auhide/chef-gpt warn

Convert the weights to safetensors before loading them anywhere that matters.

1. Do not load the pickle files in-process — pickle deserialization executes arbitrary code from the file.
2. Convert locally in a sandbox: `pip install safetensors` and use `safetensors.torch.save_file` on a state dict loaded with `torch.load(..., weights_only=True)` (refuses most code-execution payloads), or use Hugging Face's `convert.py` space/script.
3. Pin the exact revision hash you converted from, and load only your converted safetensors artifact from then on.

Align the stop-token declarations — a pure metadata fix to `generation_config.json` (and `config.json`).

1. Identify the token the chat template actually ends assistant turns with (e.g. `<|eot_id|>`, `<end_of_turn>`, `<|im_end|>`) and make sure its id is in `generation_config.json`'s `eos_token_id` list.
2. Keep `config.json`'s `eos_token_id` consistent with (or a subset of) `generation_config.json`'s — runtimes differ in which file they read.
3. For the pad-equals-EOS hazard: repoint `pad_token` at a dedicated padding token before fine-tuning; serving is unaffected.
4. Until the repo is fixed, pass explicit stop tokens to your serving stack (e.g. vLLM `stop_token_ids`, llama.cpp `--override-kv tokenizer.ggml.eos_token_id`).

Not patchable: the vocab size mirrors the embedding matrix in the weights. Verify the change was intentional.

1. Do not edit `vocab_size` in config.json to "match the parent" — it must equal the embedding table in the shipped weights or the model won't load.
2. Diff the tokenizers (`tokenizer.json` / added_tokens) against the parent to see what was added or removed, and test your own identifiers, codes, and non-English text through both.
3. If the drift is unexplained by the model card, treat tokenization-sensitive behavior as unvalidated on this model.