Update README.md

README.md

@@ -89,27 +89,6 @@ print(tok.decode(out[0, inputs["input_ids"].shape[1]:]).replace("<|im_end|>", ""
 Sampling defaults live in `generation_config.json` (temperature 0.7, top-k 40). Keep prompt + answer under
 1024 bytes; the model never saw longer sequences.
 
-### GGUF / llama.cpp
-
-The tokenizer has no BPE merges and a pre-tokenizer unknown to the stock converter, so
-`convert_hf_to_gguf.py` fails on it. The `make_gguf.py` wrapper from the code repository patches this
-at conversion time without modifying llama.cpp (details in its header):
-
-```bash
-git clone https://github.com/ggml-org/llama.cpp
-python make_gguf.py llama.cpp CalmaCatCoder-Next-96M calmacat-96m-f16.gguf f16     # or q8_0
-```
-
-Because `<|im_end|>` is plain text, llama.cpp does not stop on it by itself. Save the ChatML prompt to a file
-and use a reverse prompt:
-
-```bash
-llama-completion -m calmacat-96m-f16.gguf -no-cnv --no-escape -f prompt.txt -r "<|im_end|>" -n 300 --temp 0.7 --top-k 40
-```
-
-A warning `special_eos_id is not in special_eog_ids` is expected: the model has no EOS token.
-Prefer f16/bf16 or q8_0; this model is so small that aggressive quantization will likely hurt it.
-
 ## How it was trained
 
 All of it ran on a single RTX 5060 Ti (16 GB) under Windows with a PyTorch nightly build.