Packs
1 packResults for “model-integration”
19 skills
affaan-m ml-adoption-playbook
Provides an adaptive methodology for adding machine learning models to existing codebases, covering problem framing, data readiness, architectural decoupling, and baseline model integration.
226k
drnabeelkhan ai-engineer
Implements machine learning models, embeddings, and AI-powered features with ethical considerations, including model selection, integration, and monitoring.
2
majiayu000 ml
Guides machine learning development with experiment tracking, hyperparameter optimization, model registry, and MLOps pipeline integration.
567 · 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
More results
huggingface huggingface-best
Queries Hugging Face benchmark leaderboards to find the best AI models for a task, filters by device constraints, and returns a ranked comparison table with scores.
10.8k
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
nvidia tao-train-reid
Trains, evaluates, exports, and runs inference for person re-identification models using TAO, learning discriminative embeddings for cross-camera matching.
2.2k · bundle
mukul975 detecting-data-and-model-poisoning
Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.
24.6k · bundle
mhassan0000 mle-workflow
Turns model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.
1
sakamoto-family-smile mle-workflow
Turn model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.
0
leandrobenjaminl ml-modeling
Entrena modelos de machine learning con Scikit-learn, LightGBM y XGBoost, desde un baseline hasta un modelo productivo con validación robusta y explicabilidad.
0 · 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
gabrielmoreira gi-enhancer
Predicts enhancer activity in DNA sequences using the hosted Genomic Intelligence G0 DeepSTARR model, returning per-window activity scores.
17 · bundle
paramchordiya ml-engineering
Enforces rigorous ML modeling, feature engineering, training, and evaluation standards at principal-engineer level.
0
jeffallan ml-pipeline
Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking, creates orchestration DAGs, builds feature store schemas, deploys model registries, and automates retraining and validation workflows.
10.4k · bundle
k-dense-ai scvi-tools
Provides deep generative models for single-cell omics analysis, including probabilistic batch correction, transfer learning, differential expression, and multi-modal integration.
30.2k · bundle
qhjqhj00 pennylane
Train quantum circuits with automatic differentiation and build hybrid quantum-classical models using PennyLane, including VQE, QAOA, and integration with PyTorch, JAX, and TensorFlow.
3 · bundle
majiayu000 hqq-quantization
Quantize large language models to 8/4/3/2/1-bit precision without calibration data, using multiple optimized backends and integrations with HuggingFace Transformers, vLLM, and PEFT/LoRA.
567 · bundle
gabrielmoreira gi-expression
Predicts tissue or cell-type gene expression (log TPM and TPM) from a TSS-centered DNA sequence using the hosted Genomic Intelligence G0 Expression model, conditioned on a free-text cell-type description.
17 · bundle