NemoSlides, a Nemotron Specialized in Slide Generation

NemoSlides is a post-trained hybrid architecture language model built on NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 by NVIDIA Corporation. It underwent supervised fine-tuning (SFT) using Nemo Automodel.

NemoSlides is purpose-built to generate high-quality, aesthetic slides from a single instruction.

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Model Summary

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Evaluation Results

To evaluate the outcome we use Gemini 3 Flash as a VLM judge. Our final model achieves a +48% improvement over the Nano baseline.

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QuickStart

Installation

pip install transformers torch

Using Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "trillionlabs/NemoSlides"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Create a 9-slide Slidev deck for Apex Materials Group's board of directors reviewing FY24 capital allocation and dividend policy."},
]

input_ids = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

output = model.generate(input_ids, max_new_tokens=4096, do_sample=True, temperature=0.7)
print(tokenizer.decode(output[0][input_ids.shape[-1]:], skip_special_tokens=True))

Deployment

We recommend deploying the model with the lastest version of vLLM.

wget https://huggingface.co/trillionlabs/NemoSlides/blob/main/nano_v3_reasoning_parser.py

vllm serve trillionlabs/NemoSlides \
  --tensor-parallel-size 1 \
  --port 8000 \
  --trust-remote-code \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --reasoning-parser-plugin nano_v3_reasoning_parser.py \
  --reasoning-parser nano_v3

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Rendering Slides

We use Slidev to generate slides. Please check the official repo to render the output into slide.

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License

This model is released under the Apache 2.0 License.

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Acknowledgement

This project is conducted as part of NVIDIA Nemotron Developer Days Seoul 2026 Hackathon. We thank NVIDIA for the oppurtunity and support.