mineru-llama-cpp

In-process llama.cpp VLM inference engine for MinerU, exposing a single Engine class with synchronous and asynchronous generate/stream methods. Wraps a pinned build of llama.cpp (no HTTP layer, no subprocess) via the CPython Limited API.

Version 0.1.5 pins llama.cpp to 86a283532072722c5f3363d37d59a874d09fa99b and uses the CPython 3.10 stable ABI. macOS source builds and wheels require macOS 14 or newer. Older 0.1.2 wheels retain their original upstream version and deployment targets.

Status

The performance figures below describe earlier validated builds. The current upstream upgrade has its own validation report; pending platforms are not covered by these historical measurements.

Verified on 4 platforms (build + import + text generate + two-step document extraction):

Key build decisions (all in top-level CMakeLists.txt):

• GGML_BACKEND_DL=ON — backends are dlopen'd MODULEs, not hard-linked; missing GPU loaders (e.g. no libvulkan.so.1) gracefully fall back to CPU
• LLAMA_OPENSSL=OFF — drops OpenSSL/libssl/libcrypto dependency
• GGML_NATIVE=OFF — portable binaries (no -march=native); required for GGML_BACKEND_DL on x86 anyway
• GGML_OPENMP — ON on Linux (14% faster, libgomp bundled in wheel), OFF on macOS/Windows (zero benefit, removes libomp/libgomp dependency)
• BUILD_SHARED_LIBS=ON — prerequisite for GGML_BACKEND_DL

CI wheels

Version 0.1.5 targets six cp310-abi3 wheels. A wheel is reused across standard, GIL-enabled CPython 3.10–3.14, with installation tests on each version. Download the wheel matching your platform from the 0.1.5 GitHub Release and install it with python -m pip install /path/to/mineru_llama_cpp-0.1.5-*.whl. The older 0.1.2 wheels predate this migration.

Alpine/musllinux wheels are no longer built. Linux CPU wheels bundle libgomp.so.1; Linux x86_64 retains the existing x86_64-v3 CPU requirement (AVX2/FMA/BMI2/F16C). The glibc tag does not describe CPU instruction support. AMD RDNA3/4 GPUs use Vulkan, without a ROCm installation.

Vulkan is an optional dynamically loaded module. Missing runtime libraries or devices leave CPU available and do not prevent importing the Python package. CUDA and SYCL are disabled in all builds and cannot be selected. GPU drivers and the Vulkan loader are provided by the system; GPU runtimes are not bundled. macOS keeps the existing llama.cpp Metal/CPU implementation.

Every wheel is checked outside the checkout, including a child process with external GPU SDK search paths removed, and must load its packaged CPU backend. Strict ABI auditing verifies the Python 3.10 floor. macOS also audits Mach-O deployment targets and dependencies. CPU-only CI does not establish GPU execution or full MinerU extraction correctness; device-specific evidence is recorded separately in the validation record.

Backend selection

Set MINERU_LLAMA_CPP_BACKEND before constructing an engine:

MINERU_LLAMA_CPP_BACKEND=auto python your_script.py

Values: auto (default), cpu, vulkan, metal. Automatic selection prefers discrete GPUs over integrated GPUs, then Metal and Vulkan within the same device class. Each engine selects one backend and may use multiple devices from it; its model and multimodal projector use that backend. n_gpu_layers=0 forces CPU. Explicit CPU requests load the best packaged CPU variant and optional CPU BLAS backend without probing GPU drivers. Backend configuration is still validated before loading modules, including with zero GPU layers. Explicitly requesting an unavailable backend raises an error. Removed sycl and cuda values are invalid even with n_gpu_layers=0. Errors after inference starts are surfaced without retrying the request on another backend. Use verbosity=LOG_LEVEL_INFO to see the chosen backend/device, and LOG_LEVEL_DEBUG for native loading details.

External runtimes

Vulkan requires a GPU driver and Vulkan loader matching the system architecture. Linux needs libvulkan.so.1; Windows needs vulkan-1.dll from the GPU driver installation. The wheel does not include a Vulkan loader. Without a usable Vulkan device or loader, automatic selection falls back to CPU. Explicit vulkan selection raises an error when unavailable.

Release CI prepares a fixed Vulkan SDK and builds its backend together with the ggml core. No oneAPI toolkit, SYCL runtime or Level Zero SDK is used. The aggregate release check requires exactly six wheels and verifies their platforms, architectures, ABI tags and required modules. The macOS build still includes the Xcode 16.4 compatibility check.

PyPI publishing

build-wheels.yml only builds and validates wheels. publish-wheels.yml handles both automatic version-tag releases and manual uploads through PyPI Trusted Publishing with GitHub OIDC. Only its separate publishing job has id-token: write; wheel building, selection and verification run without publishing credentials.

Before the first upload, create the GitHub environment pypi in opendatalab/mineru-llama-cpp and register one GitHub Trusted Publisher in the PyPI project's Publishing settings:

Publishing runs only in opendatalab/mineru-llama-cpp. Forks can build and validate wheels. CI obtains short-lived credentials through OIDC and does not use a stored PyPI API token. Automatic publishing starts after a successful v* tag build and requires all six wheels. The publishing workflow must be present on the upstream default branch for its workflow_run trigger to operate. Ordinary branch builds and failed builds do not publish. Manual publishing accepts a successful build run from the same repository, applies wheel_glob as a literal shell argument, audits the selected subset, and transfers only those wheels to the publishing job. Both paths verify the source run's workflow, repository and successful completion before downloading artifacts. The artifact audit checks out the source run's exact head_sha and applies its build policy once. A separate job checks out the publishing workflow's own commit for publishing tests and strict ABI auditing. Historical runs do not need to contain newer publishing tests. No additional CUDA/SYCL file or dependency denylist is applied.

Install (development)

git clone --recurse-submodules https://github.com/johnking0099/mineru-llama-cpp.git
cd mineru-llama-cpp
pip install --no-build-isolation -e .
pip install -e ".[test]"

The first build compiles llama.cpp from source (a few minutes); subsequent builds only recompile this library's own C++ files.

Tests accept MINERU_LLAMA_CPP_TEST_MODEL and MINERU_LLAMA_CPP_TEST_MMPROJ to select local MinerU Q8_0 model files. Set MINERU_LLAMA_CPP_TEST_IMAGE to a real page image to run the image test without the original developer's local fixture. tests/test_output_utf8.py tests the production UTF-8 decoder without loading models; it builds a small test extension using the [test] dependencies and a C++ compiler. The deterministic generation regressions use byte token IDs from the MinerU Q8_0 vocabulary.

Preparing GGUF models

Engine takes two local .gguf files: the main model and its multimodal projector (mmproj). MinerU2.5 ships as HuggingFace safetensors, so you convert it once with llama.cpp's own tooling. Both tools come with the llama.cpp submodule; llama-quantize is also shipped in this package's bin/ directory after a build.

Pre-built Q8_0 GGUF files are available on ModelScope: MinerU2.5-Pro-2605-1.2B-Q8_0.gguf (main model, 506MB) and mmproj-MinerU2.5-Pro-2605-1.2B-Q8_0.gguf (mmproj, 677MB).

To convert from safetensors yourself:

Main model — convert to a BF16 GGUF, then quantize from that BF16 file with llama-quantize:

# safetensors dir -> BF16 GGUF
python third_party/llama.cpp/convert_hf_to_gguf.py \
    /path/to/MinerU2.5-Pro-2605-1.2B \
    --outfile MinerU2.5-Pro-2605-1.2B.gguf \
    --outtype bf16

# BF16 GGUF -> quantized variants (pick what you need)
bin/llama-quantize MinerU2.5-Pro-2605-1.2B.gguf MinerU2.5-Pro-2605-1.2B-Q8_0.gguf   Q8_0
bin/llama-quantize MinerU2.5-Pro-2605-1.2B.gguf MinerU2.5-Pro-2605-1.2B-Q5_K_M.gguf Q5_K_M
bin/llama-quantize MinerU2.5-Pro-2605-1.2B.gguf MinerU2.5-Pro-2605-1.2B-Q4_K_M.gguf Q4_K_M

mmproj (vision projector) — convert directly from safetensors with --mmproj at the desired precision (no separate llama-quantize step; --outtype does the quantization here). The tool auto-prepends the mmproj- prefix to the output filename:

# BF16 mmproj
python third_party/llama.cpp/convert_hf_to_gguf.py \
    /path/to/MinerU2.5-Pro-2605-1.2B \
    --mmproj \
    --outfile mmproj-MinerU2.5-Pro-2605-1.2B.gguf \
    --outtype bf16

# Q8_0 mmproj
python third_party/llama.cpp/convert_hf_to_gguf.py \
    /path/to/MinerU2.5-Pro-2605-1.2B \
    --mmproj \
    --outfile mmproj-MinerU2.5-Pro-2605-1.2B-Q8_0.gguf \
    --outtype q8_0

On macOS/Metal, use the Q8_0 main model — BF16 crashes on Metal (see Known issues). The mmproj can stay BF16 or Q8_0.

Usage

from mineru_llama_cpp import Engine, SamplingParams

with Engine("/path/to/model.gguf", "/path/to/mmproj.gguf") as engine:
    result = engine.generate([{"role": "user", "content": "hello"}])
    print(result.content)

    for chunk in engine.stream([{"role": "user", "content": "hello"}]):
        print(chunk.delta, end="", flush=True)

Async:

async with Engine("/path/to/model.gguf", "/path/to/mmproj.gguf") as engine:
    result = await engine.agenerate([{"role": "user", "content": "hello"}])
    async for chunk in engine.astream([{"role": "user", "content": "hello"}]):
        print(chunk.delta, end="", flush=True)

Images go in content as {"type": "image_url", "image_url": {"url": "data:image/png;base64,...."}} — must be a pre-encoded base64 data URI; this library does not accept PIL.Image objects, file paths, or HTTP URLs (see docs/superpowers/specs/2026-08-03-mineru-llama-cpp-engine-design.md §6 for why).

Engine parameters

Batch inference

from mineru_llama_cpp import Engine
from mineru_vl_utils import MinerUClient

with Engine(model_path, mmproj_path, n_parallel=8) as engine:
    client = MinerUClient(backend="llama-cpp-engine", llama_cpp_engine=engine)
    results = client.batch_two_step_extract(images)

batch_two_step_extract dispatches requests concurrently across the engine's slots (batching_mode="concurrent").

Cross-platform deployment

Linux

# Standard build (CPU + Vulkan via GGML_BACKEND_DL, OpenMP ON)
uv pip install --no-build-isolation -e .

# Or with Vulkan explicitly:
SKBUILD_CMAKE_ARGS="-DGGML_VULKAN=ON" uv pip install --no-build-isolation -e .

With GGML_BACKEND_DL=ON, the Vulkan backend is a dlopen'd MODULE — if libvulkan.so.1 is missing, the engine gracefully falls back to CPU without crashing. The libgomp.so.1 OpenMP runtime is bundled into the wheel (under mineru_llama_cpp/lib/), so users don't need it preinstalled.

Windows

# Requires MSVC Build Tools + vcvars64 in PATH
set CMAKE_GENERATOR=Ninja
uv pip install --no-build-isolation -e .

On Windows x86_64, MSVC compiles ggml-cpu directly. On Windows ARM64, ggml-cpu/CMakeLists.txt rejects MSVC — use clang-cl instead:

set SKBUILD_CMAKE_ARGS=-DCMAKE_C_COMPILER=clang-cl;-DCMAKE_CXX_COMPILER=clang-cl

Windows ARM64 wheels also include a native Vulkan MODULE. Install an ARM64 GPU driver that provides Vulkan; missing loader or devices allow CPU fallback. The Vulkan SDK is only required when building from source. Use the native Windows Arm SDK and add -DGGML_VULKAN=ON to the ARM64 build arguments above. Its native Lib/vulkan-1.lib and Bin/glslc.exe must target ARM64. The release build pins SDK 1.4.350.0 and audits every DLL, executable and Python extension for ARM64 machine code. It does not bundle a GPU driver or SYCL.

The __init__.py adds bin/ to the DLL search path via os.add_dll_directory() — Windows has no RPATH ($ORIGIN), so this is needed for the .pyd to find its sibling DLLs.

On older Intel UHD iGPUs (pre-2020, 24-32 EU), Vulkan may be slower than CPU for small models. Set n_gpu_layers=0 to force CPU.

macOS

Requires macOS 14+. Apple Silicon wheels embed Metal shader source and compile it on the user's machine. M1-M4 default to ordinary Metal kernels; eligible M5 devices enable the tensor API only after runtime capability and compilation checks. Tensor compilation uses Metal 4.0 on supported systems; the package's minimum deployment target remains macOS 14. Intel wheels use CPU. Use Q8_0 models for both model and mmproj (see the existing BF16 limitation). OpenMP is OFF on macOS. Intel source builds default to a CPU instruction baseline compatible with Rosetta; explicit CMake feature flags can opt into AVX/FMA on capable hardware.

To disable only the optional tensor API, set this before starting Python:

export GGML_METAL_TENSOR_DISABLE=1

Ordinary Metal GPU acceleration remains available. To force CPU, use Engine(..., n_gpu_layers=0).

Diagnose an installed wheel from outside the checkout, preserving complete initialization logs and structured results:

python /path/to/mineru-llama-cpp/tools/diagnose_metal.py \
  --model /path/to/model-Q8_0.gguf --mmproj /path/to/mmproj-Q8_0.gguf \
  --image /path/to/page.png --extract --mode default --output /tmp/metal-check

Modes are default, disable, enable and cpu; each starts a separate process. --extract additionally requires Pillow and mineru-vl-utils. result.json records the compiled upstream SHA, timings, peak RSS, tensor capability and actual layer offloading; native.log retains the raw logs. Tensor capability alone does not prove tensor kernels were executed. See upgrade validation for measured and pending combinations.

Build configuration

All build decisions are in the top-level CMakeLists.txt as forced cache variables. The patches/llama.cpp/*.patch files are auto-applied during CMake configure via git apply (requires the submodule to be a real git checkout).

OpenMP (libgomp) — platform-differential

• Linux: ON (default). ~14% faster than pthread fallback. libgomp.so.1 bundled in the wheel as a real file (symlink resolved via file(REAL_PATH)). Verified in a python:3.12-slim container with no system libgomp.
• macOS: OFF. Metal is primary path; no libgomp on macOS.
• Windows: OFF. Zero perf delta on ARM64; removes libomp140 dependency.

Verifying the build

python -c "
from mineru_llama_cpp import Engine
with Engine('/path/to/model.gguf', '/path/to/mmproj.gguf') as engine:
    print(engine.generate([{'role': 'user', 'content': 'hello'}]).content)
"

Known issues

See docs/known-issues.md — notably: use Q8_0 models, not BF16, on Metal.

License

This project's own code is licensed under the MIT License. Commercial use, modification, and redistribution are permitted, provided the copyright and license notices are retained. Model weights and third-party components remain subject to their respective licenses.