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Your ML & Data Knowledge Wiki. Best practices and expert-level knowledge for Machine Learning and Data Engineering, covering 1000+ frameworks and libraries from training to deployment.
Browse implementation patterns, configuration guides, debugging heuristics, and battle-tested defaults for frameworks like vLLM, DeepSpeed, Megatron-LM, FlashAttention, Triton, Unsloth, LangChain, and many more. Every page is structured so both humans and AI agents can find what they need fast.
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| Category | Description | Browse |
|---|---|---|
| Workflows | Step-by-step processes and procedures | Browse All |
| Principles | Core ideas and foundational knowledge | Browse All |
| Implementations | Code-level details and modules | Browse All |
| Heuristics | Best practices and guidelines | Browse All |
| Environments | Setup and configuration guides | Browse All |
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Workflows
- Workflow:Obss Sahi COCO Dataset Slicing
- Workflow:TobikoData Sqlmesh Github CICD automation
- Workflow:Turboderp org Exllamav2 EXL2 Model Conversion
- Workflow:Shiyu coder Kronos CSV Finetuning
- Workflow:Spcl Graph of thoughts GoT Keyword Counting Pipeline
- Workflow:Anthropics Anthropic sdk python Streaming Message Interaction
- Workflow:Scikit learn Scikit learn Hyperparameter Tuning
- Workflow:Helicone Helicone LLM Request Proxy Logging
- Workflow:Lm sys FastChat Vicuna SFT Finetuning
- Workflow:Microsoft BIPIA Attack Success Rate Evaluation
Principles
- Principle:AnswerDotAI RAGatouille Index Loading
- Principle:Spcl Graph of thoughts Document Merging Prompt Design
- Principle:Shiyu coder Kronos Topk Dropout Portfolio Strategy
- Principle:OpenRLHF OpenRLHF Process Reward Dataset Construction
- Principle:Microsoft Onnxruntime ONNX Input Schema Definition
- Principle:Huggingface Peft Prefix Tuning
- Principle:Tensorflow Serving HTTP Compression
- Principle:Apache Druid Publication Settings
- Principle:Microsoft Agent framework Azure Chat Client Configuration
- Principle:FMInference FlexLLMGen Checkpoint Format Abstraction
Implementations
- Implementation:Interpretml Interpret AutoVisualizeProvider
- Implementation:Bitsandbytes foundation Bitsandbytes Quantize 4bit
- Implementation:NVIDIA DALI DynamicScratchpad
- Implementation:Pytorch Serve Mnist Response Data
- Implementation:SeleniumHQ Selenium HasLaunchApp
- Implementation:Lm sys FastChat Split Train Test
- Implementation:Rapidsai Cuml Boston Dataset
- Implementation:Avhz RustQuant KNearestNeighbors
- Implementation:LaurentMazare Tch rs Tokenizer From File
- Implementation:Online ml River Metrics Recall
Heuristics
- Heuristic:Ggml org Llama cpp Quantization Quality Tips
- Heuristic:Allenai Open instruct Warning Archived Dev Scripts
- Heuristic:Openai Whisper Compression Ratio Threshold
- Heuristic:Evidentlyai Evidently Column Type Inference Rules
- Heuristic:Webdriverio Webdriverio Stale Element Auto Refetch
- Heuristic:Romsto Speculative Decoding Ngram Order Selection
- Heuristic:Pytorch Serve CPU Performance Tuning
- Heuristic:Marker Inc Korea AutoRAG Passage Filter Safety Minimum
- Heuristic:LLMBook zh LLMBook zh github io LoRA Initialization Strategy
- Heuristic:Risingwavelabs Risingwave Docker Memory Allocation
Environments
- Environment:Online ml River Build Toolchain
- Environment:Mistralai Client python Realtime Transcription Environment
- Environment:Run llama Llama index Fsspec Remote Storage
- Environment:Alibaba ROLL ROCm GPU Environment
- Environment:Princeton nlp SimPO VLLM Inference
- Environment:Kubeflow Kubeflow Python KFP SDK Environment
- Environment:Pytorch Serve vLLM Engine Environment
- Environment:Mbzuai oryx Awesome LLM Post training Python Matplotlib
- Environment:Spotify Luigi Hadoop HDFS Cluster
- Environment:Langgenius Dify Python Backend Environment