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| title: README | |
| emoji: 🚀 | |
| colorFrom: gray | |
| colorTo: purple | |
| sdk: gradio | |
| pinned: false | |
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| # 🚀 Big-Endian Models for IBM AIX and IBM i | |
| Welcome! | |
| This organization hosts **machine learning models adapted and validated for Big Endian architectures**, with a primary focus on: | |
| - IBM AIX on Power Systems (now with **10 LLMs ready to use**) | |
| - CPU-only inference (no GPU required) | |
| - Real enterprise environments (not toy benchmarks) | |
| Our goal is simple: | |
| > **Make modern AI usable on legendary mission-critical platforms.** | |
| No vendor lock-in. | |
| No mandatory accelerators. | |
| Just practical inference where your workloads already live. | |
| --- | |
| ## 📦 Available Models (v0.1.1 — Production Ready) | |
| **10 big-endian GGUF models**, fully tested on AIX 7.3 POWER9: | |
| | Model | Params | Speed | Size | Type | | |
| |-------|--------|-------|------|------| | |
| | **LFM2.5-1.2B-Instruct** | 1.17B | **26.9 tok/s** ⭐ Fastest | 695 MB | Instruct | | |
| | **LFM2.5-1.2B-Thinking** | 1.17B | **19.25 tok/s** ⭐ NEW | 731 MB | Reasoning | | |
| | H2O-Danube2-1.8B | 1.8B | 18.59 tok/s | 1.04 GB | Chat | | |
| | SmolLM2-1.7B-Instruct | 1.7B | 17.94 tok/s | 1.0 GB | Instruct | | |
| | DeepSeek-R1-Distill-1.5B | 1.5B | 15.45 tok/s | 1.04 GB | Reasoning | | |
| | StableLM-2-Zephyr-1.6B | 1.6B | 15.02 tok/s | 983 MB | Chat | | |
| | Qwen2.5-Coder-1.5B | 1.54B | 7.67 tok/s | 940 MB | Code | | |
| | Qwen2.5-1.5B-Instruct | 1.54B | 7.55 tok/s | 940 MB | Instruct | | |
| | Llama-3.2-1B-Instruct | 1.24B | 9.03 tok/s | 770 MB | Instruct | | |
| | TinyLlama-1.1B-Chat | 1.1B | ~18 tok/s | 638 MB | Chat | | |
| **Benchmarks**: AIX 7.3 TL4, IBM POWER9 @ 2.75 GHz, 16 threads (SMT-2), GCC 13.3.0 | |
| --- | |
| ## 🚀 Quick Start | |
| Download a model and run on AIX: | |
| ```bash | |
| # Clone and build | |
| git clone https://gitlab.com/librepower/llama-aix.git | |
| cd llama-aix | |
| ./scripts/build_aix_73.sh | |
| # Get a model (example: fastest model) | |
| mkdir -p models | |
| wget https://huggingface.co/librepowerai/LFM2.5-1.2B-Instruct-Q4_K_M-BE/resolve/main/LFM2.5-1.2B-Instruct-Q4_K_M.gguf -O models/ | |
| # Run inference | |
| export LIBPATH=$PWD/build/bin | |
| ./build/bin/llama-simple -m models/LFM2.5-1.2B-Instruct-Q4_K_M.gguf -n 256 "Your prompt" | |
| All models available at: https://huggingface.co/librepowerai | |
| --- | |
| 🔬 What you will find here | |
| Models and artifacts specifically prepared for: | |
| - Big Endian compatibility | |
| - AIX and IBM i runtime environments | |
| - CPU inference (optimized with VSX SIMD + OpenBLAS) | |
| - Minimal external dependencies | |
| v0.1.1 Optimizations: | |
| - VSX auto-vectorization: -O3 -mvsx -maltivec (+6.7% performance) | |
| - OpenBLAS BLAS backend: GEMM acceleration for attention layers | |
| - GCC 13.3.0 native build: no xlc required | |
| Typical contents: | |
| - Big-Endian converted GGUF models (Q4_K_M quantization) | |
| - BE-safe tokenizer assets | |
| - Complete build scripts and documentation | |
| - Performance benchmarks on real Power hardware | |
| Everything here has been tested on actual AIX systems — not emulators. | |
| --- | |
| ⚙️ Tooling | |
| Models here are intended to run with: | |
| - llama-aix - Full port of llama.cpp to AIX 7.3 | |
| https://gitlab.com/librepower/llama-aix | |
| - Native AIX / IBM i toolchains (GCC or IBM Open XL) | |
| - CPU inference (no GPU acceleration needed) | |
| We deliberately avoid GPU assumptions and licensing complexity. | |
| --- | |
| 🧭 Why Big Endian? | |
| Most open-source AI models today assume: | |
| - Little Endian | |
| - Linux | |
| - x86 or CUDA | |
| Enterprise reality is different. | |
| Many production systems run on: | |
| - IBM AIX or IBM i | |
| - POWER9 / POWER10 / POWER11 | |
| - Big Endian binaries | |
| - Highly regulated, long-lifecycle environments | |
| Porting models to BE is not trivial: | |
| tokenizers, SIMD paths, memory layout, third-party deps — everything matters. | |
| This repository exists to close that gap. | |
| --- | |
| 🌱 Philosophy | |
| We believe innovation is often not about new hardware — | |
| it is about unlocking what you already own. | |
| That means: | |
| - reducing infrastructure footprint | |
| - maximizing TCO | |
| - extending the life of enterprise platforms | |
| - keeping data on-prem when needed | |
| - enabling AI without massive architectural changes | |
| This is practical engineering work, driven by real use cases — not marketing slides. | |
| --- | |
| 📚 Documentation & Research | |
| - Build Guide: https://gitlab.com/librepower/llama-aix/-/blob/main/docs/BUILD_AIX_73.md | |
| - Troubleshooting: https://gitlab.com/librepower/llama-aix/-/blob/main/docs/TROUBLESHOOTING_AIX.md | |
| - VSX Research: https://gitlab.com/librepower/llama-aix/-/blob/main/docs/VSX_OPTIMIZATION_RESEARCH.md (Phase A & B complete, Phase C roadmap for v0.2) | |
| - Blog: https://sixe.eu/news/running-liquid-ais-new-model-on-ibm-aix-no-gpu-required | |
| --- | |
| 🔗 Related projects | |
| - LibrePower Open Source Initiative | |
| https://librepower.org | |
| - llama-aix (this port) | |
| https://gitlab.com/librepower/llama-aix | |
| - AIX Ports (RPM packages) | |
| https://gitlab.com/librepower/aix | |
| - llama-ibm-i | |
| https://ajshedivy.notion.site/How-to-run-LLMs-on-IBM-i-1ce662038dd180f8b59bd9cfada2815b | |
| --- | |
| ✉️ Contact | |
| hello {at} librepower.org | |
| #LibrePower — Unlocking Power Systems through open source | |
| Minimal footprint. Unmatched RAS. Better TCO. | |
| Last updated: February 18, 2026 — v0.1.1 production release with 10 models | |