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LLM Fine-Tuning & Custom Models

Custom Foundation Models

For nations, massive enterprises, and research institutions looking for complete independence from Big Tech. We architect and execute the pre-training of custom Foundation Models from scratch using your massive proprietary datasets, creating an asset that your organization owns 100%.

Pre-TrainingData MoatsSovereign AIHPC Clusters
100%
Sovereignty
Ensured complete national data sovereignty for a regional government's NLP model.
Terabytes
Data Digested
Successfully orchestrated the deduplication and tokenization of massive archival datasets.
Expert Led
Arsalan Abbas
Principal AI Scientist
Deep Learning ExpertsHPC Architecture
Capabilities

Core Features

Total IP Ownership

You own the model weights, the architecture, and the training data. A permanent, compounding asset for your enterprise balance sheet.

Sovereign Language & Culture

Pre-training models on underrepresented languages, regional dialects, and cultural nuances that Western models like GPT-4 ignore.

Novel Modalities

Training models not just on text, but on proprietary modalities like DNA sequences, financial tick data, or seismographic telemetry.

Uncensored Alignment

Complete control over the model's alignment, safety filters, and behavior, free from external corporate censorship policies.

Implementation

Our Process

01

Feasibility & HPC Sizing

Month 1

Massive scale pre-training requires immense compute. We calculate the exact parameter size, token count, and GPU cluster hours required to reach convergence.

02

Massive Data Preparation

Month 2-3

Building distributed pipelines using Spark/Ray to ingest, deduplicate, filter, and tokenize Terabytes to Petabytes of raw pre-training data.

03

Distributed Pre-Training

Month 4-6

Orchestrating the training run across hundreds or thousands of GPUs using Megatron-LM or DeepSpeed. Managing node failures and checkpointing.

04

Post-Training (SFT & RLHF)

Month 7

The base model is just a text predictor. We align it into a useful assistant via Supervised Fine-Tuning and Reinforcement Learning.

05

Benchmarking & Release

Month 8

Evaluating the model across standard academic benchmarks (MMLU, HumanEval) and custom domain-specific tests before production deployment.

Tech Stack

Technologies We Use

PyTorch / JAX
Core Deep Learning Framework
Megatron-LM / DeepSpeed
Distributed Training
Ray / Apache Spark
Distributed Data Processing
NVIDIA DGX SuperPODs
HPC Infrastructure
W&B / TensorBoard
Training Telemetry
Common Questions

FAQ

How much does it cost to train a foundation model from scratch?

Why wouldn't we just fine-tune an existing model?

What happens if a GPU fails during the months-long training?

Ready to Innovate?

Accelerate Your Business with
Custom Foundation Models

Book a free strategy call. We'll scope the exact requirements for your use case and walk you through our implementation approach.

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