mlmf/Cargo.toml at main · ciresnave/mlmf · GitHub

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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 = "0.3.0"

[dependencies]
    # Candlelight - Unified Candle ML framework wrapper (from GitHub)
    # Use default-features = false to avoid pulling in flash-attn and other CUDA
    # acceleration features.  MLMF is a file-loading library and never runs inference
    # kernels, so it only needs DType/Device from candlelight.  flash-attn's build
    # script fetches CUTLASS from GitHub and checks out files with extremely long
    # Doxygen-generated HTML names that exceed Windows' MAX_PATH (260 chars), breaking
    # the build on Windows entirely.
    candlelight = { git = "https://github.com/ciresnave/candlelight", default-features = false }

    # Serialization and file handling
    safetensors = "0.7"
    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"
    sysinfo   = "0.38"
    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", "rayon", "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"

[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"]