Product / Model Ops

FORGE
LLM.

Private, secure model adaptation for LLMs and vision systems: LoRA, QLoRA, YOLOv8 custom training, evaluation, registry, and edge export through ONNX, TensorRT, and OpenVINO.

Power Cognex RAG Plan Model Pipeline
Closed Loop Model Ops

FROM PRIVATE DATA
TO EDGE DEPLOY.

Forge LLM connects private dataset ingestion, model training, evaluation, registry workflows, and deployment bundles so domain models can be safely improved and shipped into operational environments.

LoRA/QLoRAFine-tune methods
3 backendsModal, AWS, local
MLflowLineage + registry
INT8 edgeTRT + OpenVINO
Pipeline

TRAIN, EVALUATE,
EXPORT.

Forge Pipeline Architecture

Forge LLM pipeline architecture from private data ingestion through data preparation, compute and fine-tuning, evaluation, model registry, and edge export

Method Selection

LoRA

Recommended for most LLM adaptation

Trains low-rank adapters while base weights remain frozen, giving strong domain adaptation with fewer trainable parameters.

QLoRA

Memory-efficient large model tuning

Uses 4-bit NF4 base quantization plus adapters to tune larger models under constrained GPU budgets.

Full fine-tune

Maximum adaptation fidelity

Updates all weights for specialized domains when adapter accuracy is not sufficient.

YOLO custom training

Vision models for edge targets

Trains custom detection models and exports ONNX/TensorRT/OpenVINO-ready weights.

Domain Use Cases

MODELS THAT
SPEAK THE DOMAIN.

01 / Drone Ops

Operations Language Model

Fine-tune on telemetry logs, maintenance SOPs, flight manuals, and incidents so Cognex RAG understands fault codes, mission profiles, and battery patterns.

02 / Healthcare Imaging

Medical Vision Model

YOLOv8 training on private imaging datasets for anomaly detection and edge deployment without data leaving controlled infrastructure.

03 / Surgical AI

Procedure + Instrument Model

QLoRA fine-tuning on procedure transcripts, instrument catalogs, and documentation for multimodal clinical workflows.

04 / IoT Inspection

Device Quality Inspection

Train vision models to detect correct mounting, cable routing, damage, and tamper indicators on installed IoT devices.

05 / Industrial Language

Maintenance Domain LLM

Adapt models to work orders, part numbers, failure modes, inspection reports, and regulatory language.

06 / Edge Camera

Custom Asset Detection

Train YOLOv8n/s on organization-specific assets, quantize to INT8, and deploy to Jetson or Intel edge targets.

Engineering Challenges

SAFE MODEL
ADAPTATION.

Critical

Catastrophic Forgetting

Full fine-tunes can overfit domain data and lose instruction-following skill. Forge mixes domain data with general instruction samples and uses early-stopping eval gates.

Critical

Private Medical Data

For healthcare data sovereignty, Forge supports air-gapped local GPU runs with audit logging, dataset version tracking, and model checksum records.

Critical

QLoRA Accuracy Loss

Safety-critical tasks may require NF4 for the model bulk but FP16 retained on final classification layers, plus A/B evaluation against LoRA baselines.

High

ONNX Operator Compatibility

Forge runs pre-export operator audits and post-export tolerance validation to catch PyTorch-to-ONNX and TensorRT compilation issues.

High

Backend Reproducibility

Modal, SageMaker, and local GPUs can differ subtly. Forge pins containers, captures environment fingerprints, and validates outputs within tolerance.

Medium

Small Noisy Corpora

Low-data domains use grounded synthetic instruction pairs, paraphrase augmentation, and conservative LoRA ranks to reduce memorization.

Technology Stack

PRIVATE TRAINING
TO EDGE RUNTIME.

Fine-Tuning Framework

Hugging Face + PEFT

Transformers, PEFT adapters, bitsandbytes NF4 quantization, and TRL SFTTrainer for instruction-format training.

LLM Method

LoRA / QLoRA

Adapter ranks tuned per task, QLoRA for 34B+ models, mixed precision gates for safety-critical classification.

Vision Training

YOLOv8

Custom object classes, COCO-format datasets, auto-augmentation, ONNX export, TensorRT and OpenVINO compilation.

Serverless GPU

Modal

A100/H100 jobs with pinned containers, spot options, environment fingerprinting, and zero idle GPU cost.

Managed Cloud

AWS SageMaker

VPC isolation, IAM-scoped access, CloudTrail audit logging, and multi-GPU training jobs for enterprise runs.

Air-Gapped

Local GPU

On-premise containerized training for healthcare, defense, and sovereignty-sensitive datasets.

Registry

MLflow

Tracks hyperparameters, loss curves, eval metrics, dataset hashes, environment fingerprints, and promotion gates.

Universal Export

ONNX

Primary model export with operator audits and PyTorch-vs-ONNX validation before runtime compilation.

Edge Compile

TensorRT + OpenVINO

INT8 calibration, IR compilation, runtime configs, model cards, and versioned edge deployment bundles.

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