\"85.9\" y=\"225.9\" width=\"28.2\" height=\"28.2\" rx=\"3\" stroke-opacity=\"0.45\"\u002F>\u003Cline x1=\"84\" y1=\"208\" x2=\"84\" y2=\"200\"\u002F>\u003Cline x1=\"84\" y1=\"272\" x2=\"84\" y2=\"280\"\u002F>\u003Cline x1=\"68\" y1=\"224\" x2=\"60\" y2=\"224\"\u002F>\u003Cline x1=\"132\" y1=\"224\" 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