Add README documentation

README.md

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+---
+license: apache-2.0
+tags:
+  - llama
+  - gguf
+  - big-endian
+  - aix
+  - power
+  - librepower
+library_name: llama.cpp
+---
+
+# TinyLlama 1.1B Chat - Big-Endian GGUF
+
+This is a **big-endian** version of [TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) in GGUF format, optimized for **IBM AIX on POWER architecture**.
+
+## Model Details
+
+- **Base Model**: TinyLlama 1.1B Chat v1.0
+- **Format**: GGUF (Q4_K_M quantization)
+- **Endianness**: Big-endian (IBM Power Systems)
+- **Size**: 638 MB
+- **License**: Apache 2.0
+
+## Usage
+
+This model is designed for use with [llama-aix](https://gitlab.com/librepower/llama-aix), a port of llama.cpp for IBM AIX.
+
+```bash
+# Download model
+wget https://huggingface.co/librepower/tinyllama-1.1b-chat-be/resolve/main/tinyllama-1.1b-q4_k_m-be.gguf
+
+# Run inference on AIX
+./llama-simple -m tinyllama-1.1b-q4_k_m-be.gguf -n 128 -p "Hello, world!"
+```
+
+## Performance
+
+On IBM POWER9 (16 cores, 128GB RAM):
+- **Speed**: ~18 tokens/second
+- **Memory**: ~800 MB RAM
+
+## Why Big-Endian?
+
+IBM Power Systems use big-endian byte order, while most modern systems use little-endian. This model has been converted using llama.cpp's endianness conversion tool to run natively on AIX without runtime conversion overhead.
+
+## Conversion
+
+This model was converted from the original little-endian GGUF using:
+
+```bash
+./llama-gguf-split --convert-be model.gguf model-be.gguf
+```
+
+## About LibrePower
+
+**Unlocking Power Systems through open source.**
+
+LibrePower brings modern AI and open-source tools to IBM Power Systems, extending the life and capabilities of enterprise infrastructure.
+
+- Web: https://librepower.org
+- GitLab: https://gitlab.com/librepower
+- Newsletter: https://librepower.substack.com
+
+## Related Projects
+
+- [llama-aix](https://gitlab.com/librepower/llama-aix) - llama.cpp for IBM AIX
+- [redbook-explorer](https://gitlab.com/librepower/redbook-explorer) - RAG application for IBM Redbooks
+
+## Citation
+
+Original model by Zhang et al. (TinyLlama team):
+
+```bibtex
+@article{tinyllama,
+  title={TinyLlama: An Open-Source Small Language Model},
+  author={Zhang, Peiyuan and Guangtao, Zeng and Wang, Tianduo and Lu, Wei},
+  journal={arXiv preprint arXiv:2401.02385},
+  year={2024}
+}
+```