75 Best Machine Learning Algorithms with Hugging Face Framework
Categories- Pros ✅Massive Memory Savings & Faster TrainingCons ❌Implementation Complexity & Hardware SpecificAlgorithm Type 📊Neural NetworksPrimary Use Case 🎯Natural Language ProcessingComputational Complexity ⚡MediumImplementation Frameworks 🛠️PyTorch & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Memory OptimizationPurpose 🎯Natural Language Processing
- Pros ✅Reduces Memory Usage, Fast Fine-Tuning and Maintains PerformanceCons ❌Limited To Specific Architectures & Requires Careful Rank SelectionAlgorithm Type 📊Supervised LearningPrimary Use Case 🎯Natural Language ProcessingComputational Complexity ⚡MediumImplementation Frameworks 🛠️PyTorch & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Low-Rank DecompositionPurpose 🎯Natural Language Processing
- Pros ✅Linear Complexity & Long-Range ModelingCons ❌Limited Adoption & Complex TheoryAlgorithm Type 📊Neural NetworksPrimary Use Case 🎯Sequence ModelingComputational Complexity ⚡MediumImplementation Frameworks 🛠️PyTorch & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Linear Scaling With Sequence LengthPurpose 🎯Sequence Modeling
- Pros ✅Extreme Memory Reduction, Maintains Quality and Enables Consumer GPU TrainingCons ❌Complex Implementation & Quantization ArtifactsAlgorithm Type 📊Supervised LearningPrimary Use Case 🎯Natural Language ProcessingComputational Complexity ⚡MediumImplementation Frameworks 🛠️PyTorch & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡4-Bit QuantizationPurpose 🎯Natural Language Processing
- Pros ✅High Accuracy, Domain Specific and Scientific ImpactCons ❌Computationally Expensive & Specialized UseAlgorithm Type 📊Self-Supervised LearningPrimary Use Case 🎯Drug DiscoveryComputational Complexity ⚡HighImplementation Frameworks 🛠️PyTorch & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Protein EmbeddingsPurpose 🎯Classification
- Pros ✅Excellent Multimodal & Fast InferenceCons ❌High Computational Cost & Complex DeploymentAlgorithm Type 📊Supervised LearningPrimary Use Case 🎯Computer VisionComputational Complexity ⚡Very HighImplementation Frameworks 🛠️TensorFlow & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Code GenerationPurpose 🎯Computer Vision
- Pros ✅Unified Processing & Rich UnderstandingCons ❌Massive Compute Needs & Complex TrainingAlgorithm Type 📊Supervised LearningPrimary Use Case 🎯Computer VisionComputational Complexity ⚡Very HighImplementation Frameworks 🛠️Hugging Face & PyTorchAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Multi-Modal FusionPurpose 🎯Computer Vision
- Pros ✅High Efficiency & Low Memory UsageCons ❌Complex Implementation & Limited InterpretabilityAlgorithm Type 📊Supervised LearningPrimary Use Case 🎯Natural Language ProcessingComputational Complexity ⚡HighImplementation Frameworks 🛠️PyTorch & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Selective State SpacesPurpose 🎯Natural Language Processing
- Pros ✅Fast Inference & Memory EfficientCons ❌Less Interpretable & Limited BenchmarksAlgorithm Type 📊Neural NetworksPrimary Use Case 🎯Natural Language ProcessingComputational Complexity ⚡MediumImplementation Frameworks 🛠️PyTorch & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Convolutional AttentionPurpose 🎯Natural Language Processing
- Pros ✅Exceptional Quality & Stable TrainingCons ❌Slow Generation & High ComputeAlgorithm Type 📊Unsupervised LearningPrimary Use Case 🎯Computer VisionComputational Complexity ⚡HighImplementation Frameworks 🛠️PyTorch & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Denoising ProcessPurpose 🎯Computer Vision
- Pros ✅High Efficiency & Long ContextCons ❌Complex Implementation & New ParadigmAlgorithm Type 📊Supervised LearningPrimary Use Case 🎯Natural Language ProcessingComputational Complexity ⚡HighImplementation Frameworks 🛠️PyTorch & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Selective State SpacesPurpose 🎯Natural Language Processing
- Pros ✅Better Efficiency Than Transformers & Linear ComplexityCons ❌Limited Adoption & New ArchitectureAlgorithm Type 📊Neural NetworksPrimary Use Case 🎯Natural Language ProcessingComputational Complexity ⚡MediumImplementation Frameworks 🛠️PyTorch & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Retention MechanismPurpose 🎯Natural Language Processing
- Pros ✅High Quality Code, Multi-Language and Context AwareCons ❌Security Concerns & Bias IssuesAlgorithm Type 📊Self-Supervised LearningPrimary Use Case 🎯Natural Language ProcessingComputational Complexity ⚡HighImplementation Frameworks 🛠️Hugging Face & OpenAI APIAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Code UnderstandingPurpose 🎯Natural Language Processing
- Pros ✅Up-To-Date Information & Reduced HallucinationsCons ❌Complex Architecture & Higher LatencyAlgorithm Type 📊Neural NetworksPrimary Use Case 🎯Natural Language ProcessingComputational Complexity ⚡HighImplementation Frameworks 🛠️Hugging Face & PyTorchAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Dynamic Knowledge AccessPurpose 🎯Natural Language Processing
- Pros ✅Linear Complexity & Memory EfficientCons ❌Limited Adoption & New ArchitectureAlgorithm Type 📊Supervised LearningPrimary Use Case 🎯Natural Language ProcessingComputational Complexity ⚡MediumImplementation Frameworks 🛠️PyTorch & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Selective State SpacesPurpose 🎯Natural Language Processing
- Pros ✅Real-Time Processing & Multi-Language SupportCons ❌Audio Quality Dependent & Accent LimitationsAlgorithm Type 📊Supervised LearningPrimary Use Case 🎯Natural Language ProcessingComputational Complexity ⚡MediumImplementation Frameworks 🛠️PyTorch & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Real-Time SpeechPurpose 🎯Natural Language Processing
- Pros ✅Minimal Parameter Updates, Fast Adaptation and Cost EffectiveCons ❌Limited Flexibility, Domain Dependent and Requires Careful Prompt DesignAlgorithm Type 📊Neural NetworksPrimary Use Case 🎯Natural Language ProcessingComputational Complexity ⚡LowImplementation Frameworks 🛠️Hugging Face, PyTorch and OpenAI APIAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Parameter-Efficient AdaptationPurpose 🎯Natural Language Processing
- Pros ✅No Catastrophic Forgetting & Continuous AdaptationCons ❌Training Complexity & Memory RequirementsAlgorithm Type 📊Neural NetworksPrimary Use Case 🎯Continual LearningComputational Complexity ⚡HighImplementation Frameworks 🛠️PyTorch & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Catastrophic Forgetting PreventionPurpose 🎯Continual Learning
- Pros ✅Creative Control & Quality OutputCons ❌Resource Intensive & Limited DurationAlgorithm Type 📊Supervised LearningPrimary Use Case 🎯Computer VisionComputational Complexity ⚡Very HighImplementation Frameworks 🛠️PyTorch & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Motion SynthesisPurpose 🎯Computer Vision
- Pros ✅Better Long Context & Easy ImplementationCons ❌Limited Improvements & Context DependentAlgorithm Type 📊Neural NetworksPrimary Use Case 🎯Natural Language ProcessingComputational Complexity ⚡LowImplementation Frameworks 🛠️PyTorch & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Position EncodingPurpose 🎯Natural Language Processing
- Pros ✅High Alignment & User FriendlyCons ❌Requires Human Feedback & Training ComplexityAlgorithm Type 📊Supervised LearningPrimary Use Case 🎯Natural Language ProcessingComputational Complexity ⚡MediumImplementation Frameworks 🛠️OpenAI API & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Human Feedback TrainingPurpose 🎯Natural Language Processing
- Pros ✅Zero-Shot Capability & High AccuracyCons ❌Large Model Size & Computational IntensiveAlgorithm Type 📊Neural NetworksPrimary Use Case 🎯Computer VisionComputational Complexity ⚡HighImplementation Frameworks 🛠️PyTorch & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Universal SegmentationPurpose 🎯Computer Vision
- Pros ✅Follows Complex Instructions, Multimodal Reasoning and Strong GeneralizationCons ❌Requires Large Datasets & High Inference CostAlgorithm Type 📊Supervised LearningPrimary Use Case 🎯Computer VisionComputational Complexity ⚡HighImplementation Frameworks 🛠️PyTorch & Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Instruction TuningPurpose 🎯Computer Vision
- Pros ✅Superior Context Understanding, Improved Interpretability and Better Long-Document ProcessingCons ❌High Computational Cost, Complex Implementation and Memory IntensiveAlgorithm Type 📊Neural NetworksPrimary Use Case 🎯Natural Language ProcessingComputational Complexity ⚡HighImplementation Frameworks 🛠️PyTorch, TensorFlow and Hugging FaceAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Multi-Level Attention MechanismPurpose 🎯Natural Language Processing
- Pros ✅Medical Expertise & Clinical AccuracyCons ❌Limited Domains & Regulatory ChallengesAlgorithm Type 📊Supervised LearningPrimary Use Case 🎯Natural Language ProcessingComputational Complexity ⚡HighImplementation Frameworks 🛠️Hugging Face & TensorFlowAlgorithm Family 🏗️Neural NetworksKey Innovation 💡Medical SpecializationPurpose 🎯Natural Language Processing
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Facts about Best Machine Learning Algorithms with Hugging Face Framework
- FlashAttention 2
- FlashAttention 2 uses Neural Networks learning approach
- The primary use case of FlashAttention 2 is Natural Language Processing
- The computational complexity of FlashAttention 2 is Medium.
- The implementation frameworks for FlashAttention 2 are PyTorch,Hugging Face..
- FlashAttention 2 belongs to the Neural Networks family.
- The key innovation of FlashAttention 2 is Memory Optimization.
- FlashAttention 2 is used for Natural Language Processing
- LoRA (Low-Rank Adaptation)
- LoRA (Low-Rank Adaptation) uses Supervised Learning learning approach
- The primary use case of LoRA (Low-Rank Adaptation) is Natural Language Processing
- The computational complexity of LoRA (Low-Rank Adaptation) is Medium.
- The implementation frameworks for LoRA (Low-Rank Adaptation) are PyTorch,Hugging Face..
- LoRA (Low-Rank Adaptation) belongs to the Neural Networks family.
- The key innovation of LoRA (Low-Rank Adaptation) is Low-Rank Decomposition.
- LoRA (Low-Rank Adaptation) is used for Natural Language Processing
- State Space Models V3
- State Space Models V3 uses Neural Networks learning approach
- The primary use case of State Space Models V3 is Sequence Modeling
- The computational complexity of State Space Models V3 is Medium.
- The implementation frameworks for State Space Models V3 are PyTorch,Hugging Face..
- State Space Models V3 belongs to the Neural Networks family.
- The key innovation of State Space Models V3 is Linear Scaling With Sequence Length.
- State Space Models V3 is used for Sequence Modeling
- QLoRA (Quantized LoRA)
- QLoRA (Quantized LoRA) uses Supervised Learning learning approach
- The primary use case of QLoRA (Quantized LoRA) is Natural Language Processing
- The computational complexity of QLoRA (Quantized LoRA) is Medium.
- The implementation frameworks for QLoRA (Quantized LoRA) are PyTorch,Hugging Face..
- QLoRA (Quantized LoRA) belongs to the Neural Networks family.
- The key innovation of QLoRA (Quantized LoRA) is 4-Bit Quantization.
- QLoRA (Quantized LoRA) is used for Natural Language Processing
- ProteinFormer
- ProteinFormer uses Self-Supervised Learning learning approach
- The primary use case of ProteinFormer is Drug Discovery
- The computational complexity of ProteinFormer is High.
- The implementation frameworks for ProteinFormer are PyTorch,Hugging Face..
- ProteinFormer belongs to the Neural Networks family.
- The key innovation of ProteinFormer is Protein Embeddings.
- ProteinFormer is used for Classification
- Gemini Pro 2.0
- Gemini Pro 2.0 uses Supervised Learning learning approach
- The primary use case of Gemini Pro 2.0 is Computer Vision
- The computational complexity of Gemini Pro 2.0 is Very High.
- The implementation frameworks for Gemini Pro 2.0 are TensorFlow,Hugging Face..
- Gemini Pro 2.0 belongs to the Neural Networks family.
- The key innovation of Gemini Pro 2.0 is Code Generation.
- Gemini Pro 2.0 is used for Computer Vision
- FusionFormer
- FusionFormer uses Supervised Learning learning approach
- The primary use case of FusionFormer is Computer Vision
- The computational complexity of FusionFormer is Very High.
- The implementation frameworks for FusionFormer are Hugging Face,PyTorch..
- FusionFormer belongs to the Neural Networks family.
- The key innovation of FusionFormer is Multi-Modal Fusion.
- FusionFormer is used for Computer Vision
- MambaFormer
- MambaFormer uses Supervised Learning learning approach
- The primary use case of MambaFormer is Natural Language Processing
- The computational complexity of MambaFormer is High.
- The implementation frameworks for MambaFormer are PyTorch,Hugging Face..
- MambaFormer belongs to the Neural Networks family.
- The key innovation of MambaFormer is Selective State Spaces.
- MambaFormer is used for Natural Language Processing
- Hyena
- Hyena uses Neural Networks learning approach
- The primary use case of Hyena is Natural Language Processing
- The computational complexity of Hyena is Medium.
- The implementation frameworks for Hyena are PyTorch,Hugging Face..
- Hyena belongs to the Neural Networks family.
- The key innovation of Hyena is Convolutional Attention.
- Hyena is used for Natural Language Processing
- Diffusion Models
- Diffusion Models uses Unsupervised Learning learning approach
- The primary use case of Diffusion Models is Computer Vision
- The computational complexity of Diffusion Models is High.
- The implementation frameworks for Diffusion Models are PyTorch,Hugging Face..
- Diffusion Models belongs to the Neural Networks family.
- The key innovation of Diffusion Models is Denoising Process.
- Diffusion Models is used for Computer Vision
- MambaByte
- MambaByte uses Supervised Learning learning approach
- The primary use case of MambaByte is Natural Language Processing
- The computational complexity of MambaByte is High.
- The implementation frameworks for MambaByte are PyTorch,Hugging Face..
- MambaByte belongs to the Neural Networks family.
- The key innovation of MambaByte is Selective State Spaces.
- MambaByte is used for Natural Language Processing
- RetNet
- RetNet uses Neural Networks learning approach
- The primary use case of RetNet is Natural Language Processing
- The computational complexity of RetNet is Medium.
- The implementation frameworks for RetNet are PyTorch,Hugging Face..
- RetNet belongs to the Neural Networks family.
- The key innovation of RetNet is Retention Mechanism.
- RetNet is used for Natural Language Processing
- CodePilot-Pro
- CodePilot-Pro uses Self-Supervised Learning learning approach
- The primary use case of CodePilot-Pro is Natural Language Processing
- The computational complexity of CodePilot-Pro is High.
- The implementation frameworks for CodePilot-Pro are Hugging Face,OpenAI API..
- CodePilot-Pro belongs to the Neural Networks family.
- The key innovation of CodePilot-Pro is Code Understanding.
- CodePilot-Pro is used for Natural Language Processing
- Retrieval-Augmented Transformers
- Retrieval-Augmented Transformers uses Neural Networks learning approach
- The primary use case of Retrieval-Augmented Transformers is Natural Language Processing
- The computational complexity of Retrieval-Augmented Transformers is High.
- The implementation frameworks for Retrieval-Augmented Transformers are Hugging Face,PyTorch..
- Retrieval-Augmented Transformers belongs to the Neural Networks family.
- The key innovation of Retrieval-Augmented Transformers is Dynamic Knowledge Access.
- Retrieval-Augmented Transformers is used for Natural Language Processing
- Mamba
- Mamba uses Supervised Learning learning approach
- The primary use case of Mamba is Natural Language Processing
- The computational complexity of Mamba is Medium.
- The implementation frameworks for Mamba are PyTorch,Hugging Face..
- Mamba belongs to the Neural Networks family.
- The key innovation of Mamba is Selective State Spaces.
- Mamba is used for Natural Language Processing
- Whisper V3 Turbo
- Whisper V3 Turbo uses Supervised Learning learning approach
- The primary use case of Whisper V3 Turbo is Natural Language Processing
- The computational complexity of Whisper V3 Turbo is Medium.
- The implementation frameworks for Whisper V3 Turbo are PyTorch,Hugging Face..
- Whisper V3 Turbo belongs to the Neural Networks family.
- The key innovation of Whisper V3 Turbo is Real-Time Speech.
- Whisper V3 Turbo is used for Natural Language Processing
- Prompt-Tuned Transformers
- Prompt-Tuned Transformers uses Neural Networks learning approach
- The primary use case of Prompt-Tuned Transformers is Natural Language Processing
- The computational complexity of Prompt-Tuned Transformers is Low.
- The implementation frameworks for Prompt-Tuned Transformers are Hugging Face,PyTorch..
- Prompt-Tuned Transformers belongs to the Neural Networks family.
- The key innovation of Prompt-Tuned Transformers is Parameter-Efficient Adaptation.
- Prompt-Tuned Transformers is used for Natural Language Processing
- Continual Learning Transformers
- Continual Learning Transformers uses Neural Networks learning approach
- The primary use case of Continual Learning Transformers is Continual Learning
- The computational complexity of Continual Learning Transformers is High.
- The implementation frameworks for Continual Learning Transformers are PyTorch,Hugging Face..
- Continual Learning Transformers belongs to the Neural Networks family.
- The key innovation of Continual Learning Transformers is Catastrophic Forgetting Prevention.
- Continual Learning Transformers is used for Continual Learning
- Runway Gen-3
- Runway Gen-3 uses Supervised Learning learning approach
- The primary use case of Runway Gen-3 is Computer Vision
- The computational complexity of Runway Gen-3 is Very High.
- The implementation frameworks for Runway Gen-3 are PyTorch,Hugging Face..
- Runway Gen-3 belongs to the Neural Networks family.
- The key innovation of Runway Gen-3 is Motion Synthesis.
- Runway Gen-3 is used for Computer Vision
- RoPE Scaling
- RoPE Scaling uses Neural Networks learning approach
- The primary use case of RoPE Scaling is Natural Language Processing
- The computational complexity of RoPE Scaling is Low.
- The implementation frameworks for RoPE Scaling are PyTorch,Hugging Face..
- RoPE Scaling belongs to the Neural Networks family.
- The key innovation of RoPE Scaling is Position Encoding.
- RoPE Scaling is used for Natural Language Processing
- InstructGPT-3.5
- InstructGPT-3.5 uses Supervised Learning learning approach
- The primary use case of InstructGPT-3.5 is Natural Language Processing
- The computational complexity of InstructGPT-3.5 is Medium.
- The implementation frameworks for InstructGPT-3.5 are OpenAI API,Hugging Face..
- InstructGPT-3.5 belongs to the Neural Networks family.
- The key innovation of InstructGPT-3.5 is Human Feedback Training.
- InstructGPT-3.5 is used for Natural Language Processing
- Segment Anything Model 2
- Segment Anything Model 2 uses Neural Networks learning approach
- The primary use case of Segment Anything Model 2 is Computer Vision
- The computational complexity of Segment Anything Model 2 is High.
- The implementation frameworks for Segment Anything Model 2 are PyTorch,Hugging Face..
- Segment Anything Model 2 belongs to the Neural Networks family.
- The key innovation of Segment Anything Model 2 is Universal Segmentation.
- Segment Anything Model 2 is used for Computer Vision
- InstructBLIP
- InstructBLIP uses Supervised Learning learning approach
- The primary use case of InstructBLIP is Computer Vision
- The computational complexity of InstructBLIP is High.
- The implementation frameworks for InstructBLIP are PyTorch,Hugging Face..
- InstructBLIP belongs to the Neural Networks family.
- The key innovation of InstructBLIP is Instruction Tuning.
- InstructBLIP is used for Computer Vision
- Hierarchical Attention Networks
- Hierarchical Attention Networks uses Neural Networks learning approach
- The primary use case of Hierarchical Attention Networks is Natural Language Processing
- The computational complexity of Hierarchical Attention Networks is High.
- The implementation frameworks for Hierarchical Attention Networks are PyTorch,TensorFlow..
- Hierarchical Attention Networks belongs to the Neural Networks family.
- The key innovation of Hierarchical Attention Networks is Multi-Level Attention Mechanism.
- Hierarchical Attention Networks is used for Natural Language Processing
- Med-PaLM 2
- Med-PaLM 2 uses Supervised Learning learning approach
- The primary use case of Med-PaLM 2 is Natural Language Processing
- The computational complexity of Med-PaLM 2 is High.
- The implementation frameworks for Med-PaLM 2 are Hugging Face,TensorFlow..
- Med-PaLM 2 belongs to the Neural Networks family.
- The key innovation of Med-PaLM 2 is Medical Specialization.
- Med-PaLM 2 is used for Natural Language Processing