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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:Helicone Helicone Integrate Provider To Gateway
- Workflow:Iamhankai Forest of Thought CGDM Post Processing
- Workflow:Openai Openai python Image Generation
- Workflow:Ggml org Llama cpp Text Generation
- Workflow:Hiyouga LLaMA Factory DPO Preference Alignment
- Workflow:OpenGVLab InternVL Multi Stage Pretraining
- Workflow:Langchain ai Langgraph Persistence and Memory Setup
- Workflow:LLMBook zh LLMBook zh github io LoRA Finetuning
- Workflow:Haotian liu LLaVA Web Demo Deployment
- Workflow:Arize ai Phoenix Trace Ingestion Pipeline
Principles
- Principle:Getgauge Taiko Text Input
- Principle:Apache Shardingsphere YAML Configuration Definition
- Principle:Datahub project Datahub Python SDK Installation
- Principle:Huggingface Alignment handbook LLM Evaluation Benchmarks
- Principle:Haotian liu LLaVA Conversation Prompt Construction
- Principle:Helicone Helicone Prerequisites Installation
- Principle:VainF Torch Pruning Hessian Importance
- Principle:ARISE Initiative Robosuite Object Modeling
- Principle:Rapidsai Cuml Cluster Label Assignment
- Principle:Bentoml BentoML Runtime Environment Definition
Implementations
- Implementation:Bigscience workshop Petals Petals Installation
- Implementation:Interpretml Interpret Cpu 64
- Implementation:Ggml org Llama cpp GGUF Hash
- Implementation:Helicone Helicone Env File Setup
- Implementation:Mit han lab Llm awq Auto scale block
- Implementation:LMCache LMCache LMCBlenderBuilder Get Or Create
- Implementation:Tensorflow Serving Tfrt Predict Util
- Implementation:Ggml org Ggml Cpu sgemm
- Implementation:Protectai Llm guard Create app
- Implementation:Microsoft Onnxruntime ORTModule Training Execution
Heuristics
- Heuristic:Cohere ai Cohere python ToolCallV2 Auto UUID Override
- Heuristic:Apache Flink False Positive Availability Optimization
- Heuristic:Danijar Dreamerv3 Percentile Return Normalization
- Heuristic:Fede1024 Rust rdkafka Transaction Error Recovery
- Heuristic:Getgauge Taiko Browser Launch Flags
- Heuristic:Bigscience workshop Petals KV Cache Sizing For Attention Types
- Heuristic:Rapidsai Cuml CUDA Kernel Caching
- Heuristic:DevExpress Testcafe Window Resize Correction
- Heuristic:SeldonIO Seldon core Autoscaling Dual Config Tip
- Heuristic:Helicone Helicone Cost Precision Multiplier
Environments
- Environment:Kubeflow Kubeflow Python KFP SDK Environment
- Environment:Huggingface Alignment handbook BitsAndBytes CUDA
- Environment:Allenai Open instruct Ray Distributed
- Environment:Mbzuai oryx Awesome LLM Post training Python Matplotlib
- Environment:Explodinggradients Ragas Python Runtime Environment
- Environment:ThreeSR Awesome Inference Time Scaling Python Runtime Environment
- Environment:Spcl Graph of thoughts OpenAI API Access
- Environment:Scikit learn Scikit learn OpenMP Thread Configuration
- Environment:Bentoml BentoML BentoCloud Credentials
- Environment:Triton inference server Server TRT LLM Deployment