NVIDIA-NemotronLabs-AI-for-Media-Sports-Tennis

NVIDIA-NemotronLabs-AI-for-Media-Sports-Tennis

At a Glance

Description:

NVIDIA-NemotronLabs-AI-for-Media-Sports-Tennis is a fine-tuned multimodal large language model derived from Nemotron 3 Nano Omni. It was further trained on an in-house NVIDIA tennis dataset of manually labeled point-level video clips to specialize in tennis match understanding. The model supports structured multiple-choice and open-ended Q&A over point clips, covering shot mechanics, court positioning, player movement, match facts, rules knowledge, and audio cues. It works best when a full tennis point clip is passed as input.

This model is fine-tuned from Nemotron 3 Nano Omni on an in-house NVIDIA tennis dataset of tennis point video clips using the NVIDIA Sports Intelligence playbooks. For base model details, see the base model card.

This model is ready for commercial or non-commercial use.

License/Terms of Use

Governing Terms: Use of this model is governed by the OpenMDW License Agreement v1.1 (OpenMDW-1.1)

Deployment Geography:

Global

Use Case:

This model is designed for tennis-specific multimodal understanding over point-level video. Expected users include:

• Tennis broadcast and media workflows — point-level Q&A, shot and outcome understanding, and audio cue interpretation over match footage
• Coaching and performance analysis — structured and open-ended questions about shots, positioning, movement, and rally structure on point clips
• Sports analytics and production — match-fact and score-state reasoning aligned with point-level annotations
• Enterprise tennis applications — searchable point-level understanding and tennis-domain assistants over archived match video

Release Date:

Hugging Face 09/10/2026 via Hugging Face

Model Architecture:

Architecture Type: Other (Mamba2-Transformer hybrid Mixture of Experts backbone with multimodal encoders)

Network Architecture:

• Nemotron 3 Nano LLM (30B A3B) — 31B-parameter Mamba2-Transformer hybrid MoE backbone with ~3B active parameters per token.
• CRADIO v4-H — vision encoder for image and video frames.
• Parakeet — speech encoder for audio inputs.

Number of model parameters: 3.1 x 10^10 (31B A3B)

How the model was developed: Weights are initialized from the pretrained Nemotron 3 Nano Omni BF16 checkpoint. Fine-tuning trains only the language-model parameters; the vision and audio encoders remain frozen. Optimization uses AdamW (learning rate 5×10⁻⁵, β=(0.9, 0.999)) with warmup and cosine decay, regularized by weight decay 0.05 and gradient clipping at max-norm 1.0, with no additional damping. Training is BF16 packed supervised fine-tuning over point-level tennis clips. Hyperparameters follow the published NeMo AutoModel recipe rather than a dedicated search.

Input(s):

Input Type(s): Video, Audio, Image, Text

Input Format(s):

• Video: mp4, up to 2 minutes. For tennis tasks, pass a full tennis point clip as input for best results. For 1080p videos, sample up to 1 FPS (frames per second) / 128 frames. For lower-resolution videos such as 720p, higher temporal sampling such as 2 FPS / 256 frames may be used.
• Audio: wav, mp3 files (up to 1 hour), 8kHz and higher sampling rates
• Image: Red, Green, Blue (RGB) (jpeg, png)
• Text: String

Input Parameters:

• Video: Three-Dimensional (3D)
• Audio: One-Dimensional (1D)
• Image: Two-Dimensional (2D)
• Text: One-Dimensional (1D)

Other Properties Related to Input:

• Recommended input: a full tennis point (video clip, with audio when available), matching the point-level clips used during fine-tuning
• Maximum context length up to 256k tokens
• Language support: English only

Output(s)

Output Type(s): Text

Output Format(s):

• Text: String

Output Parameters:

• Text: One-Dimensional (1D)

Other Properties Related to Output:

• Maximum context length up to 256k tokens.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to central processing unit (CPU)-only solutions.

Software Integration:

Runtime Engine(s):

• NeMo
• Megatron

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Ampere (A100 80GB SXM/NVLink)
• NVIDIA Blackwell (B200 SXM/NVLink, RTX Pro 6000 SE, DGX Spark, Jetson Thor, RTX 5090)
• NVIDIA Hopper (H100 SXM/NVLink, H200 SXM/NVLink)
• NVIDIA Lovelace (L40S)

Preferred/Supported Operating System(s):

• Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functio