MODELFORGE · BY TRISTATE LABS

Fine-tune open models with your data.

Bring your dataset. Choose an open-weight model. Get a model adapted to your specific task—or use it through a managed inference API.

For students · researchers · independent builders · early-stage startups

MODELFORGE / SERVICE BRIEF01—03

Adaptation without the infrastructure overhead.MODEL · DATA · EVALUATION

THE SIMPLE VERSION

You have the data.
We handle the compute.

Open-weight models are increasingly capable, but adapting one to a specialized task still requires the right dataset, training configuration, GPU infrastructure, evaluation process, and model-serving setup.

WHY MODELFORGE

We handle the compute-heavy workflow so you can concentrate on your project, research, or prototype.

Bring Your Dataset

Use your own domain-specific data to adapt a compatible open-weight model.

Skip the GPU Setup

Don’t spend your project building training infrastructure from scratch.

Choose How You Receive It

Get the trained model checkpoint or optionally use it through managed inference.

WHO IT'S FOR

Useful for the curious.

Focused support for people with a question, a dataset, and a reason to experiment.

Students

Build the project. Not the GPU infrastructure.

For final-year projects, academic experiments, hackathons, and personal AI projects.

Final-year projects
AI/ML projects
Academic demonstrations
Hackathons
Experimental models

Researchers

Spend your time on the research.

For professors, PhD students, master’s students, and independent researchers experimenting with model adaptation.

Research experiments
Domain-specific models
Experimental fine-tuning
Benchmarking
Academic prototypes

Independent Builders

Have a dataset and an idea?

Experiment with open-weight models without setting up the entire training stack yourself.

Custom experiments
Open-weight models
Rapid iteration

Early-Stage Startups

Validate the model before building the infrastructure.

Fine-tune a model, connect it to a prototype through an API, evaluate the result, and decide whether it is worth scaling.

Proofs of concept
API prototypes
Model evaluation

HOW IT WORKS

A clear path from idea
to trained model.

No black boxes. We start by understanding what you are trying to learn, then determine whether fine-tuning is the sensible approach.

Tell Us What You’re Building

Describe your project, research problem, dataset, and desired outcome.

Model & Dataset Review

We review the dataset and selected model to determine whether fine-tuning is appropriate.

Fine-Tuning

The selected open-weight model is fine-tuned using the agreed training configuration and infrastructure.

Receive Your Model

Choose a model checkpoint or use the trained model through managed inference.

SERVICES

Pick the output
that fits the work.

No fake pricing. We scope each request around the model, dataset, and intended use.

MODEL CHECKPOINT

Train it. Get the model.

For customers who want the trained model checkpoint and control over deployment.

Custom dataset
Compatible open-weight model
Fine-tuning
Training configuration
Trained model checkpoint
Basic training information

MANAGED INFERENCE

Train it. Call it through an API.

For customers who want to use their fine-tuned model without managing the serving infrastructure.

Everything in Model Checkpoint
Hosted inference
API endpoint
Model serving
Prototype-friendly deployment

FOR STUDENTS & RESEARCHERS

Don't spend your research project fighting with GPUs.

Your project should be about the question you're trying to answer—not configuring CUDA, managing GPU instances, debugging training environments, or figuring out model serving.

ModelForge provides the fine-tuning service. You remain in control of your research, evaluation, application, and academic work.

01Research Question

02Dataset

03Model

04Fine-Tuning

05Experiment / Evaluation

06Result

FOR EARLY-STAGE STARTUPS

Prove the idea before building the stack.

At the prototype stage, you may not know whether a customized model is worth building an entire infrastructure around. ModelForge lets you test the idea first.

A PRACTICAL FIRST STEPTest the model before committing to production infrastructure.MODEL EVALUATION · API PROTOTYPE · NEXT DECISION

POSSIBILITIES

What could you fine-tune?

Fine-tuning is one tool among several. Depending on the problem, prompting, RAG, or another approach may be more appropriate.

Domain-Specific Language

Adapt a model to specialized terminology, writing styles, or domain-specific datasets.

Classification

Custom text, document, and intent classification for specialized tasks.

Structured Output

Train models to produce consistent structured responses.

Instruction Following

Adapt a model to follow specialized instructions and task formats.

Academic Experiments

Experiment with model behavior, datasets, training techniques, and evaluation.

Startup Prototypes

Test whether a customized model can power an early product concept.

UNDER THE HOOD

Open models. Your data.
Your experiment.

Technical enough to be useful, understandable enough to keep the work moving.

Open-weight language models

Supervised Fine-Tuning

LoRA / PEFT

Custom datasets

Model evaluation

Model checkpoints

API inference

Compatibility matters.

Model compatibility depends on architecture, licensing, dataset format, and infrastructure requirements. We do not claim support for every model.

GETTING STARTED

What you provide.

To get started, you typically provide:

DatasetDescription of the taskDesired model behaviorPreferred base model, if knownApproximate dataset sizeEvaluation requirements, if availablePreferred delivery option

Not sure whether your dataset is suitable?

That's okay. Tell us what you're working with and we'll assess the setup first.

QUESTIONS

Before you start.

A few straightforward answers about the service and what to expect.

What is fine-tuning?

Do I need my own GPU?

Can I use my own dataset?

Can I choose the model?

Do I receive the trained model?

Can I use the model through an API?

Is fine-tuning always better than RAG?

Can students use ModelForge for college projects?

Can startups use ModelForge for an MVP?

TRANSPARENCY

A focused service, without the infrastructure overhead.

ModelForge focuses on one thing: helping you adapt open-weight models to your dataset.

Training and inference infrastructure may be fulfilled through specialized infrastructure providers. The customer experience remains centered on the model, dataset, and result—not on managing cloud infrastructure.

READY WHEN YOU ARE

Have a dataset?
Have an idea?

Tell us about your dataset, model, and project. We'll determine whether fine-tuning is a sensible approach and outline the next step.