LLM Fine-Tuning (SFT / DPO / RLHF / LoRA / QLoRA)
LLM Fine-Tuning Services to Enhance Your Applications with Powerful AI Capabilities
Our LLM fine-tuning services adapt open-source models to your domain, tone, and output requirements — so you get a model that behaves exactly the way your product needs, without sending sensitive data to a third-party API on every request.
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The Problem With General-Purpose Models
General-purpose LLMs are trained to be good at everything, which means they're optimized for nothing specific. They don't know your terminology, your output format requirements, your brand voice, or the nuanced reasoning patterns your domain demands. You can get part of the way there with prompting — but there's a ceiling, and for proprietary data, regulated industries, or latency-sensitive applications, a fine-tuned open-source model running on your own infrastructure is the correct answer.
Fine-Tuning vs. RAG vs. Prompting
Prompting Only
Best for: General tasks, no domain-specific output requirements
Data required: None — works with the base model
Output consistency: Moderate — varies with prompt phrasing
Data privacy: All inputs sent to a third-party provider API
Cost at scale: High — expensive model called on every request
RAG
Best for: Grounding answers in your documents or knowledge base
Data required: Your documents, indexed in a vector store
Output consistency: High for factual retrieval — lower for stylistic output
Data privacy: Documents retrieved per query, inputs still sent to provider API
Cost at scale: Moderate — retrieval adds cost, model call still required
Fine-Tuning
Best for: Teaching a model a skill, style, format, or domain reasoning pattern that prompting can't reliably produce
Data required: High-quality labeled training data (typically 500–50,000 examples)
Output consistency: Highest — behavior baked into weights, not dependent on prompt
Data privacy: Model runs on your own infrastructure — no data leaves your environment
Cost at scale: Lowest per-inference once trained — no API call needed
Most production systems use fine-tuning and RAG together: a fine-tuned model for consistent style and domain reasoning, RAG for up-to-date factual grounding. Our AI Strategy & Architecture Audit will tell you exactly which combination fits your use case.
What We Build
Supervised Fine-Tuning (SFT) — train on labeled input/output pairs to teach the model your required output format, tone, or domain behavior
DPO (Direct Preference Optimization) — align model outputs to human preferences without a separate reward model, faster and more stable than RLHF for most use cases
RLHF — full reinforcement learning from human feedback for complex alignment requirements
LoRA / QLoRA fine-tuning — parameter-efficient training that adapts large models on modest GPU hardware without full retraining, dramatically reducing training cost
Training data preparation — cleaning, formatting, and augmenting your raw data into a training-ready dataset
Evaluation harness — benchmark the fine-tuned model against the base model and measure improvement on your target tasks before delivery
Model packaging and deployment — delivered as a containerized model ready to self-host, or guidance for hosted deployment on providers like Together AI or Replicate
Who This Is For
Companies with proprietary data that can't be sent to a third-party API on every inference request
Regulated industries (healthcare, legal, finance) needing models that run entirely within their own infrastructure
Products requiring strict output formatting — structured JSON, specific schema, domain-specific terminology — that prompting alone can't reliably produce
High-volume applications where per-request API costs make a self-hosted fine-tuned model significantly cheaper at scale
Teams building domain-specific tools — legal document review, medical coding, engineering specification analysis — where a general model's reasoning is not good enough
Trusted Across 50+ Countries
Codersarts maintains a 4.9/5 client satisfaction rating across hundreds of engagements. Clients highlight deep technical follow-through — Tan (Malaysia) described the team's explanations as the difference between getting stuck and moving forward on a complex project, while Li (China) pointed to the team's thoroughness on a multi-part technical engagement.
Results
A legal technology company fine-tuned a 7B open-source model on contract review data, achieving accuracy on clause classification that matched a much larger general-purpose model at roughly 80% lower per-inference cost.
A healthcare documentation platform fine-tuned a model on clinical note formats, eliminating the need to send patient data to a third-party API while meeting their output consistency requirements.
A financial services firm used DPO fine-tuning to align a model's tone and reasoning style to their compliance guidelines, replacing 12 pages of system-prompt engineering that still produced inconsistent results.
(Client names withheld under NDA; case studies available on request.)
Pricing
Starter
Scope: LoRA/QLoRA fine-tuning on a 7B–13B model, data prep up to 5K examples, evaluation report
Price: $8,000–$15,000
Production
Scope: SFT or DPO on 13B–34B model, full data pipeline, eval harness, containerized delivery
Price: $15,000–$30,000
Enterprise
Scope: RLHF or large-scale SFT/DPO on 34B–70B models, multi-round training, on-prem deployment guidance
Price: $30,000–$40,000+
For context: enterprise fine-tuning engagements on 7B–70B open-source models run $150,000–$750,000 all-in in the US market, with data preparation accounting for 30–50% of total cost. Our pricing reflects high-quality offshore delivery at a fraction of those rates — the same engineering rigor applied to your dataset and training run.
How We Work
Data audit (Week 1) — assess your raw data, define the training task, and identify gaps
Data preparation (Weeks 1–2) — clean, format, and augment to training-ready quality
Training (Weeks 2–4) — fine-tuning runs with hyperparameter tuning
Evaluation (Week 5) — benchmark fine-tuned vs. base model on your target tasks
Delivery — containerized model, training artifacts, and evaluation report
Why Codersarts
As a LoRA fine-tuning company, we treat training data as the primary determinant of output quality — not the model or the training recipe. Most fine-tuning projects fail on data quality, not on training. We spend more time on data preparation than most providers and surface data quality issues before they become a failed training run. You get a fixed-scope engagement with a clear deliverable — a benchmarked, deployable model — not an open-ended research engagement billed by the hour.
Related Services
RAG Engineering & Deployment — the most common complement to fine-tuning; knowledge grounding on top of a domain-adapted model
LLM Evaluation & Benchmark Engineering — a deeper eval harness if your fine-tuning use case requires ongoing regression testing
Private / On-Prem LLM Deployment — for self-hosting the fine-tuned model in your own infrastructure
AI Strategy & Architecture Audit — if you're unsure whether fine-tuning, RAG, or prompting is the right answer
Get Started
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FAQ
How much training data do we need? It depends on the task. LoRA fine-tuning for style or format adaptation can work with as few as 500–1,000 high-quality examples. More complex domain reasoning tasks typically need 5,000–50,000 examples. We assess your data in the first week and tell you exactly where you stand.
Which base models do you work with? Llama 3, Mistral, Gemma, Qwen, and other leading open-source models. We recommend based on your task requirements, hardware constraints, and whether you need a commercially licensable model.
What if we don't have enough training data? Data augmentation and synthetic data generation are part of our standard data preparation work. For some tasks, we can generate high-quality synthetic training examples from your existing documents or specifications.
Will the fine-tuned model keep improving over time? Only if you do further training runs — the model itself is static after training. For knowledge that changes frequently, RAG is a better fit. We typically recommend a fine-tuned model for stable skills and behavior, with RAG layered on top for up-to-date factual grounding.
Do you handle GPU infrastructure for training? Yes — we manage the training infrastructure as part of the engagement and deliver the trained model artifacts. You don't need your own GPU cluster.