Model Fine Tuning

Fine-tune when prompting isn't enough

Prompting gets you far. When you need consistency on a narrow task, lower latency, or domain-specific outputs that prompt engineering can't deliver, fine-tuning is the next step. We handle the full loop: dataset, training, evaluation, deployment.

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01Overview

What we deliver

Fine-tuning is overused early (when prompts would work) and underused later (when prompts are hitting a ceiling). We help you decide when it makes sense, prepare the dataset properly, run the training, and evaluate rigorously before you ship.

02Why choose this service

Key benefits

Dataset quality first

Most fine-tuning projects fail on the data, not the training. We focus on dataset design, labeling, and validation before a GPU runs.

Rigorous evaluation

Held-out test sets, domain-specific metrics, and head-to-head comparisons with strong prompting baselines.

Right tool for the job

Supervised fine-tuning, LoRA adapters, DPO, or classic ML. We pick what fits, not what's trendy.

Production deployment

Containerized models, monitoring, and retraining pipelines so the tuned model stays good as data evolves.

03How we work

Our process

Feasibility & Baseline

Prove prompting won't work first. Measure where it falls short and whether fine-tuning can close the gap.

Dataset Preparation

Gather, clean, label, and validate the training data. Usually the longest step.

Training & Evaluation

Run training, evaluate on held-out data, compare against baseline, iterate on data and hyperparameters.

Deploy & Monitor

Containerize, deploy, and set up drift monitoring and scheduled retraining.

04Applications

Common use cases

01Domain-specific content generation with strict style constraints

02Classification tasks where prompting hits an accuracy ceiling

03Lower-latency inference on narrow tasks

04Preference-tuned response style for a specific audience

05Task-specific small models that replace larger general-purpose LLMs

05Technologies

Tools we use

OpenAI Fine-tuning APIHugging FaceLoRA / QLoRADPOPyTorchScikit-learnWeights & BiasesAWS SageMaker

06FAQ

Common questions

Should we fine-tune, or is prompting enough?

Usually prompting first. We fine-tune when prompting can't hit your accuracy, latency, or consistency bar.

How much training data do we need?

A few hundred well-labeled examples for narrow tasks, thousands for broader ones. Quality matters more than volume.

07Explore more

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How can we help you?

We'll tell you how we'd build it, and how fast.

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