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NemoSlides

Open-weight slide generation, fine-tuned on Nemotron.

NemoSlides fine-tunes NVIDIA-Nemotron-3-Nano-30B-A3B on a 705-sample synthetic corpus of Slidev decks. The resulting 30B-parameter MoE (3B active) generates presentation-grade decks from a single prompt and ranks #1 on the 30-row SlidevBench test split — ahead of gpt-5.4, glm-5.1, and the 120B-A12B nemotron-super.

#1SlidevBench rank
3.69Overall (floor)
3.99Overall (renderable)
+48%Δ vs. base nano
93%Render rate
30B / 3BTotal / active

Architecture

NemoSlides is a supervised fine-tune on top of NVIDIA's post-trained Nemotron-3-Nano-30B-A3B. Training runs through NeMo-RL's run_sft.py with LoRA + FSDP2 on a published 2n8g recipe. Evaluation reuses the same vLLM inference path as the base model, isolating the adapter as the only variable.

flowchart LR
    P["user prompt"] --> M["NemoSlides<br/>Nemotron-3-Nano-30B-A3B<br/>+ LoRA adapter"]
    M --> D["Slidev markdown<br/>+ &lt;think&gt; trace"]
    D --> R["Slidev + Playwright<br/>render"]
    R --> SL["rendered deck"]

    subgraph train["Training (offline)"]
        S["seeds<br/>data/seeds.json"] --> C["Codex CLI<br/>per-seed author"]
        C --> J["chat JSONL<br/>trillionlabs/slides-sft-v0"]
        J --> NR["NeMo-RL run_sft.py<br/>LoRA · FSDP2"]
        NR --> AD["LoRA adapter"]
    end
    AD -.loaded at inference.-> M

A hand-drawn Excalidraw replacement for this diagram lives at docs/assets/diagrams/architecture.svg once rendered; see docs/assets/diagrams/README.md for the export workflow.

Headline results

SlidevBench — 30 held-out prompts. Judge: google/gemini-3-flash-preview (vision). Rubric: Content / Design / Coherence (judge) + Visual Craft (objective Slidev-feature scan). Weighted Overall = 0.40·VisCraft + 0.25·Design + 0.20·Content + 0.15·Coherence.

Model Render Content Design Coherence VisCraft Overall
nemoslides-30b-a3b (ours) 93% 4.03 3.53 4.00 3.50 3.69
gpt-5.4 100% 4.27 3.17 4.07 3.40 3.62
glm-5.1 100% 3.83 3.03 3.83 2.90 3.26
nemotron-super (120B-A12B) 100% 4.13 2.63 3.73 1.97 2.83
nemotron-nano (30B-A3B, base) 87% 3.50 2.30 3.37 1.80 2.50

Weighted Overall across all 5 models

Full results, per-dimension tables, and Δ analysis live in 05 · Results.

What NemoSlides generates

Rendered slides from the finetuned model on held-out test prompts — layouts span cover, two-cols, image-right, fact, center, end, shiki code blocks, Mermaid diagrams, and v-click progressive reveals.

See the full side-by-side comparison of all 5 models on all 30 SlidevBench prompts in the SlidevBench gallery →

Documentation

This site covers the technical implementation in five parts.

Section Contents
01 · Architecture System components, module layout, inference + training stacks, request/response flow
02 · Data Synthesis pipeline (Data Designer + Codex), dataset schema, validators, feature coverage
03 · Training Base model, NeMo-RL SFT recipe, LoRA + FSDP2 config, chat-JSONL format, inference-time setup
04 · Evaluation SlidevBench definition, two-fold protocol (VLM judge + human blindtest), rubric anchors, feature scanner
05 · Results Headline numbers, per-dimension breakdown, SFT Δ, before/after, blindtest status
Reproduce Full repro playbook — baseline eval, synthesis, training, finetuned eval

References