Add comprehensive dataset README

README.md

@@ -1,43 +1,215 @@
 ---
 license: mit
 task_categories:
-- text-generation
-language:
-- en
+  - text-generation
 tags:
-- code
-- llama-cpp
-- wheels
-- linux
+  - llama-cpp
+  - llama-cpp-python
+  - wheels
+  - prebuilt
+  - manylinux
+  - cpu
+  - gpu
+  - vulkan
+  - openblas
+  - mkl
+  - avx2
+  - avx512
+  - gguf
+  - inference
+pretty_name: "llama-cpp-python Prebuilt Wheels"
+size_categories:
+  - 1K<n<10K
 ---
 
-# 🏭 Llama-cpp-python Mega-Factory
+# 🏭 llama-cpp-python Prebuilt Wheels
 
-> **"Stop waiting for `pip` to compile. Just install and run."**
+**The most complete collection of prebuilt `llama-cpp-python` wheels for manylinux.**
 
-This repository is a high-performance archive of pre-compiled `llama-cpp-python` wheels. Specifically built for **Debian/Ubuntu**, these binaries eliminate "compilation hell" and unlock the full potential of your hardware.
+Never compile `llama-cpp-python` from source again. Just `pip install` the exact wheel you need.
 
----
+## 📊 Stats
 
-## 🚀 Why These Wheels?
+| | Count |
+|--|-------|
+| **Total Wheels** | 3,795+ |
+| **Versions** | 0.3.0 — 0.3.16 (17 versions) |
+| **Python** | 3.8, 3.9, 3.10, 3.11, 3.12, 3.13, 3.14 |
+| **CPU Backends** | OpenBLAS, Intel MKL, Basic (no BLAS) |
+| **GPU Backends** | Vulkan, CLBlast, OpenCL, SYCL, RPC |
+| **CPU Optimizations** | AVX, AVX2, AVX512, FMA, F16C, VNNI, VBMI, BF16, AMX, AVX-VNNI |
+| **Platform** | `manylinux_2_31_x86_64` |
 
-Standard wheels target the "lowest common denominator" to avoid crashes on old hardware. This factory uses a massive **Everything Preset** to target specific server-grade instruction sets, maximizing your **Tokens per Second (T/s)**.
+## ⚡ Quick Install
 
-* **Zero Dependencies:** No `cmake`, `gcc`, or `nvcc` required on your target machine.
-* **Server-Grade Power:** Optimized builds for architectures like `Sapphire Rapids`, `Icelake`, `Alderlake`, and `Haswell`.
-* **Full Backend Support:** Pre-configured for `Vulkan`, `OpenBLAS`, `CLBlast`, and `MKL`.
-* **Cutting Edge:** Supporting Python versions from `3.10` up to experimental `3.14`.
+```bash
+# Direct install — just replace the filename with the wheel you need
+pip install https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/WHEEL_FILENAME.whl
+```
 
----
+### Examples
+
+```bash
+# OpenBLAS + AVX2/FMA/F16C (most modern desktops, 2013+)
+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
+
+# AVX512 + VNNI + VBMI (Ice Lake servers, 2019+)
+pip install https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.16+openblas_avx512_fma_f16c_vnni_vbmi-cp311-cp311-manylinux_2_31_x86_64.whl
+
+# Vulkan GPU acceleration
+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 — maximum compatibility (any x86-64 CPU)
+pip install https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.16+basic_basic-cp311-cp311-manylinux_2_31_x86_64.whl
+```
+
+### In a requirements.txt
+
+```
+# URL-encode the + as %2B
+https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.16%2Bopenblas_avx2_fma_f16c-cp311-cp311-manylinux_2_31_x86_64.whl
+```
+
+### In a Dockerfile
+
+```dockerfile
+RUN pip install --no-cache-dir \
+    https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.16%2Bopenblas_avx2_fma_f16c-cp311-cp311-manylinux_2_31_x86_64.whl
+```
+
+### In a HuggingFace Space (packages.txt + requirements.txt)
+
+**packages.txt:**
+```
+libopenblas-dev
+```
+
+**requirements.txt:**
+```
+https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.16%2Bopenblas_avx2_fma_f16c-cp311-cp311-manylinux_2_31_x86_64.whl
+```
+
+## 🔧 Which Wheel Do I Need?
+
+### Step 1: Choose Your Backend
+
+| Backend | Best For | Tag |
+|---------|----------|-----|
+| **OpenBLAS** | General CPU inference, good default | `openblas` |
+| **Intel MKL** | Intel CPUs, potentially faster BLAS | `mkl` |
+| **Basic** | Maximum compatibility, no external deps | `basic` |
+| **Vulkan** | GPU acceleration (NVIDIA, AMD, Intel) | `vulkan` |
+| **CLBlast** | OpenCL GPU acceleration | `clblast` |
+| **OpenCL** | Generic OpenCL devices | `opencl` |
+| **SYCL** | Intel GPU (Arc, Flex, Data Center) | `sycl` |
+| **RPC** | Distributed inference over network | `rpc` |
+
+### Step 2: Choose Your CPU Optimization
+
+Check what your CPU supports:
+
+```bash
+# Linux — check CPU flags
+grep -o 'avx[a-z0-9_]*\|fma\|f16c\|sse4_2' /proc/cpuinfo | sort -u
+```
+
+| CPU Era | Example CPUs | Recommended Tag |
+|---------|-------------|-----------------|
+| **2013+ Desktop** | Haswell, Ryzen 1st gen | `avx2_fma_f16c` |
+| **2017+ Server** | Skylake-X, EPYC | `avx512_fma_f16c` |
+| **2019+ Server** | Ice Lake, EPYC 3rd gen | `avx512_fma_f16c_vnni_vbmi` |
+| **2021+ Desktop** | Alder Lake, 12th gen Intel | `avx2_fma_f16c_avxvnni` |
+| **2023+ Server** | Sapphire Rapids, 4th gen Xeon | `avx512_fma_f16c_vnni_vbmi_bf16_amx` |
+| **2012+ Legacy** | Ivy Bridge | `avx_f16c` |
+| **2011+ Legacy** | Sandy Bridge | `avx` |
+| **Any x86-64** | Anything 64-bit | `basic` |
+
+### Step 3: Build the Filename
+
+```
+llama_cpp_python-{VERSION}+{BACKEND}_{CPU_TAG}-{PYTHON}-{PYTHON}-manylinux_2_31_x86_64.whl
+```
+
+**Example:** Python 3.12 + OpenBLAS + AVX2/FMA/F16C + version 0.3.16:
+```
+llama_cpp_python-0.3.16+openblas_avx2_fma_f16c-cp312-cp312-manylinux_2_31_x86_64.whl
+```
+
+GPU backends don't need a CPU tag:
+```
+llama_cpp_python-0.3.16+vulkan-cp312-cp312-manylinux_2_31_x86_64.whl
+```
+
+## 📋 CPU Optimization Tags Reference
+
+| Tag | CPU Instructions Enabled | CMake Flags |
+|-----|-------------------------|-------------|
+| `basic` | None (pure x86-64) | — |
+| `avx` | AVX | `-DGGML_AVX=ON` |
+| `avx_f16c` | AVX + F16C | `-DGGML_AVX=ON -DGGML_F16C=ON` |
+| `avx2_fma_f16c` | AVX2 + FMA + F16C | `-DGGML_AVX2=ON -DGGML_FMA=ON -DGGML_F16C=ON` |
+| `avx512_fma_f16c` | AVX512 + FMA + F16C | `-DGGML_AVX512=ON -DGGML_FMA=ON -DGGML_F16C=ON` |
+| `avx512_fma_f16c_vnni_vbmi` | AVX512 + FMA + F16C + VNNI + VBMI | `+ -DGGML_AVX512_VNNI=ON -DGGML_AVX512_VBMI=ON` |
+| `avx512_fma_f16c_vnni_vbmi_bf16_amx` | Full server (Sapphire Rapids) | `+ -DGGML_AVX512_BF16=ON -DGGML_AMX_TILE/INT8/BF16=ON` |
+| `avx2_fma_f16c_avxvnni` | AVX2 + FMA + F16C + AVX-VNNI | `+ -DGGML_AVX_VNNI=ON` |
+
+## 🐍 Python Version Support
+
+| Python | Tag | Status |
+|--------|-----|--------|
+| 3.8 | `cp38` | ✅ Full coverage |
+| 3.9 | `cp39` | ✅ Full coverage |
+| 3.10 | `cp310` | ✅ Full coverage |
+| 3.11 | `cp311` | ✅ Full coverage |
+| 3.12 | `cp312` | ✅ Full coverage |
+| 3.13 | `cp313` | ✅ Full coverage |
+| 3.14 | `cp314` | ✅ Full coverage |
+
+## 📦 Naming Convention (PEP 440)
+
+All wheels follow the [PEP 440](https://peps.python.org/pep-0440/) local version identifier standard:
+
+```
+llama_cpp_python-{VERSION}+{LOCAL_TAG}-{PYTHON}-{ABI}-{PLATFORM}.whl
+                           ^
+                           └── Local version label (backend + CPU flags)
+```
+
+The `+` separates the upstream version from the local build variant. The local tag uses `_` to separate components. This is fully PEP 440 compliant and works with `pip`, `requirements.txt`, and all standard Python packaging tools.
+
+## 🏭 How These Wheels Are Built
+
+These wheels are built automatically by the **Ultimate Llama Wheel Factory** — a distributed build system running on HuggingFace Spaces:
+
+- **[Dispatcher](https://huggingface.co/spaces/AIencoder/wheel-factory-dispatcher)** — Command center for creating and managing build jobs
+- **[Workers 1-4](https://huggingface.co/spaces/AIencoder/wheel-factory-worker-1)** — Autonomous Docker-based build agents
+- **[Auditor](https://huggingface.co/spaces/AIencoder/wheel-factory-auditor)** — Validates filenames and repo health
+
+Each wheel is compiled from source with explicit cmake flags — no `-march=native`, ensuring the exact instruction set advertised in the filename.
+
+## ❓ FAQ
+
+**Q: Do I need to install OpenBLAS separately?**
+A: For `openblas` wheels on Linux, yes: `sudo apt install libopenblas-dev`. For `basic` wheels, no external dependencies needed. For HuggingFace Spaces, add `libopenblas-dev` to `packages.txt`.
+
+**Q: Which wheel is fastest?**
+A: Use the most specific wheel your CPU supports. `avx2_fma_f16c` is the sweet spot for most modern hardware. If your CPU has AVX512, use the `avx512` variants for potentially better performance on large batch sizes.
+
+**Q: Can I use these on Ubuntu/Debian/Fedora/etc?**
+A: Yes! `manylinux_2_31` works on any Linux distro with glibc ≥ 2.31. That includes Ubuntu 20.04+, Debian 11+, Fedora 34+, RHEL 9+, and most other modern distros.
+
+**Q: What about Windows/macOS/CUDA wheels?**
+A: This repo currently focuses on manylinux. For other platforms, check [abetlen's wheel index](https://abetlen.github.io/llama-cpp-python/whl/) or [jllllll's cuBLAS wheels](https://github.com/jllllll/llama-cpp-python-cuBLAS-wheels).
+
+**Q: A wheel doesn't work / crashes with SIGILL?**
+A: You're probably using a wheel with CPU instructions your hardware doesn't support (e.g., AVX512 on a non-AVX512 CPU). Try a less specific wheel like `avx2_fma_f16c` or `basic`.
+
+## 📄 License
 
-## 📊 The "Everything" Matrix
-Our private distributed build farm is currently maintaining **3,600+ combinations**:
+MIT — same as [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) and [llama.cpp](https://github.com/ggml-org/llama.cpp).
 
-| Category | Coverage |
-| :--- | :--- |
-| **Llama-cpp-python** | v0.3.12 — v0.3.16+ |
-| **Python Versions** | 3.10, 3.11, 3.12, 3.13, 3.14 |
-| **Backends** | `Basic (CPU)`, `OpenBLAS`, `Vulkan`, `CLBlast`, `MKL` |
-| **Optimizations** | `AVX-512`, `VNNI`, `AMX`, `AVX2` |
+## 🙏 Credits
 
----
+- [Georgi Gerganov](https://github.com/ggerganov) — llama.cpp
+- [Andrei Betlen](https://github.com/abetlen) — llama-cpp-python
+- Built by [AIencoder](https://huggingface.co/AIencoder) with the Ultimate Llama Wheel Factory