{
  lib,
  pkgs,
  fetchFromGitHub,
}:

let
  # FreeToken is an NVIDIA-GPU inference engine: setup.py builds two C++
  # extensions that link the CUDA runtime, and the runtime needs a
  # CUDA-enabled torch. CUDA libraries carry the unfree "CUDA EULA" license,
  # so this package re-imports the flake's nixpkgs with allowUnfree enabled —
  # scoped to freetoken only, leaving the rest of the overlay unchanged.
  nixpkgsUnfree = import pkgs.path {
    inherit (pkgs.stdenv.hostPlatform) system;
    config.allowUnfree = true;
  };

  py = nixpkgsUnfree.python3Packages;

  # torch>=2.11,<2.12: torch-bin 2.11 is this nixpkgs' CUDA build (it links
  # the cuda12.9-* libraries). The extensions must link the same libcudart
  # instance torch-bin links, so CUDA_HOME below is built from the matching
  # cudaPackages.cuda_cudart.
  cudart = nixpkgsUnfree.cudaPackages.cuda_cudart;
  # cuda_runtime_api.h includes crt/host_defines.h & co., which ship with
  # nvcc's header set, not with cudart.
  nvccHeaders = nixpkgsUnfree.cudaPackages.cuda_nvcc;

  # setup.py requires CUDA_HOME containing CUDA headers and libcudart.
  # Single-output cudart provides ${cudart}/include and ${cudart}/lib; the
  # crt/ headers come from nvcc; lib64/ is added for torch's cpp_extension,
  # lib/ is picked up by setup.py's cuda-home/lib fallback.
  cudaHome = nixpkgsUnfree.runCommand "freetoken-cuda-home" { } ''
    mkdir -p $out/include $out/lib64
    ln -s ${cudart}/include/* $out/include/
    ln -s ${nvccHeaders}/include/crt $out/include/crt
    ln -s ${cudart}/lib $out/lib
    for f in ${cudart}/lib/*.so*; do
      ln -s "$f" "$out/lib64/$(basename "$f")"
    done
  '';

  # flashlib==0.3.0, pinned by freetoken, is not in nixpkgs. torch and triton
  # come from the prebuilt wheel builds (torch-bin / triton-bin), the same
  # instances torch-bin propagates — the scope defaults (source-built torch
  # and triton) would put two tritons/torches into the closure.
  flashlib = py.callPackage ./flashlib.nix {
    torch = py.torch-bin;
    triton = py.triton-bin;
  };

  # freetoken pins apache-tvm-ffi==0.1.13.post3; nixpkgs has 0.1.10.
  # 0.1.13 needs cython>=3.2.8 (nixpkgs: 3.2.4), so build it with a newer
  # cython, scoped to this package only.
  apache-tvm-ffi =
    (py.apache-tvm-ffi.override {
      cython = py.cython.overridePythonAttrs (old: rec {
        version = "3.3.0";
        src = fetchFromGitHub {
          owner = "cython";
          repo = "cython";
          tag = version;
          hash = "sha256-gIEqq8DAJF197gH1cMuq5JxI4rV/VHS70vg8hywXDqw=";
        };
      });
    }).overridePythonAttrs
      (old: {
        version = "0.1.13.post3";
        src = fetchFromGitHub {
          owner = "apache";
          repo = "tvm-ffi";
          tag = "v0.1.13-post3";
          fetchSubmodules = true;
          hash = "sha256-AN7AqBl62T8DqnRt7KRvGjqo/c0SJ66QZrCQQ5yicHw=";
        };
        # 0.1.13 runs its suite with pytest-xdist (pyproject addopts = [ "-n"
        # "auto" ]) and touches GPUs; the nixpkgs check inputs have neither
        # xdist nor a GPU. The imports check still runs (and passes).
        dontUsePytestCheck = true;
      });
in
py.buildPythonApplication (finalAttrs: {
  pname = "freetoken";
  version = "0.1.2";
  pyproject = true;

  src = fetchFromGitHub {
    owner = "FlashML-org";
    repo = "FreeToken";
    tag = "v${finalAttrs.version}";
    hash = "sha256-0MhuubuTjNvtQZxisC2cg1dJeR+A6wZ901H5FRv+l+c=";
  };

  # setup.py imports torch.utils.cpp_extension at build time and compiles two
  # C++ extensions (freetoken.kernel._pinned_tensor and _cpu_moe) that link
  # libcudart from CUDA_HOME. ninja is required by BuildExtension.
  build-system = with py; [
    ninja
    setuptools
    torch-bin
    wheel
  ];

  nativeBuildInputs = [ nixpkgsUnfree.autoPatchelfHook ];

  # cudart: RPATH target for the built extensions (autoPatchelf finds
  # libcudart.so.12 here); libtorch comes from torch-bin via dependencies.
  buildInputs = [ cudart ];

  env.CUDA_HOME = cudaHome;

  dependencies = with py; [
    apache-tvm-ffi
    einops
    fastapi
    flashlib
    gguf
    huggingface-hub
    # freetoken's floor is modelscope>=1.37 (nixpkgs has 1.36.2); the only
    # API used is modelscope.snapshot_download (server/args.py), which is
    # stable across both.
    modelscope
    msgpack
    numpy
    openai
    partial-json-parser
    prompt-toolkit
    pydantic
    pyzmq
    safetensors
    torch-bin
    # triton==3.6.0 pin, satisfied with the wheel build (triton-bin) — the
    # same instance torch-bin propagates, keeping one triton in the closure.
    triton-bin
    tqdm
    transformers
    uvicorn
  ];

  # Tests need an NVIDIA GPU and real model checkpoints (pytest markers
  # slow/needs_weights) — impossible in the build sandbox.
  doCheck = false;
  pythonImportsCheck = [ "freetoken" ];

  # Wheel metadata constraints that nixpkgs packages do not match:
  # - gguf is versioned by upstream git rev in nixpkgs ("8951") rather than
  #   PyPI semver; the reader API freetoken uses is stable.
  # - modelscope floor is 1.37 (nixpkgs: 1.36.2); only the stable
  #   snapshot_download API is used.
  pythonRelaxDeps = [
    "gguf"
    "modelscope"
  ];

  passthru = {
    category = "AI Inference";
    updateScript = [
      "nix-update"
      "--flake"
      ".#freetoken"
    ];
  };

  meta = {
    description = "Local MoE-offload LLM inference runtime with OpenAI- and Anthropic-compatible APIs";
    longDescription = ''
      FreeToken is an edge-native MoE serving engine for running
      frontier-scale open-weight models on consumer NVIDIA GPUs (RTX
      30/40/50 series). It treats GPUs, CPUs and host memory as one elastic
      inference platform, with semantic-aware KV/expert caching and runtime
      VRAM re-allocation between caches. It serves OpenAI- and
      Anthropic-compatible APIs.

      Nix packaging notes:
      - Built against this nixpkgs' CUDA 12.9 stack (torch-bin 2.11 +
        cudaPackages.cuda_cudart) instead of upstream's cu130 wheels; CUDA
        12.9 supports the same GPU range, including RTX 50 (sm_120).
      - The optional native accel extras (flashinfer, sglang-kernel) and the
        freetoken-kernel-cache companion wheel are not packaged; the runtime
        falls back to its pure-Triton kernels where those would be used.
      - Requires an NVIDIA GPU at runtime.
    '';
    homepage = "https://github.com/FlashML-org/FreeToken";
    changelog = "https://github.com/FlashML-org/FreeToken/releases/tag/v${finalAttrs.version}";
    # Upstream code is Apache-2.0, but this derivation links CUDA libraries
    # under the unfree CUDA EULA (same as nixpkgs' torch-bin).
    license = lib.licenses.unfree;
    platforms = [ "x86_64-linux" ];
    mainProgram = "ft";
  };
})