Datasets:

AIencoder
/

llama-cpp-wheels

@@ -1,206 +1,99 @@
 ---
 license: mit
-task_categories:
-  - text-generation
 tags:
-  - llama-cpp
-  - llama-cpp-python
-  - wheels
-  - prebuilt
-  - cpu
-  - gpu
-  - manylinux
-  - gguf
-  - inference
-pretty_name: "llama-cpp-python Prebuilt Wheels"
 size_categories:
-  - 1K<n<10K
 ---
-# 🏭 llama-cpp-python Prebuilt Wheels
-**The most complete collection of prebuilt `llama-cpp-python` wheels for manylinux x86_64.**
-Stop compiling. Start inferencing.
-```bash
-pip install https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.16+openblas_avx2_fma_f16c-cp311-cp311-manylinux_2_31_x86_64.whl
-```
-## 📊 What's Inside
-| | Count |
 |---|---|
-| **Total Wheels** | 3,794+ |
-| **Versions** | 0.3.0 — 0.3.16 (17 versions) |
-| **Python** | 3.8, 3.9, 3.10, 3.11, 3.12, 3.13, 3.14 |
-| **Platform** | `manylinux_2_31_x86_64` |
-| **Backends** | 8 |
-| **CPU Profiles** | 13+ flag combinations |
-## ⚡ Backends
-| Backend | Tag | Description |
-|---------|-----|-------------|
-| **OpenBLAS** | `openblas` | CPU BLAS acceleration — best general-purpose choice |
-| **Intel MKL** | `mkl` | Intel Math Kernel Library — fastest on Intel CPUs |
-| **Basic** | `basic` | No BLAS — maximum compatibility, no extra dependencies |
-| **Vulkan** | `vulkan` | Universal GPU acceleration — works on NVIDIA, AMD, Intel |
-| **CLBlast** | `clblast` | OpenCL GPU acceleration |
-| **SYCL** | `sycl` | Intel GPU acceleration (Data Center, Arc, iGPU) |
-| **OpenCL** | `opencl` | Generic OpenCL GPU backend |
-| **RPC** | `rpc` | Distributed inference over network |
-## 🖥️ CPU Optimization Profiles
-Wheels are built with specific CPU instruction sets enabled. Pick the one that matches your hardware:
-| CPU Tag | Instructions | Best For |
-|---------|-------------|----------|
-| `basic` | None | Any x86-64 CPU (maximum compatibility) |
-| `avx` | AVX | Sandy Bridge+ (2011) |
-| `avx_f16c` | AVX + F16C | Ivy Bridge+ (2012) |
-| `avx2_fma_f16c` | AVX2 + FMA + F16C | **Haswell+ (2013) — most common** |
-| `avx2_fma_f16c_avxvnni` | AVX2 + FMA + F16C + AVX-VNNI | Alder Lake+ (2021) |
-| `avx512_fma_f16c` | AVX-512 + FMA + F16C | Skylake-X+ (2017) |
-| `avx512_fma_f16c_vnni` | + AVX512-VNNI | Cascade Lake+ (2019) |
-| `avx512_fma_f16c_vnni_vbmi` | + AVX512-VBMI | Ice Lake+ (2019) |
-| `avx512_fma_f16c_vnni_vbmi_bf16_amx` | + BF16 + AMX | Sapphire Rapids+ (2023) |
-### How to Pick the Right Wheel
-**Don't know your CPU?** Start with `avx2_fma_f16c` — it works on any CPU from 2013 onwards (Intel Haswell, AMD Ryzen, and newer).
-**Want maximum compatibility?** Use `basic` — works on literally any x86-64 CPU.
-**Have a server CPU?** Check if it supports AVX-512:
-```bash
-grep -o 'avx[^ ]*\|fma\|f16c\|bmi2\|sse4_2' /proc/cpuinfo | sort -u
-```
-## 📦 Filename Format
-All wheels follow the [PEP 440](https://peps.python.org/pep-0440/) local version identifier standard:
-```
-llama_cpp_python-{version}+{backend}_{cpu_flags}-{python}-{python}-{platform}.whl
-```
-Examples:
-```
-llama_cpp_python-0.3.16+openblas_avx2_fma_f16c-cp311-cp311-manylinux_2_31_x86_64.whl
-llama_cpp_python-0.3.16+vulkan-cp312-cp312-manylinux_2_31_x86_64.whl
-llama_cpp_python-0.3.16+basic-cp310-cp310-manylinux_2_31_x86_64.whl
-```
-The local version label (`+openblas_avx2_fma_f16c`) encodes:
-- **Backend**: `openblas`, `mkl`, `basic`, `vulkan`, `clblast`, `sycl`, `opencl`, `rpc`
-- **CPU flags** (in order): `avx`, `avx2`, `avx512`, `fma`, `f16c`, `vnni`, `vbmi`, `bf16`, `avxvnni`, `amx`
-## 🚀 Quick Start
-### CPU (OpenBLAS + AVX2 — recommended for most users)
 ```bash
-sudo apt-get install libopenblas-dev
-pip install https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.16+openblas_avx2_fma_f16c-cp311-cp311-manylinux_2_31_x86_64.whl
 ```
-### GPU (Vulkan — works on any GPU vendor)
-```bash
-sudo apt-get install libvulkan1
-pip install https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.16+vulkan-cp311-cp311-manylinux_2_31_x86_64.whl
 ```
-### Basic (zero dependencies)
-```bash
-pip install https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.16+basic-cp311-cp311-manylinux_2_31_x86_64.whl
 ```
-### Example Usage
-```python
-from llama_cpp import Llama
-llm = Llama.from_pretrained(
-    repo_id="Qwen/Qwen2.5-Coder-7B-Instruct-GGUF",
-    filename="*q4_k_m.gguf",
-    n_ctx=4096,
-)
-output = llm.create_chat_completion(
-    messages=[{"role": "user", "content": "Write a Python hello world"}],
-    max_tokens=256,
-)
-print(output["choices"][0]["message"]["content"])
-```
-## 🔧 Runtime Dependencies
-| Backend | Required Packages |
-|---------|------------------|
-| OpenBLAS | `libopenblas0` (runtime) or `libopenblas-dev` (build) |
-| MKL | Intel oneAPI MKL |
-| Vulkan | `libvulkan1` |
-| CLBlast | `libclblast1` |
-| OpenCL | `ocl-icd-libopencl1` |
-| Basic | **None** |
-| SYCL | Intel oneAPI DPC++ runtime |
-| RPC | Network access to RPC server |
-## 🏭 How These Wheels Are Built
-These wheels are built by the **Ultimate Llama Wheel Factory** — a distributed build system running entirely on free HuggingFace Spaces:
-| Component | Link |
-|-----------|------|
-| 🏭 Dispatcher | [wheel-factory-dispatcher](https://huggingface.co/spaces/AIencoder/wheel-factory-dispatcher) |
-| ⚙️ Workers 1-4 | [wheel-factory-worker-1](https://huggingface.co/spaces/AIencoder/wheel-factory-worker-1) ... 4 |
-| 🔍 Auditor | [wheel-factory-auditor](https://huggingface.co/spaces/AIencoder/wheel-factory-auditor) |
-The factory uses explicit cmake flags matching llama.cpp's official CPU variant builds:
-```
-CMAKE_ARGS="-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DGGML_AVX2=ON -DGGML_FMA=ON -DGGML_F16C=ON -DGGML_AVX=OFF -DGGML_AVX512=OFF -DGGML_NATIVE=OFF"
-```
-Every flag is set explicitly (no cmake defaults) to ensure reproducible, deterministic builds.
-## ❓ FAQ
-**Q: Which wheel should I use?**
-For most people: `openblas_avx2_fma_f16c` with your Python version. It's fast, works on 90%+ of modern CPUs, and only needs `libopenblas`.
-**Q: Can I use these on Ubuntu / Debian / Fedora / Arch?**
-Yes — `manylinux_2_31` wheels work on any Linux distro with glibc 2.31 or newer (Ubuntu 20.04+, Debian 11+, Fedora 34+, Arch).
-**Q: What about Windows / macOS / CUDA wheels?**
-This repo focuses on manylinux x86_64. For other platforms, see:
-- [abetlen's official wheel index](https://abetlen.github.io/llama-cpp-python/whl/) — CPU, CUDA 12.1-12.5, Metal
-- [jllllll's CUDA wheels](https://github.com/jllllll/llama-cpp-python-cuBLAS-wheels) — cuBLAS + AVX combos
-**Q: These wheels don't work on Alpine Linux.**
-Alpine uses musl, not glibc. These are `manylinux` (glibc) wheels. Build from source or use `musllinux` wheels.
-**Q: I get "illegal instruction" errors.**
-You're using a wheel with CPU flags your processor doesn't support. Try `basic` (no SIMD) or check your CPU flags with:
-```bash
-grep -o 'avx[^ ]*\|fma\|f16c' /proc/cpuinfo | sort -u
 ```
-**Q: Can I contribute more wheels?**
-No. The factory is a private space only to be accessable by me but you can suggest some wheels to add via community tab
-## 📄 License
-MIT — same as [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) and [llama.cpp](https://github.com/ggml-org/llama.cpp).
-## 🙏 Credits
-- [llama.cpp](https://github.com/ggml-org/llama.cpp) by Georgi Gerganov and the ggml community
-- [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) by Andrei Betlen
-- Built with 🏭 by [AIencoder](https://huggingface.co/AIencoder)

 ---
 license: mit
 tags:
+- llama-cpp-python
+- wheels
+- pre-built
+- binary
+pretty_name: llama-cpp-python Pre-Built Wheels
 size_categories:
+- 1K<n<10K
 ---
+# 🏭 llama-cpp-python Pre-Built Wheels
+The most complete collection of pre-built `llama-cpp-python` wheels in existence — **8,333 wheels** across every platform, Python version, backend, and CPU optimization level.
+No more building from source. Just find your wheel and `pip install` it directly.
+## 📊 Collection Stats
+| Platform | Wheels |
 |---|---|
+| 🐧 Linux x86_64 (manylinux) | 4,940 |
+| 🍎 macOS Intel (x86_64) | 1,040 |
+| 🪟 Windows (amd64) | 1,010 |
+| 🪟 Windows (32-bit) | 634 |
+| 🍎 macOS Apple Silicon (arm64) | 289 |
+| 🐧 Linux i686 | 214 |
+| 🐧 Linux aarch64 | 120 |
+| 🐧 Linux x86_64 (plain) | 81 |
+| 🐧 Linux RISC-V | 5 |
+| **Total** | **8,333** |
+## 🚀 How to Install
+Find your wheel using the naming convention below, then install directly:
 ```bash
+pip install "https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/YOUR_WHEEL_NAME.whl"
 ```
+### Wheel Naming Convention
 ```
+llama_cpp_python-{version}+{backend}_{profile}-{pytag}-{pytag}-{platform}.whl
 ```
+**Versions:** `0.2.82` through `0.3.18+`
+**Backends (manylinux wheels):**
+- `openblas` — OpenBLAS BLAS acceleration
+- `mkl` — Intel MKL acceleration
+- `basic` — No BLAS, maximum compatibility
+- `vulkan` — Vulkan GPU
+- `clblast` — CLBlast OpenCL GPU
+- `opencl` — OpenCL GPU
+- `rpc` — Distributed inference
+**CPU Profiles (manylinux wheels):**
+- `basic` — Any x86-64 CPU
+- `sse42` — Nehalem+ (2008+)
+- `sandybridge` — AVX (2011+)
+- `ivybridge` — AVX + F16C (2012+)
+- `haswell` — AVX2 + FMA + BMI2 (2013+) ← most common
+- `skylakex` — AVX-512 (2017+)
+- `icelake` — AVX-512 VNNI+VBMI (2019+)
+- `alderlake` — AVX-VNNI (2021+)
+- `sapphirerapids` — AVX-512 BF16 + AMX (2023+)
+**Python tags:** `cp38`, `cp39`, `cp310`, `cp311`, `cp312`, `cp313`, `cp314`, `pp38`, `pp39`, `pp310`
+### Examples
+```bash
+# Linux x86_64, Python 3.11, OpenBLAS, Haswell CPU (most common setup)
+pip install "https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.18+openblas_haswell-cp311-cp311-manylinux_2_31_x86_64.whl"
+# Windows, Python 3.11
+pip install "https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.18-cp311-cp311-win_amd64.whl"
+# macOS Apple Silicon, Python 3.12
+pip install "https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.18-cp312-cp312-macosx_11_0_arm64.whl"
+# macOS Intel, Python 3.11
+pip install "https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.18-cp311-cp311-macosx_10_9_x86_64.whl"
 ```
+## 🏗️ Sources
+- **manylinux wheels** — Built by the [Ultimate Llama Wheel Factory](https://huggingface.co/AIencoder) — a distributed 4-worker HuggingFace Space system covering every llama.cpp cmake option possible on manylinux
+- **Windows / macOS / Linux ARM wheels** — Sourced from [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python) official releases
+## 📝 Notes
+- All wheels are MIT licensed (same as llama-cpp-python)
+- manylinux wheels target `manylinux_2_31_x86_64` (glibc 2.31+, Ubuntu 20.04+)
+- CUDA wheels for Windows/macOS are included (cu121–cu124)
+- Metal wheels for macOS are included
+- This collection is updated periodically as new versions are released