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MLLM
2.0.0 documentation
MLLM
2.0.0 documentation
  • Quick Start
    • How to Support a New LLM: Step-by-Step
    • How to Add a New Operator in MLLM
    • How to run modules async
    • How to perf modules
  • Mllm API Service
    • MLLM CLI
  • Architectures
    • MLLM Framework Core Architecture
    • Tensor
    • Supported MLLM aops Operations
    • Op Plugin System
  • Compile
    • MLLM IR
  • Quantization
    • Data Types in MLLM
    • How to Add New Data Types
  • MLLM LM Cache
  • CPU Backend
    • Parallel API and Thread Configuration in MLLM
    • FA2, Radix, Paged
    • CPU ARM Backend
      • MLLM Self Hosted BLAS
      • Multithread Behaviors in CPU Backend
    • CPU X86 Backend
  • QNN Backend
    • QNN Environment Setup
    • QNN Backend Design
    • QNN Model Conversion
  • MLLM C++ API
    • mllm API
    • Neural Network Layers API
    • Functional API
    • Tensor API
    • ARGeneration API
    • Module API
    • Layer API
  • Contribute
    • Roadmap & Help wanted!
    • Guidelines
    • Model Supports
  • Talks
  • Algorithm
    • Pruning
  • FAQ

Pymllm API

  • pymllm
    • pymllm.compile
      • pymllm.compile.mlir
    • pymllm.convertor
      • pymllm.convertor.mllm_type_mapping
      • pymllm.convertor.model_file_v1
      • pymllm.convertor.model_file_v2
    • pymllm.ffi
      • pymllm.ffi.base
    • pymllm.nn
      • pymllm.nn.functional
    • pymllm.quantize
      • pymllm.quantize.cast2fp32_pass
      • pymllm.quantize.gguf
      • pymllm.quantize.kai
        • pymllm.quantize.kai.w4a32
      • pymllm.quantize.pipeline
      • pymllm.quantize.quantize_pass
      • pymllm.quantize.solver
      • pymllm.quantize.spinquant
    • pymllm.service
      • pymllm.service.models_hub
      • pymllm.service.network
      • pymllm.service.rr_process
      • pymllm.service.tools
    • pymllm.utils
      • pymllm.utils.adb
      • pymllm.utils.error_handler
      • pymllm.utils.mllm_convertor
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QNN BackendΒΆ

  • QNN Environment Setup
    • Overview
    • Prerequisites
    • SDK Download and Installation
    • Environment Setup
    • Op Package Compilation
    • Development Tips
    • Next Steps
  • QNN Backend Design
    • Overview
    • Architecture Components
    • Execution Workflows
    • Quantization Support
    • Custom Operations
    • Performance Optimization
    • Best Practices
    • Troubleshooting
    • API Reference
  • QNN Model Conversion
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QNN Environment Setup
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CPU X86 Backend
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