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gguf/README.md

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+# Sophia GGUF
+
+Sophia's llama.cpp/GGUF build — a ~1B-parameter Chinese chat model (KDA+MLA hybrid, pretrained from scratch on a single RTX 5090).
+
+Runs on llama.cpp's `kimi-k3` architecture (Sophia is a fully dense 28-layer instance: [KDA,KDA,KDA,MLA] layer pattern, bounded decay gate, cross-layer attention residuals, SiTU-GLU).
+
+## Files
+
+| File | Size | Notes |
+|---|---|---|
+| `sophia-bf16.gguf` | 2.1 GB | BF16, best quality |
+| `sophia-Q4_K_M.gguf` | 685 MB | Q4_K_M; precision-sensitive tensors (KDA scalars, norms, conv, res_score) stay high-precision via fallback |
+
+## Requirements
+
+Needs an llama.cpp build with the `sophia` pre-tokenizer patch (~15 lines, see [Sophia repo](https://github.com/Arain119/Sophia) `tools/gguf/llama_cpp_sophia.patch`). The GGUF embeds `tokenizer.ggml.pre = "sophia"`: Sophia's tokenizer.json serializes its Split patterns as literals that never match, so the effective tokenization is ByteLevel+BPE over the whole input — the pre-tokenizer must not split at all.
+
+```bash
+# chat (embedded chat template applies automatically)
+llama-cli -m sophia-Q4_K_M.gguf -st -t 8
+
+# server
+llama-server -m sophia-bf16.gguf -c 4096 -t 8 --port 8180
+```
+
+## Parity evidence (BF16 / CPU)
+
+- Tokenization: **151/151 exact token-id match** vs the HF tokenizer
+- Teacher-forced logits: **145/150 argmax agreement (96.7%)**, mean |Δlogprob| = 0.0137; every divergence is a top-2 near-tie (gap ≤ 0.06)
+- Chat-template generation: byte-identical to the HF reference
+
+Converter and reproduction scripts: [Sophia repo](https://github.com/Arain119/Sophia) `tools/gguf/`; details in `docs/gguf_llamacpp.md`.