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

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