THE VLMOPS PLATFORM.
ANNOTATE. FINE-TUNE. DEPLOY.
Go from raw images to a production-ready Vision Language Model in one platform. Phrase grounding, visual Q&A, chain-of-thought, with managed training on GPUs from T4 to H100.
01_INGEST
02_CONTEXT
03_REFINE
04_VERIFY
05_SCALE
Dataset
Annotate
Train
Evaluate
Deploy
10+
Model Architectures
T4 to B200
GPU Support
SOC 2
Type II Compliant
OpenAI
Compatible API
Trusted By Teams At
WHAT IS DATATURE VI
END-TO-END VLM
FINE-TUNING OPERATIONS.
01 // LABEL
VLM-NATIVE ANNOTATION
Phrase grounding links natural language to bounding boxes. VQA adds question-answer pairs. IntelliScribe auto-generates captions and highlights matching phrases, 3-5x faster than manual labeling.
INTELLISCRIBE: 3-5X ANNOTATION SPEED
02 // TRAIN
MANAGED FINE-TUNING
Pick a base model, set LoRA or full SFT, choose your GPU tier, and launch. Live loss curves, checkpoint traversal, and visual prediction previews. Close your browser. Vi trains in the background.
LORA: 3-5X LESS MEMORY, 2-3X FASTER
03 // SHIP
SDK & NIM DEPLOYMENT
Download models with the Vi SDK for local inference with 4-bit quantization. Or deploy via NVIDIA NIM containers with OpenAI-compatible API endpoints, guided JSON decoding, and video processing.
pip install vi-sdk[all]
HOW IT WORKS
THE FULL VLM FINE-TUNING LIFECYCLE.
01 // ANNOTATE
VLM-NATIVE ANNOTATION
Label images and video with five annotation modes built for vision-language models. IntelliScribe accelerates labeling 3-5x with AI-generated captions and phrase highlighting.
- Phrase Grounding: link natural language to bounding boxes
- Visual Q&A: question-answer pairs per image
- Freetext: open-ended descriptive captions and reports
- Chain-of-Thought: step-by-step reasoning labels
- VLA: vision-language-action labels for robotics
“A red valve handle on the pressure gauge near the mounting bracket”
02 // TRAIN
MANAGED COMPUTE
LoRA, QLoRA, or Full SFT across T4 to B200 GPUs. Up to 16 GPUs per run. NF4 quantization for 4x memory savings.
Qwen2.5-VL-7B · LoRA · A100-80GB × 2
EPOCH 47/100 · LOSS: 0.42
03 // EVALUATE
VISUAL DIFF
Side-by-side ground truth vs. predictions. Scrub through checkpoints. F1, IoU, Precision, Recall for grounding. BLEU, BERTScore for VQA.
04 // DEPLOY
SHIP MODELS YOUR WAY
Vi SDK for local inference with quantization, runs on a laptop GPU. NVIDIA NIM for containerized serving with OpenAI-compatible endpoints. Chain-of-Thought for 15-30% accuracy improvement on complex tasks.
PERFORMANCE OPTIMIZATION
THE FINE-TUNING
ADVANTAGE.
Vague · Low confidence · No grounding
Precise · Grounded · Production-ready
Accuracy Gap
Base models are trained to be generalists, understanding broad visual categories but failing on specialized industrial or medical contexts. Vi fine-tuning transforms these models into domain experts, delivering 15-30% higher accuracy on specific production tasks.
Token Efficiency
Fine-tuned models internalize complex instructions. By removing the need for extensive few-shot prompting, you reduce token consumption significantly. Fine-tuning produces structured, reliable outputs without the jitter of base inference, directly lowering latency and operational costs.
Edge Readiness
Size is not performance. A specialized, fine-tuned 2B parameter model often outperforms a massive 32B base model on specific visual inspection tasks. This "shrink-to-fit" approach allows high-performance VLM deployment on edge hardware and air-gapped systems.
USE CASES
VLM USE CASES ACROSS INDUSTRIES.
VLMs replace rigid classifiers with natural language. Describe what to find, ask questions about images, and get grounded answers, across any domain.
QUALITY INSPECTION
Prompt
"Locate any cracked solder joints on the upper IC package"
Vi Response
SUPPORTED VLM ARCHITECTURES
Fine-tune and deploy the leading vision-language architectures.
ALIBABA
Qwen2.5-VL
Dynamic resolution for images and video. Processes videos over 1 hour. Recommended default for most tasks.
ALIBABA
Qwen3-VL
Interleaved multimodal context with thinking mode for chain-of-thought reasoning. Extensible to 1M tokens.
OPENGVLAB
InternVL3.5
Visual Resolution Router for adaptive token compression. Flash variants with up to 50% fewer visual tokens.
NVIDIA
Cosmos-Reason2
Physical-world reasoning: understands space, time, and physics for robotics and embodied AI systems.
MOONSHOT AI
Kimi K2.5
Long-context multimodal reasoning with agent swarm orchestration. 1T total, 32B active MoE.
META
Llama 4
Natively multimodal with early fusion architecture. Scout variant: 10M context window.
Bring Your Own Models
Import custom LoRA adapters, fine-tuned checkpoints, or full model weights directly into Vi. New architectures added every month.
DESIGNEDBYRESEARCHERS,BUILTFORINDUSTRY.
The Vi SDK gives you programmatic control over every step: dataset management, concurrent asset uploads, annotation CRUD, training runs, model download, and local inference. Type-safe, with structured error handling.
pip install vi-sdk[all]import vi
client = vi.Client(
secret_key="sk-...",
organization_id="org-..."
)
# Load fine-tuned model
model = vi.model.load(
run_id="run_abc123",
load_in_4bit=True
)
# Run inference
result, error = model.predict(
image="./inspection.jpg",
prompt="Locate any cracked joints."
)
print(result.result.sentence)
print(result.result.groundings)THE COMPLETE VLMOPS WORKFLOW
FROM PIXEL TO PRODUCTION.
01 // ANNOTATE
DESCRIBE WHAT YOU SEE.
Upload images or video frames, then describe objects in natural language. Vi returns bounding boxes, structured reasoning, and chain-of-thought explanations for each annotation.
faster with IntelliScribe
START FREE. SCALE WITH YOUR MODELS.
All plans include annotation tools, all model architectures, and SDK access.
FREE
For Individual Exploration
- ✓3,000 Data Rows
- ✓300 Compute Credits / Month
- ✓Solo Use Only
- ✓All Model Architectures
- ✓IntelliScribe AI
- ✓Vi SDK Access
DEVELOPER
For Developers and Researchers
- ✓10,000 Data Rows
- ✓Pay-Per-Use GPU Compute
- ✓Up To 10 Collaborators
- ✓Priority GPU Queues
- ✓All Model Architectures
- ✓Everything In Free
PROFESSIONAL
For Teams Scaling VLM Workflows
- ✓50,000 Data Rows
- ✓5,000 Credits / Month
- ✓50 Collaborators
- ✓Model-Assisted Labeling
- ✓Deployment Containers
- ✓Dedicated Expert Support
- ✓Everything In Developer
ENTERPRISE
For Regulated and Private Environments
- ✓Custom Data Rows (1M+)
- ✓Custom Credits (50K+/Mo)
- ✓Unlimited Collaborators
- ✓Custom Model Imports
- ✓VPC & On-Premise Deployment
- ✓Dedicated Success Manager
FAQ
VLM FINE-TUNING
FAQ.
Everything you need to know about Datature Vi, VLM fine-tuning, and deployment.
YOUR VLM PIPELINE
STARTS HERE.
3,000 Data Rows and 300 Compute Credits free every month.
No credit card required.