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trillionlabs/NemoSlides

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face

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Model Card

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.

Model Summary

Property	Value
Base Model	NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
Total Parameters	30B
Active Parameters	3B
Architecture	Hybrid (Attention + SSM + MoE)
Precision	bf16
License	Apache 2.0

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.

QuickStart

Installation

bash

pip install transformers torch

Using Transformers

python

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.

bash

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

Rendering Slides

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

License

This model is released under the Apache 2.0 License.

Acknowledgement

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