Vikhrmodels/salt-asr_speech_1_wav_1_tts_speech_3_text-10k 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.

Review and pin the custom code; never float on `main` with trust_remote_code=True.

1. Read every `.py` file in the repo before first load — this code runs in your process.
2. Pin the revision: `from_pretrained(model_id, revision="<commit sha>", trust_remote_code=True)` so a later push can't swap the code under you.
3. Prefer a version of the architecture already in `transformers` if one exists, which removes the remote-code requirement entirely.

Get a license from the owner or pick a licensed alternative — this is a legal gap, not a technical one.

1. With no declared license you have no usage rights by default; treat the weights as all-rights-reserved.
2. Open an issue or discussion on the repo asking the owner to declare a license, or use the licensed upstream/parent model instead.

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.