A Model Tuned to Your Domain, Proven Against the Baseline
From LoRA adapters and full fine-tunes to RLHF and synthetic data - hire verified ML engineers who train models on your data and prove the lift with a repeatable eval harness.
What Is AI Training & Fine-Tuning?
Fine-tuning takes a strong base model and specializes it on your data - your terminology, formats, tone, and edge cases. Parameter-efficient methods like LoRA make this affordable: instead of retraining billions of weights, a small adapter learns your domain in hours on a single GPU. The payoff is a model that outperforms prompting alone on your specific task, often while running on a smaller, cheaper base.
Serious training work is measurement-first. Before any GPU spins up, an engineer builds an evaluation harness - a held-out test set and scoring rubric that captures what 'good' means for your task. Training data gets cleaned and formatted, synthetic examples fill coverage gaps, and every checkpoint is benchmarked against the base model so you can see exactly what the fine-tune bought you. Without that harness, 'it feels better' is all you get.
Whether you need a support model that speaks your product's language, a structured-extraction model for a niche document type, a distilled small model to cut inference costs, or preference tuning (RLHF/DPO) to align outputs with your quality bar - this category connects you with engineers who deliver measured improvements, packaged for deployment.
When Do You Need Fine-Tuning?
Common scenarios where a tuned model beats prompting a bigger one.
Domain Specialization
Teach a model your industry vocabulary, house style, and edge cases - legal clauses, medical notes, or your support macros - beyond what a prompt can hold.
Cost & Latency Reduction
Distill a frontier-model workflow into a small open model that runs faster and cheaper - keeping quality where it matters with eval-verified parity.
Eval Harness Development
A repeatable benchmark for your task: held-out test sets, LLM-judge rubrics, and regression tracking so every model change is measured, not vibed.
Synthetic Data Generation
Fill dataset gaps with generated, validated examples - rare edge cases, minority classes, or privacy-safe stand-ins for sensitive records.
Preference Tuning (RLHF / DPO)
Align model outputs with your quality bar using human or AI preference data - tone, safety, formatting, and refusal behavior tuned to spec.
Structured Output Models
Fine-tune for reliable JSON extraction from your document types - invoices, contracts, or forms - where prompting alone keeps missing fields.
Example Projects
Real project briefs showing the kind of training work our specialists deliver.
Support-Tone LoRA for a SaaS Help Desk
Fine-tuned an open 8B model on 6,000 curated support conversations to match product terminology and house tone. The eval harness showed clear win-rate improvement over the prompted base model, at a fraction of the previous per-token cost.
Contract-Clause Extraction Model
Built a training set from 4,000 annotated agreements, generated synthetic examples for rare clause types, and fine-tuned for structured JSON extraction with per-field accuracy tracked against a held-out set.
Frontier-to-Small Model Distillation
Distilled a GPT-4-class classification workflow into a small open model for on-premises deployment, generating 50k teacher-labeled examples and verifying near-parity on the eval suite while cutting inference cost dramatically.
DPO Alignment Pass for a Consumer Chatbot
Collected preference pairs from production conversations, ran direct preference optimization on the deployed model, and validated tone, safety, and refusal behavior against a 300-case rubric before rollout.
What You'll Get
- A tuned model (LoRA adapter or full fine-tune) packaged for deployment
- Eval harness with held-out test sets, rubrics, and benchmark results
- Before/after report: your model vs. the base model on YOUR task
- Cleaned, formatted training dataset with documented preprocessing
- Synthetic data generation where coverage gaps need filling
- Training pipeline (Axolotl / Unsloth / HF Trainer) you can re-run
- Deployment guidance: serving options, quantization, and cost estimates
- Model card documenting data, methods, limits, and license terms
Tech Stack & Tools
Ecosystem at a glance
Skills You'll Get Access To
Every professional matched to your project is verified in these core competencies.
Timeline & Budget Guide
Typical ranges to help you plan. Actual costs depend on data readiness, model size, and how rigorous the evaluation needs to be.
LoRA fine-tune on an existing clean dataset with a focused eval set and packaged adapter
Data curation + synthetic augmentation, tuned model, full eval harness with base-model benchmarking
Preference tuning (RLHF/DPO), distillation programs, multi-model comparisons, or production serving with monitoring
What REWORK Provides
We don't just connect you with talent - we support the entire project lifecycle.
AI Brief Generation
Describe your task and data in plain language and our AI generates a detailed project brief with scope, deliverables, and budget estimates.
Escrow Protection
Funds are held securely until milestones are met. You only pay for completed, approved work.
Professional Matching
We match you with verified ML engineers based on your model family, data situation, and deployment target.
Project Management Tools
Built-in milestone tracking, file sharing, and communication tools to keep your project on track.
Start Your Fine-Tuning Project Today
Describe your task and data, get an AI-generated project brief, and get matched with a verified ML engineer - all with escrow-protected delivery.