[RFC] Single-archive model distribution format for LLM

🚀 The feature, motivation and pitch

Problem

Deploying an ExecuTorch LLM requires managing multiple separate files:

• .pte — exported program
• .ptd — optional external weights
• Tokenizer file (tokenizer.json, .model, .bin)
• Prompt template / chat template — not stored anywhere, app must hard-code per model family
• Generation config (num_bos, num_eos, etc.) — caller must set correctly

Some metadata lives in .pte via constant_methods (get_max_seq_len, get_eos_ids, etc.), but tokenizer and chat template are external. Users can easily mismatch tokenizer and model, or use the wrong prompt format.

Proposal

A zip archive (.etm — ExecuTorch Model) bundling everything:

model.etm
├── model.pte
├── tokenizer.json
├── metadata.json          # chat_template, num_bos, default_temperature, etc.
└── weights/               # optional
    └── foundation.ptd

metadata.json carries what's not in constant_methods today — most importantly chat_template, so the runner can format prompts without app-side logic.

Prior Art

• MediaPipe Tasks (.task): TFLite model + tokenizer + metadata in a single zip-like bundle
• GGUF (llama.cpp): single file embedding tokenizer vocab and metadata alongside weights
• Hugging Face: config.json + tokenizer.json + chat_template in a model repo

Open Questions

1. File extension: .etm? .etb? Just .zip?
2. LLM-only initially, or general-purpose for all ExecuTorch models?
3. Chat template format: Jinja2 (HF-compatible) or simpler substitution?
4. Runtime loading: unzip to temp dir vs. read from zip in-memory?
5. Keep supporting multi-file approach alongside?

Alternatives

No response

Additional context

No response

RFC (Optional)

No response