llama-cpp-py-sync 0.8840

Auto-synchronized Python bindings for llama.cpp using CFFI ABI mode

llama-cpp-py-sync

Auto-synchronized Python bindings for llama.cpp

Overview

llama-cpp-py-sync provides Python bindings for llama.cpp that are kept up-to-date automatically. It generates bindings from upstream headers using CFFI ABI mode, and ships prebuilt wheels.

Key Features

• Automatic upstream sync and binding regeneration
• Prebuilt wheels built by CI
• CPU wheels published to PyPI
• Backend-specific wheels (CUDA / Vulkan / Metal) published to GitHub Releases
• CI checks that the generated CFFI surface matches the upstream C API (functions, structs, enums, and signatures)
• A small, explicit Python API (Llama.generate, tokenize, get_embeddings, etc.)

What You Get (and What You Don’t)

• This project binds to the public C API that llama.cpp exposes in llama.h.
• It does not attempt to bind llama.cpp’s internal C++ implementation such as private headers, C++ classes/templates, or functions that never appear in llama.h.
• We use CFFI ABI mode: Python loads a prebuilt shared library at runtime (no compiled Python extension module for the bindings).
• Because of that, you still need a compatible llama.cpp shared library available, either bundled in the wheel or via LLAMA_CPP_LIB.
• You get a small high-level API (llama_cpp_py_sync.Llama) for common tasks, and an “escape hatch” to call the low-level C functions directly via CFFI when needed.

High-level vs Low-level APIs

• High-level API: llama_cpp_py_sync.Llama is the recommended entry point for typical usage such as generation, tokenization, and embeddings.

import llama_cpp_py_sync as llama

with llama.Llama("path/to/model.gguf", n_ctx=2048, n_gpu_layers=0) as llm:
    print(llm.generate("Hello", max_tokens=64))

• Low-level API: llama_cpp_py_sync._cffi_bindings exposes CFFI access to the underlying llama.cpp C API for advanced use.

from llama_cpp_py_sync._cffi_bindings import get_ffi, get_lib

ffi = get_ffi()
lib = get_lib()

print(ffi.string(lib.llama_print_system_info()).decode("utf-8", errors="replace"))

Installation

This project supports Python 3.8 through 3.14. CI builds wheels with Python 3.13.13 for reproducibility; the published wheels are intended to work across supported Python versions.

From PyPI (Recommended)

pip install llama-cpp-py-sync

This installs the CPU wheel.

Note: depending on CI configuration and platform support, additional wheels may also be published to PyPI.

Quick Chat (Recommended)

After installing from PyPI, you can start an interactive chat session with:

python -m llama_cpp_py_sync chat

If you do not pass --model (and LLAMA_MODEL is not set), the CLI will prompt before downloading a default GGUF model and cache it locally for future runs.

To auto-download without prompting, pass --yes.

One-shot prompt:

python -m llama_cpp_py_sync chat --prompt "Say 'ok'." --max-tokens 32

Use a specific local model:

python -m llama_cpp_py_sync chat --model path/to/model.gguf

From GitHub Releases (Wheel)

Download the wheel for your platform/backend from GitHub Releases and install the .whl:

pip install path/to/llama_cpp_py_sync-*.whl

From Source

git clone https://github.com/FarisZahrani/llama-cpp-py-sync.git
cd llama-cpp-py-sync

# Sync upstream llama.cpp
python scripts/sync_upstream.py

# Regenerate CFFI bindings from the synced llama.cpp headers
# (Optional) record the exact llama.cpp commit SHA in the generated file.
python scripts/gen_bindings.py --commit-sha "$(python scripts/sync_upstream.py --sha)"

# Build the shared library
python scripts/build_llama_cpp.py

# Install the package
pip install -e .

vendor/llama.cpp is cloned locally by scripts/sync_upstream.py (and in CI during builds) and is not committed to this repository.

Quick Start

import llama_cpp_py_sync as llama

# Load a model
llm = llama.Llama("path/to/model.gguf", n_ctx=2048, n_gpu_layers=35)

# Generate text
response = llm.generate("Hello, world!", max_tokens=100)
print(response)

# Streaming generation
for token in llm.generate("Write a poem:", max_tokens=100, stream=True):
    print(token, end="", flush=True)

# Clean up
llm.close()

Using Context Manager

with llama.Llama("model.gguf", n_gpu_layers=35) as llm:
    print(llm.generate("Once upon a time"))

Embeddings

# Load an embedding model
with llama.Llama("embed-model.gguf", embedding=True) as llm:
    emb = llm.get_embeddings("Hello, world!")
    print(f"Embedding dimension: {len(emb)}")

Check Available Backends

from llama_cpp_py_sync import get_available_backends, get_backend_info

print(get_available_backends())  # ['cuda', 'blas'] or similar

info = get_backend_info()
print(f"CUDA available: {info.cuda}")
print(f"Metal available: {info.metal}")

Full API (click to expand)

import llama_cpp_py_sync as llama

# Versions
llama.__version__
llama.__llama_cpp_commit__

# Main class
llm = llama.Llama(
    model_path="path/to/model.gguf",
    n_ctx=512,
    n_batch=512,
    n_threads=None,
    n_gpu_layers=0,
    seed=-1,
    use_mmap=True,
    use_mlock=False,
    verbose=False,
    embedding=False,
)

text = llm.generate(
    "Hello",
    max_tokens=256,
    temperature=0.8,
    top_k=40,
    top_p=0.95,
    min_p=0.05,
    repeat_penalty=1.1,
    stop_sequences=None,
    stream=False,
)

stream = llm.generate(
    "Hello",
    max_tokens=256,
    stream=True,
)

tokens = llm.tokenize("Hello")
text = llm.detokenize(tokens)
piece = llm.token_to_piece(tokens[0])

llm.get_model_desc()
llm.get_model_size()
llm.get_model_n_params()

# Embeddings (requires embedding=True)
emb = llm.get_embeddings("Hello")

llm.close()

# Module-level embeddings helpers
llama.get_embeddings("path/to/model.gguf", "Hello")
llama.get_embeddings_batch("path/to/model.gguf", ["Hello", "World"])

# Backend helpers
llama.get_available_backends()
llama.get_backend_info()
llama.is_cuda_available()
llama.is_metal_available()
llama.is_vulkan_available()
llama.is_rocm_available()
llama.is_blas_available()

How It Works

Automatic Synchronization

1. Scheduled Checks: GitHub Actions checks upstream llama.cpp on a schedule
2. Tag Mirroring: When an upstream tag exists, the workflow can mirror it into this repository
3. Wheel Building: CI builds wheels for all platforms/backends
4. Release Publishing: GitHub Releases are created only for tags that exist upstream
5. PyPI Publishing: CPU-only wheels are published to PyPI for upstream tags (if configured)

Bindings Validation (API Surface)

To keep the Python bindings aligned with upstream, CI runs a validation step that compares upstream llama.h to the generated CFFI cdef.

It checks:

• Public function coverage (missing/extra)
• Struct and enum coverage (missing fields/members)
• Function signatures (return + parameter types)

Local run (after syncing upstream headers):

python scripts/sync_upstream.py
python scripts/gen_bindings.py --commit-sha "$(python scripts/sync_upstream.py --sha)"
python scripts/validate_cffi_surface.py --check-structs --check-enums --check-signatures

CFFI ABI Mode

Unlike pybind11 or manual ctypes, CFFI ABI mode:

• Reads C declarations directly (no compilation needed for bindings)
• Loads the shared library at runtime via ffi.dlopen()
• Automatically handles type conversions
• Works across platforms without modification

Version Tracking

Check which llama.cpp version you're running:

import llama_cpp_py_sync as llama

print(f"Package version: {llama.__version__}")
print(f"llama.cpp commit: {llama.__llama_cpp_commit__}")
print(f"llama.cpp tag: {getattr(llama, '__llama_cpp_tag__', '')}")

GPU Backend Selection

Build-time Detection

The build system automatically detects available backends:

Backend	Platform	Detection
CUDA	Linux, Windows	CUDA_HOME or /usr/local/cuda
ROCm	Linux	ROCM_PATH or /opt/rocm
Metal	macOS	Xcode SDK
Vulkan	All	VULKAN_SDK environment variable
BLAS	All	OpenBLAS, MKL, or Accelerate

Runtime Configuration

# Use GPU acceleration
llm = llama.Llama("model.gguf", n_gpu_layers=35)

# CPU only (no GPU offload)
llm = llama.Llama("model.gguf", n_gpu_layers=0)

# Full GPU offload (all layers)
llm = llama.Llama("model.gguf", n_gpu_layers=-1)

API Reference

Llama Class

class Llama:
    def __init__(
        self,
        model_path: str,
        n_ctx: int = 512,           # Context window size
        n_batch: int = 512,         # Batch size for prompt processing
        n_threads: int = None,      # CPU threads (auto-detect if None)
        n_gpu_layers: int = 0,      # Layers to offload to GPU
        seed: int = -1,             # Random seed (-1 for random)
        use_mmap: bool = True,      # Memory map model file
        use_mlock: bool = False,    # Lock model in RAM
        verbose: bool = False,      # Print loading info
        embedding: bool = False,    # Enable embedding mode
    ): ...
    
    def generate(
        self,
        prompt: str,
        max_tokens: int = 256,
        temperature: float = 0.8,
        top_k: int = 40,
        top_p: float = 0.95,
        min_p: float = 0.05,
        repeat_penalty: float = 1.1,
        stop_sequences: List[str] = None,
        stream: bool = False,
    ) -> Union[str, Iterator[str]]: ...
    
    def tokenize(self, text: str, add_special: bool = True) -> List[int]: ...
    def detokenize(self, tokens: List[int]) -> str: ...
    def get_embeddings(self, text: str) -> List[float]: ...
    def close(self): ...

Backend Functions

def get_available_backends() -> List[str]: ...
def get_backend_info() -> BackendInfo: ...
def is_cuda_available() -> bool: ...
def is_metal_available() -> bool: ...
def is_vulkan_available() -> bool: ...
def is_rocm_available() -> bool: ...
def is_blas_available() -> bool: ...

Embedding Functions

def get_embeddings(model: Union[str, Llama], text: str) -> List[float]: ...
def get_embeddings_batch(model: Union[str, Llama], texts: List[str]) -> List[List[float]]: ...
def cosine_similarity(a: List[float], b: List[float]) -> float: ...

Examples

See the examples/ directory:

• basic_generation.py - Simple text generation
• streaming_generation.py - Real-time token streaming
• embeddings_example.py - Generate and compare embeddings
• backend_info.py - Check available GPU backends
• benchmark.py - Measure token throughput

Smoke Test / Chat CLI

This repository includes an interactive smoke test that can run either as a one-shot prompt (CI-friendly) or as a back-and-forth chat.

# Interactive chat (Ctrl+C or blank line to exit)
python -m llama_cpp_py_sync chat

# One-shot prompt
python -m llama_cpp_py_sync chat --prompt "Say 'ok'." --max-tokens 16

# Use a specific model
python -m llama_cpp_py_sync chat --model path/to/model.gguf

By default it uses LLAMA_MODEL if set. Otherwise it downloads a default GGUF model and caches it locally.

If the default model is missing, the CLI will prompt before downloading it. To auto-download without prompting, pass --yes.

Model cache location:

• Windows: %LOCALAPPDATA%\llama-cpp-py-sync\models\
• Linux/macOS: ~/.cache/llama-cpp-py-sync/models/

Building from Source

Prerequisites

• Python 3.8+
• Ninja
• CMake (configure step)
• C/C++ compiler (GCC, Clang, MSVC)
• Git

Build Commands

# Clone repository
git clone https://github.com/FarisZahrani/llama-cpp-py-sync.git
cd llama-cpp-py-sync

# Sync upstream llama.cpp
python scripts/sync_upstream.py

# Regenerate bindings from the synced llama.cpp headers
# (Optional) record the exact llama.cpp commit SHA in the generated file.
python scripts/gen_bindings.py --commit-sha "$(python scripts/sync_upstream.py --sha)"

# Build with auto-detected backends
python scripts/build_llama_cpp.py

# Build a specific backend
python scripts/build_llama_cpp.py --backend cuda
python scripts/build_llama_cpp.py --backend vulkan
python scripts/build_llama_cpp.py --backend cpu

# On Windows, the build script bundles required runtime DLLs (MSVC/OpenMP and backend runtimes)
# next to the built library by default. You can disable this behavior with:
python scripts/build_llama_cpp.py --no-bundle-runtime-dlls

# Detect available backends without building
python scripts/build_llama_cpp.py --detect-only

# Build wheel
pip install build
python -m build --wheel

Low-level C API access (advanced)

If you need direct access to the underlying C API (beyond the high-level Llama wrapper), you can use the generated CFFI bindings:

from llama_cpp_py_sync._cffi_bindings import get_ffi, get_lib

ffi = get_ffi()
lib = get_lib()

print(ffi.string(lib.llama_print_system_info()).decode("utf-8", errors="replace"))

Project Structure

llama-cpp-py-sync/
├── src/llama_cpp_py_sync/      # Python package
│   ├── __init__.py             # Public API
│   ├── _cffi_bindings.py       # Auto-generated CFFI bindings
│   ├── _version.py             # Version info
│   ├── llama.py                # High-level Llama class
│   ├── embeddings.py           # Embedding utilities
│   └── backends.py             # Backend detection
├── scripts/                     # Build and sync scripts
│   ├── sync_upstream.py        # Sync upstream llama.cpp
│   ├── gen_bindings.py         # Generate CFFI bindings
│   ├── build_llama_cpp.py      # Build shared library
│   └── auto_version.py         # Version generation
├── examples/                    # Example scripts
├── vendor/llama.cpp/           # Upstream source (cloned at build time)
├── .github/workflows/          # CI/CD pipelines
├── pyproject.toml              # Package metadata
└── README.md                   # This file

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run checks:

python scripts/run_tests.py

Optionally also verify wheel packaging locally:

python scripts/run_tests.py

5. Submit a pull request

License

MIT License - see LICENSE for details.

This project uses llama.cpp which is also MIT licensed.

Third-party license notices are included in THIRD_PARTY_NOTICES.txt.

Acknowledgments

• ggml-org/llama.cpp - The upstream C/C++ implementation
• CFFI - C Foreign Function Interface for Python