LLM Fine-Tuning Services
InterCode fine-tunes large language models for domain-specific performance, custom brand voice, and specialized task accuracy. Using LoRA, QLoRA, and instruction tuning techniques, we train models that outperform general-purpose LLMs on your specific use cases — at a fraction of the cost of full fine-tuning.
Custom LLMs Trained on Your Domain
Fine-tuning allows you to adapt a pretrained LLM to produce outputs with a specific style, follow domain-specific instructions, use proprietary terminology correctly, or perform specialized tasks that general models handle poorly. When done correctly, a fine-tuned smaller model can match or exceed the performance of a much larger general model on your specific task — at lower latency and cost. At InterCode, we treat fine-tuning as an engineering discipline with rigorous dataset design, training, and evaluation. Parameter-efficient fine-tuning methods like LoRA (Low-Rank Adaptation) and QLoRA (quantized LoRA) have made fine-tuning accessible without requiring massive GPU clusters. We fine-tune models ranging from 7B to 70B parameters using these techniques on consumer-grade A100 or H100 hardware, delivering adapted models in days rather than weeks. For instruction following and alignment, we apply supervised fine-tuning on curated instruction datasets and, when appropriate, RLHF (Reinforcement Learning from Human Feedback) to align outputs with human preferences. Dataset preparation is the most critical and time-consuming part of fine-tuning. We help you design data collection strategies, clean and deduplicate datasets, format examples correctly for instruction tuning, and build evaluation sets that measure the capabilities you care about. We benchmark fine-tuned models against base models and GPT-4 on your task-specific test suite, and we deploy fine-tuned models via Hugging Face Inference Endpoints, Together AI, or your own infrastructure.
Fine-Tuning Projects We Deliver
We fine-tune GPT-3.5 and open-source models (Llama 3, Mistral) for specific brand voice and writing style, making AI-generated content indistinguishable from editorial output. Domain adaptation projects include healthcare documentation assistants trained on clinical notes, legal drafting assistants fine-tuned on contract language, and finance-specific models trained on earnings reports and filings. Code model fine-tuning for proprietary APIs and frameworks allows models to generate correct code for internal libraries without hallucinating non-existent methods. Customer service models trained on historical support ticket resolution dramatically improve first-contact resolution rates.
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Learn moreFrequently Asked Questions
Fine-tuning is best for changing how a model writes or reasons — style, tone, format, or specialized task performance. RAG is best for grounding responses in specific knowledge that changes over time. They are complementary: fine-tune a model for your domain's writing style and terminology, then use RAG to give it access to current information. Most enterprise use cases start with RAG and add fine-tuning later when RAG alone hits accuracy ceilings.
A LoRA fine-tuning run on a 7B-13B model with a curated dataset of 1,000-10,000 examples costs $500-3,000 in compute, plus 2-4 weeks of engineering time for dataset preparation, training, and evaluation. Fine-tuning larger models (70B+) or doing full fine-tuning (not LoRA) costs $5,000-30,000 in compute. Dataset preparation is usually the largest cost driver. We provide a detailed cost estimate after reviewing your task and data.
For LoRA fine-tuning on a focused task, 500-5,000 high-quality examples often produce measurable improvements. Instruction tuning benefits from 10,000-100,000 diverse examples covering your task space. More data almost always helps, but quality matters more than quantity — 500 expertly curated examples typically outperform 5,000 noisy ones. We audit your available data and advise on collection strategies before committing to a training run.
Dataset preparation takes 1-3 weeks depending on data availability. A LoRA training run on a 7B model takes 4-12 hours on 4x A100 GPUs. Evaluation, iteration, and deployment add another 1-2 weeks. Total project timeline from kickoff to deployed model is typically 4-8 weeks. Full fine-tuning of large models or multi-epoch training runs take longer.
Open-source models — Llama 3, Mistral, Mixtral, Phi-3, Gemma — can be fine-tuned and self-hosted. OpenAI offers fine-tuning for GPT-3.5 Turbo and GPT-4o mini. Anthropic does not currently offer Claude fine-tuning publicly. For most use cases we recommend starting with Llama 3 or Mistral fine-tuning because self-hosted models give you full control over data privacy and deployment.
Fine-Tune Your LLM
Tell us about your target task, available data, and performance requirements. We will design a fine-tuning strategy that delivers measurable improvements over the base model.
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