LLM Fine Tuning Agency

Custom LLM Fine-Tuning & Private GPU Model Deployments

We curate specialized training datasets, fine-tune open-source LLMs (Llama 3, Mistral, Qwen) using LoRA/QLoRA, and host private models on GPU cloud infrastructures — protecting data privacy and reducing API costs.

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Direct Desk: ai@trustoryx.digital

80%

Cost Savings

100%

Data Privacy

15+

Models Shipped

vLLM

Fast Inference

Growth Obstacles

Problems This Solves

Commercial APIs like OpenAI are too expensive for your high-volume prompt and token demands.

Sending sensitive customer data to third-party APIs violates GDPR, HIPAA, or strict company data rules.

Off-the-shelf AI models do not understand your brand tone, industry terminology, or custom JSON schemas.

You struggle to host open-source models efficiently, resulting in slow generation speeds and high server bills.

Methodology

Our Proven Process

Dataset Engineering

We gather conversation logs, clean formats, and synthesize JSONL training and test instruction pairs.

QLoRA Training Setup

We configure PEFT adapters and fine-tune model parameters on cloud GPUs while checking validation loss.

Alignment & Evaluation

We align model behaviors using Direct Preference Optimization (DPO) and run benchmark tests.

Quantization & Compression

We quantize model weights to 4-bit or 8-bit formats (AWQ/GGUF) to fit smaller GPU memory cards.

Private GPU Deployment

We deploy inference controllers using vLLM or Ollama inside your private VPC or cloud servers.

Dataset Engineering

We gather conversation logs, clean formats, and synthesize JSONL training and test instruction pairs.

QLoRA Training Setup

We configure PEFT adapters and fine-tune model parameters on cloud GPUs while checking validation loss.

Alignment & Evaluation

We align model behaviors using Direct Preference Optimization (DPO) and run benchmark tests.

Quantization & Compression

We quantize model weights to 4-bit or 8-bit formats (AWQ/GGUF) to fit smaller GPU memory cards.

Private GPU Deployment

We deploy inference controllers using vLLM or Ollama inside your private VPC or cloud servers.

Scope of Work

What's Included

Fine-tuned model adapter weights (HuggingFace compatible LoRA / PEFT files)

Validated instruction dataset training logs (JSONL training and validation files)

Quantized model binaries optimized for production hosting (AWQ, GPTQ, or GGUF formats)

Private inference API wrapper code (vLLM / Ollama configuration configs)

Model validation metrics reports detailing perplexity and validation loss curves

Docker configurations and Kubernetes GPU deployment manifests

30-day post-launch deployment support and model refinement warranty

Growth Targets

Expected Results

Up to 80% cost reduction per million tokens compared to OpenAI APIs

100% data privacy with models hosted entirely inside your corporate VPC

High-speed token generation using vLLM asynchronous paging engines

Strict output formatting adhering to custom JSON schema definitions

4-bit or 8-bit quantization enabling Llama 3 70B execution on single nodes

Tech Stack

Technologies We Master

HuggingFace

Llama 3

Mistral

QLoRA

vLLM

Ollama

Docker

AWS GPU

Exhaustive Solutions

Comprehensive Capabilities

We don't just scratch the surface. Here is a detailed breakdown of everything we can engineer, optimize, and execute for your business.

Dataset Curation

Cleaning and formatting dialogue datasets, resolving duplicate samples, and generating synthetic pairs.

LoRA & QLoRA Fine-Tuning

Configuring PEFT parameters and training open-source models using CUDA nodes.

Model Quantization

Compressing model weights to 4-bit or 8-bit configurations to fit smaller hardware memory constraints.

High-Throughput Inference

Integrating vLLM server pools with page-attention controls to serve parallel client connections.

Direct Preference Optimization (DPO)

Aligning model output styles with target brand voice rules using preference pairs.

GPU Cloud Infrastructure

Deploying model containers on dedicated GPU nodes with CUDA and PyTorch runtimes.

Specialized Focus

Specialized Services

Explore our specialized engineering teams and tailored solutions for this domain.

AI & Automation

Custom AI automation workflows.

OpenAI Integration

Custom integrations using OpenAI models.

LangChain Development

Expert LangChain AI agents development.

RAG & Vector Databases

Custom semantic search systems and vector databases.

Transparent Pricing

Investment Plans

Transparent pricing with no hidden fees. Every plan includes dedicated support and monthly reporting.

Fine-Tuning MVP

$4,500USD · USD fallback/starting

QLoRA fine-tuning of up to 1,000 instruction pairs on a 7B model like Llama 3 or Mistral.

Up to 1,000 instruction pairs curated

QLoRA training on 7B base model

Validation loss checking & reports

GGUF or AWQ quantization setup

Inference server setup (Ollama)

Basic API controller wrapper

14-day post-launch support

Full weights and dataset ownership

Build Fine-Tuning MVP

Enterprise Private LLM

$9,500USD · USD fallback/starting

Large datasets (up to 10,000 rows), fine-tuning on 7B to 70B models, DPO alignment, and vLLM hosting.

Up to 10,000 instruction pairs curated

Tuning on models up to 70B parameters

Direct Preference Optimization (DPO)

Model evaluation benchmark tests

vLLM high-speed inference setup

Private cloud GPU provisioning

30-day post-launch support

Inference speed optimization guides

Build Private LLM

Custom Model Architecture

$20,000USD · USD fallback+/project

Domain-specific pre-training or fine-tuning, complex multi-GPU cluster setups, and custom evaluation.

Domain-specific base pre-training

Multi-GPU cluster configurations setup

Custom dataset synthesis scripts

Advanced RLHF alignment layers

Kubernetes GPU cluster deployments

Priority SLA support contracts

60-day post-launch support

24/7 Slack communication channel

Discuss Custom AI Model

All plans are month-to-month with no long-term contracts. Custom enterprise plans available.Contact us for a tailored proposal.

Why Trustoryx

Why Choose Us

No Long-Term Contracts

Month-to-month engagements. We earn your business every single month.

Dedicated Team

A named strategist, not a rotating cast of juniors. Consistent point of contact.

Revenue-Focused

We report on revenue impact, not vanity metrics. Every dollar is attributed.

Rapid Execution

Strategy in week 1. Execution by week 2. Results tracked from day one.

Support

Frequently Asked Questions

RAG is best for supplying a model with specific documents or database records. Fine-tuning is best for teaching a model a new behavior, writing style, specific jargon, or output format (like strict JSON), or to lower token costs and keep data private.

LoRA freezes base weights and trains a small set of adapter weights. QLoRA quantizes the base model to 4-bit before training, allowing us to fine-tune large models on a single GPU node.

We deploy models in your private cloud (AWS, GCP, Azure) using GPU instances, or on dedicated GPU clouds like RunPod, Lambda Labs, or Hostkey to save hosting costs.

We split datasets into train and validation sets, trace loss curves, and run evaluation scripts using LM-Eval Harness or custom prompts to verify accuracy.

Ready to Get Started?

Start with a free audit. We'll analyze your current performance and show you exactly where the growth opportunities are.

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Or email our dedicated desk: ai@trustoryx.digital