mlmf 0.2.0

Machine Learning Model Files - Loading, saving, and dynamic mapping for ML models
Documentation 
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[package]

    authors = ["Eric Evans <CireSnave@gmail.com>"]

    categories = ["algorithms", "science"]

    description = "Machine Learning Model Files - Loading, saving, and dynamic mapping for ML models"

    edition = "2024"

    keywords = [

        "gguf",

        "machine-learning",

        "model-files",

        "safetensors",

        "transformers",

    ]

    license = "MIT OR Apache-2.0"

    name = "mlmf"

    readme = "README.md"

    repository = "https://github.com/CireSnave/mlmf"

    version = "0.2.0"



[dependencies]

    # Core Candle dependencies

    candle-core = "0.9"

    candle-nn   = "0.9"



    # Serialization and file handling

    safetensors = "0.6"

    serde       = { version = "1.0", features = ["derive"] }

    serde_json  = "1.0"



    # Error handling and utilities

    anyhow    = "1.0"

    bytemuck  = "1.14"

    byteorder = "1.5"

    regex     = "1.10"

    thiserror = "2"



    # Optional dependencies for specific formats

    tokenizers = { version = "0.22", optional = true }



    # Progress reporting and distributed timestamps

    chrono    = { version = "0.4", features = ["serde"] }

    indicatif = { version = "0.18", optional = true }



    # Parallel processing

    rayon = { version = "1.10", optional = true }



    # Memory mapping for GGUF

    memmap2 = { version = "0.9", optional = true }



    # ONNX support

    prost       = { version = "0.14", optional = true }

    prost-types = { version = "0.14", optional = true }



    # Model card timestamps

    time = { version = "0.3", features = ["serde"] }



    # Half precision floats for ONNX

    half = { version = "2.3", optional = true }



    # Distributed computing support

    tokio = { version = "1.0", features = ["full"] }

    uuid  = { version = "1.0", features = ["v4"] }



[features]

    # Comprehensive by default - MLMF should be full-featured out of the box

    default = ["awq", "gguf", "onnx", "progress", "pytorch", "tokenizers"]



    # Individual format features (can be used for custom combinations)

    awq     = []

    gguf    = ["memmap2"]

    onnx    = ["half", "prost", "prost-types"]

    pytorch = []



    # Utility features  

    progress   = ["indicatif"]

    rayon      = ["dep:rayon"]

    tokenizers = ["dep:tokenizers"]



    # Minimal build for resource-constrained environments

    minimal = ["progress"]



[dev-dependencies]

    tempfile = "3.8"



[build-dependencies]

    prost-build = "0.14"



[[example]]

    name              = "load_llama"

    required-features = []



[[example]]

    name              = "test_gguf_loading"

    required-features = ["gguf"]



[[example]]

    name              = "smart_mapping_test"

    required-features = []



[[example]]

    name              = "test_awq_loading"

    required-features = ["awq"]



[[example]]

    name              = "test_full_gguf_loading"

    required-features = ["gguf"]



[[example]]

    name              = "test_gguf_export"

    required-features = ["gguf"]



[[example]]

    name              = "gguf_export_guide"

    required-features = ["gguf"]



[[example]]

    name              = "pytorch_support_example"

    required-features = ["pytorch"]



[[example]]

    name              = "model_card_example"

    required-features = []



[[example]]

    name              = "onnx_import_example"

    required-features = []



    [package.metadata.docs.rs]

        all-features = true

        rustdoc-args = ["--cfg", "docsrs"]