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MMODMOD.MMOD-08 · v1.0

Reason across
pixels, audio,
and text in one model.

8 micro-lessons · ~84 min · Real Docker images

THE FUSION RACK · LIVE
FX 03IN · 1OUT
FUSING
VISION
AUDIO
TEXT
FUSION
OUT
ACTIVE: VISION · cross-attn ON↗ unified embedding
MMODAI ENGINEERINGTRENDING

Multimodal AI

Vision, audio, video, voice. Ship multimodal AI features that hold up in production.

WHY THIS MATTERS · DEEPLEARNING.AI · HUGGING FACE VLM 2025 · ARXIV 2502.14786 (SIGLIP 2) · ARXIV 2407.01449 (COLPALI)
The 2026 frontier is native multimodal — Gemini 2.5 Pro reads hour-long video at 84.8 on Video-MME, Qwen3-VL-235B leads open VLMs at 80.6 MMMU, and ColPali / late-interaction has redefined PDF retrieval. This course is built around what's actually shipping in production today: OCR-free document AI, ColPali RAG, sub-300ms voice loops, long-video Q&A, SigLIP 2 image search, and a fully air-gapped local stack.
WHAT YOU'LL LEARN
01The VLM mental model
02Picking your multimodal model
03OCR-free document AI
04Multimodal RAG with ColPali
05Voice agents — the sub-300ms loop
06Long-video understanding
07Cross-modal embeddings & search
08Eval, hallucination & air-gapped local
YOU'LL BE ABLE TO
Replace OCR + regex pipelines with a one-call OCR-free VLM extractor
Ship a ColPali multimodal RAG that beats text RAG on diagrammed PDFs
Engineer a sub-300ms voice agent loop with Deepgram + GPT-5 + ElevenLabs
Run a 5-benchmark scorecard in CI to gate every model swap
Deploy an air-gapped local stack regulated industries actually buy
SKILLS YOU'LL GAIN

Real skills, real career delta.

Skills you'll gain

10
  • Read VLM model cards criticallyWorking

    Identify vision tower / projector / decoder; modality coverage; native vs adapter-stitched; context window and resolution limits — before opening the API.

  • Pick a multimodal model from a 4-axis matrixProduction

    Decide along modalities x context length x open/closed x cost. Match GPT-5 / Gemini 2.5 Pro / Claude Opus 4.7 / Qwen3-VL / Llama 4 Scout / Pixtral to the job.

  • Build OCR-free document extraction → Pydantic JSONProduction

    Send base64 page images to GPT-5/Pixtral/Qwen2.5-VL with response_format=PydanticModel. Validate, retry, reject. Drop into AP automation flows.

  • Ship multimodal RAG with ColPali / late interactionProduction

    ColQwen2 + Qdrant multi-vector index over PDF page images. Skip OCR and chunking entirely. Measured: ~1s search on 25K pages.

  • Engineer a sub-300ms voice loopProduction

    Deepgram Nova-3 STT (~150ms first word) + GPT-5 streaming + ElevenLabs Flash v2.5 (~75ms TTFB), with VAD-driven barge-in. Or OpenAI Realtime API for the unified path.

  • Process long video with native 1M-token VLMsWorking

    Gemini 2.5 Pro for hour-long video Q&A (Video-MME 84.8). Frame-sampling fallback with decord/PyAV when content exceeds context.

  • Build cross-modal search at scale (SigLIP 2 + Qdrant)Production

    SigLIP 2 NaFlex embeddings (109 langs) → Qdrant HNSW. Billion-scale-ready. Replaces CLIP for any new build.

  • Run a 5-benchmark VLM evaluation in CIProduction

    MMMU + MathVista + Video-MME + DocVQA + RePOPE on every model swap. lmms-eval orchestrates; HTML report goes to Notion.

  • Detect & defend against multimodal hallucinationAdvanced

    POPE/RePOPE/HALLUSIONBench probes; grounded-prompting + cite-the-region prompts; refuse-when-unsure system prompts; eval gates in CI.

  • Deploy a fully air-gapped local multimodal stackAdvanced

    Ollama + Qwen2.5-VL-7B + whisper-large-v3-turbo + SigLIP 2 — on-prem, GPU-budgeted. The deployment regulated industries actually buy.

RUNNABLE ON YOUR MACHINE
$ docker pull snap/multimodal:hello
$ docker run --rm -it snap/multimodal:hello
snap/multimodal:hello
QUICK PREVIEW · 7 MIN
VERIFIED ENGINEER REVIEWS
Cross-modal retrieval explained without 100 papers.
@vlm_miraVERIFY ON GITHUB
Finally a multimodal track for shipping engineers.
@sre_mayaVERIFY ON GITHUB
LESSONS8
HOURS~1.4
LEARNERS1,340
THIS WEEK+22%