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name: encrypted-distilbert description: "Run and troubleshoot Nesa's encrypted DistilBERT sentiment demo and local Hugging Face model workflow." metadata: disco-role: operating disable-model-invocation: true license: NO_LICENSE

Encrypted DistilBERT Demo

Use this sub-skill when the user wants to run, adapt, or debug the small Nesa Equivariant Encryption sentiment-classification demo.

Typical triggers:

• "run the Nesa DistilBERT demo"
• "load nesaorg/distilbert-sentiment-encrypted"
• "show encrypted token IDs before sentiment inference"
• "why does the encrypted sentiment demo fail?"
• "compare local model files vs Hugging Face model id"

What this workflow demonstrates

The demo is a client/server simulation:

1. The client receives plaintext text.
2. The encrypted tokenizer turns text into token IDs.
3. The simulated server sees the token IDs and runs an encrypted DistilBERT sequence-classification model.
4. The client receives probabilities/logits and maps labels locally.

The public demo is educational. The model card says the community version is an approximation of the enterprise implementation and may reproduce the original model's output only about 92% of the time.

Choose an execution mode

• Local model directory: best when the user has a directory containing config.json, tokenizer files, and model weights. Validate it with scripts/validate_model_dir.py before loading.
• Hugging Face model id: best when the user permits network downloads and cache use. Use scripts/run_local_demo.py with a model id or local directory.
• Web UI DistilBERT mode: route to web-ui-runtime for browser UI startup and model selection, then return here for interpreting local classification output.

Minimal dependency set

For a CPU-only local check:

python -m pip install torch transformers safetensors

Add accelerate only if the selected model-loading path or web UI requires it. Do not install GPU wheels unless the user needs GPU verification.

Recommended workflow

1. Clarify whether the user has a local model directory or wants a Hugging Face model id.

2. If using a local directory, run the bundled validator:

   python scripts/validate_model_dir.py /path/to/model-dir
   

3. Run the bundled demo wrapper with a short prompt:

   python scripts/run_local_demo.py --model /path/to/model-dir --prompt "I love private AI"
   

   Or, when network access is allowed:

   python scripts/run_local_demo.py --model nesaorg/distilbert-sentiment-encrypted --prompt "I love private AI"
   

4. Report the encrypted token IDs, label probabilities, highest label, and any assumptions about local vs network model loading.

5. If a load fails, read references/troubleshooting.md before changing dependencies or model paths.

References and scripts

• references/workflow.md: detailed local and HF demo recipes, inputs/outputs, and validation steps.
• references/model-card.md: distilled model metadata, limitations, and interpretation notes.
• references/troubleshooting.md: failure modes for missing files, dependency errors, tokenizer/model mismatch, and slow CPU.
• scripts/validate_model_dir.py: read-only local model-directory validator.
• scripts/run_local_demo.py: standalone, promptable DistilBERT sentiment demo wrapper.

Boundaries

• Do not use this sub-skill for full web UI installation or one-click scripts; route to web-ui-runtime.
• Do not use this sub-skill to send remote encrypted Llama requests; route to backend-protocol.
• Do not claim the demo proves enterprise-grade security; route contest/security reasoning to security-contest.