Results for “model-porting”

12 skills
nvidia
deepstream-import-vision-model
Import object detection models from HuggingFace or NVIDIA NGC into a DeepStream pipeline with automated ONNX download, TensorRT engine build, custom parser, multi-stream benchmark, and PDF report generation.
2.2k · bundle
nvidia
tao-port-huggingface-model
Integrate a HuggingFace computer vision model into the NVIDIA TAO Toolkit ecosystem, covering the full pipeline from prerequisites to container testing.
2.2k · bundle
google
agent-platform-model-registry
Manage machine learning models in the Agent Platform Model Registry: list, describe, upload, update, and delete models and their versions.
14.4k
nvidia
nemo-automodel-model-onboarding
Guides implementation of new model architectures in NeMo AutoModel through five phases: discovery, implementation, registration, validation, and testing.
2.2k · bundle
nvidia
tao-train-mask-auto-encoder
Train, evaluate, export, and run inference for Masked Auto-Encoder (MAE) models for self-supervised pretraining and fine-tuning of visual representations.
2.2k · bundle
orchestra-research
moe-training
Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace, covering architectures, routing, load balancing, and expert parallelism.
10.4k · bundle
mukul975
detecting-model-extraction-attacks
Detect model stealing, model inversion, and membership inference performed through inference-API abuse by monitoring query patterns, applying output perturbation, and red-teaming your own model's extractability.
24.6k · bundle
affaan-m
mle-workflow
Turn model work into a production ML system with data contracts, repeatable training, measurable quality gates, deployable artifacts, and operational monitoring.
226k
google-gemma
gemma-dev
Selects the right Gemma model for a task, recommends deployment tooling (Gradio, Transformers.js, Vertex AI, MLX), and applies optimizations like MTP and QAT.
· bundle
orchestra-research
model-merging
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining, covering SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.
10.4k · bundle
orchestra-research
model-pruning
Compress large language models by 40-60% with minimal accuracy loss using one-shot pruning techniques like Wanda and SparseGPT, enabling faster inference and deployment on constrained hardware.
10.4k · bundle
sakamoto-family-smile
mle-workflow
Turn model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.
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