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A newer version of the Gradio SDK is available: 6.19.0

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metadata 
title: Jawbreaker
emoji: 馃崿
colorFrom: yellow
colorTo: red
pinned: true
sdk: gradio
sdk_version: 6.16.0
python_version: 3.12
app_file: app.py
license: mit
short_description: Private scam defense for someone you love.
tags:
  - track:backyard
  - sponsor:openbmb
  - sponsor:openai
  - sponsor:modal
  - achievement:offgrid
  - achievement:welltuned
  - achievement:offbrand
  - achievement:sharing
  - achievement:fieldnotes
  - gradio
  - build-small-hackathon
  - backyard ai
  - backyard-ai
  - openbmb
  - minicpm
  - minicpm5
  - tiny titan
  - tiny-titan
  - well tuned
  - well-tuned
  - off brand
  - off-brand
  - off the grid
  - off-the-grid
  - best demo
  - best-demo
  - community choice
  - community-choice
  - bonus quest champion
  - bonus-quest-champion
  - sharing is caring
  - sharing-is-caring
  - field notes
  - field-notes
  - modal
  - best use of modal
  - best-use-of-modal
  - codex
  - openai
  - best use of codex
  - best-use-of-codex
  - local-first
  - scam-defense
  - zerogpu
models:
  - openbmb/MiniCPM5-1B
  - build-small-hackathon/jawbreaker-minicpm5-1b-lora-v8
datasets:
  - build-small-hackathon/jawbreaker-scam-defense-data

Jawbreaker logo

Jawbreaker

Scam defense for someone you love.

Try it: Live Space 路 Demo video 路 Reddit post 路 LinkedIn post 路 X post 路 Article 路 Model 路 Dataset/evals 路 GitHub

Why this exists: The motivating user is a friend's grandmother who had already been affected by scam messages. Private details are intentionally omitted, but that family context shaped the product: this is not a generic spam classifier for security experts; it is a calm safety check for someone who needs to know whether to reply, click, call, or ask for help.

TL;DR for Judges

Jawbreaker is built around direct small-model inference to protect user privacy. The public hackathon Space runs MiniCPM5-1B + Jawbreaker LoRA on Hugging Face ZeroGPU for judge access, and the repo keeps local Transformers/GGUF tooling for running without hosted LLM APIs.

Jawbreaker helps a real person pause before clicking, replying, or sending money. Paste a suspicious text, email, or DM and Jawbreaker breaks it into plain-English warning signs: what the sender is pretending to be, what pressure tactic is being used, what they want, and the safest next step.

The problem is specific: scam messages now arrive as urgent, personal, plausible requests. A package fee, a bank callback, a fake recruiter, or a "new phone number" from a family member can pressure someone into clicking or paying before they ask for help. Jawbreaker turns that moment into a small safety workflow: paste the message, get a clear verdict, see the warning signs, see whether the message needs more context, and copy a short note to someone you trust.

Demo

Watch the Jawbreaker demo video

Hackathon

Submission checklist:

  • REQ-01 / Stay under 32B: complete. The live model is openbmb/MiniCPM5-1B.
  • REQ-02 / Ship a Gradio app: complete. Jawbreaker is a public Gradio Space in build-small-hackathon.
  • REQ-03 / Record a demo: complete. Demo video: https://youtu.be/oh0GRKYXvGM
  • REQ-04 / Post it: complete. Social posts: Reddit, LinkedIn, and X.
  • REQ-05 / Mind the GPU limit: complete. This is one ZeroGPU Space, below the limit.
  • REQ-06 / Tag your README: complete. Frontmatter includes the main track, sponsor tracks, and claimed bonus badges.

Built With OpenAI Codex

This project is being built with OpenAI Codex in the Codex desktop app. Codex is being used for planning, implementation, eval design, Gradio UI iteration, testing, deployment, and submission documentation.

Codex evidence:

  • Public GitHub repo linked from this Space README.
  • Codex-attributed commits are included for build work.
  • CODEX_JUDGE_EVIDENCE.md maps Codex-attributed commits to concrete files, model/eval decisions, and final public artifacts.
  • Codex scaffolded and iterated on app.py, the custom Gradio Server UI, jawbreaker/ analyzer/schema/render modules, eval/run_eval.py, training/train_lora.py, training/modal_train.py, training/modal_eval.py, and the public submission docs.
  • AGENT_TRACE.md records the development process.
  • FIELD_NOTES.md records product and technical decisions.
  • HONEST_SUBMISSION.md records what the project can and cannot honestly claim.

Why This Is Small

Jawbreaker is deliberately narrow. It does not try to be a general assistant or chatbot. It performs one safety task:

  1. Read one suspicious message.
  2. Identify scam risk and manipulation tactics.
  3. Give one clear safe action.
  4. Surface uncertainty when a message is too short or missing context.
  5. Help the user ask someone they trust with a copyable warning note.

What's Inside

Component Model / Library Where it runs
Scam analysis openbmb/MiniCPM5-1B + Jawbreaker LoRA v8 Hugging Face ZeroGPU / Transformers
Safety guard Schema validation + deterministic heuristic guard App runtime
Interface Custom gr.Server kitchen-table UI Gradio Space
Training/eval PEFT/LoRA + guarded eval harness Modal A100

Model Runtime

The deployed Space uses openbmb/MiniCPM5-1B through Hugging Face Transformers on ZeroGPU with the published Jawbreaker LoRA adapter:

  • Adapter: build-small-hackathon/jawbreaker-minicpm5-1b-lora-v8
  • Training: PEFT/LoRA on Modal A100
  • Eval: guarded Modal A100 run across the 632-case hard v8 suite, with earlier 320/394-case v4 comparison runs
  • Runtime: ZeroGPU in the Hugging Face Space
  • Off the Grid: the app loads and runs the small open model directly through Transformers in the Space runtime; it does not call OpenAI, Anthropic, hosted MiniCPM, or other external LLM APIs for inference

Why this model:

  • It makes OpenBMB MiniCPM central to the app, matching the hackathon sponsor track.
  • It is a 1B model, which fits the Tiny Titan spirit while staying useful on a narrow task.
  • The 1B v8 adapter keeps the Tiny Titan/OpenBMB path while clearing the broader 632-case safety gate.
  • It avoids external commercial model APIs.
  • It can produce the structured JSON that Jawbreaker validates before rendering.

The local/eval path still supports GGUF models through llama-cpp-python, including Qwen and MiniCPM GGUF candidates. The CPU GGUF path is kept as evidence and tooling, while the judge-facing Space uses ZeroGPU because first-click cold-start latency matters for the product experience.

Safety architecture:

  • Model output must parse as JSON and match the required schema.
  • A deterministic heuristic guard catches weak model outputs that under-call obvious danger.
  • If MiniCPM generation fails or returns malformed JSON, Jawbreaker falls back to deterministic safety analysis instead of showing an unusable error state.
  • The UI always recommends verification through official channels or a known phone number, never the suspicious link or number.
  • Session memory is local to the current Gradio session and helps show repeated scam patterns.

Model Selection Evidence

Risk accuracy comparison for Jawbreaker model candidates

Candidate Size Eval set Risk accuracy Dangerous -> safe Dangerous -> needs check Safe -> dangerous/suspicious Invalid JSON Unsafe actions Why not final
Heuristic guard none 215 hard cases 84.7% 0 0 0 0 0 Guard layer only, not model-led.
MiniCPM4.1 LoRA v3 8B 215 hard cases 97.7% 0 0 0 0 0 Strong, but not Tiny Titan.
MiniCPM5 LoRA v4 1B 394 hard cases 96.2% 0 0 3 0 0 Strong, narrower eval and a few safe-message overcalls.
MiniCPM5 LoRA v8 1B 632 hard cases 91.6% 0 0 0 0 0 Final: broadest safety gate.

Jawbreaker ships the 1B v8 adapter not because it has the prettiest accuracy number, but because it cleared the broadest completed hard safety gate with zero dangerous undercalls, zero safe-message overcalls, zero unsafe actions, zero invalid JSON, and zero model errors.

Qwen/Qwen3-0.6B was an early ZeroGPU runtime prototype and remains a documented fallback path, but it is not included in the numeric comparison because the final committed reports are for the heuristic guard and MiniCPM LoRA candidates above. The judged path is MiniCPM5-1B + Jawbreaker LoRA v8.

Training/eval artifacts:

  • Hugging Face dataset: build-small-hackathon/jawbreaker-scam-defense-data publishes the sanitized/synthetic evals, generated training splits, and final reports.
  • eval/scam_eval.jsonl: 100 hand-curated synthetic/sanitized eval cases.
  • eval/field_examples.jsonl: sanitized real-world examples from a friend, with names and phone numbers removed.
  • training/generate_jawbreaker_data.py: deterministic generator for larger train/dev/test splits.
  • training/generate_v3_data.py: contrastive hard-case generator used for the v3 LoRA pass.
  • training/generate_v4_data.py, generate_v5_data.py, generate_v6_data.py, generate_v7_data.py, generate_v8_data.py: later calibration generators used to stress-test false positives, trusted-route boundaries, fresh public scam patterns, and wrong-number investment grooming.
  • training/data/train.jsonl, dev.jsonl, test.jsonl: generated SFT records for Jawbreaker JSON behavior.
  • training/data/train_v3.jsonl, dev_v3.jsonl, test_v3.jsonl: v3 contrastive training split.
  • eval/generated_eval.jsonl: generated holdout eval set.
  • eval/hard_v2_eval.jsonl: hard eval set used to compare v2 and v3 adapters.
  • eval/hard_v4_eval.jsonl, hard_v5_eval.jsonl, hard_v6_eval.jsonl, hard_v7_eval.jsonl, hard_v8_eval.jsonl: expanded hard evals used during 1B calibration.
  • eval/reports/jawbreaker-minicpm5-1b-lora-v8-hard632-safetyguard-v4.json: main final model evidence.
  • training/train_lora.py: PEFT/LoRA script for publishing Jawbreaker MiniCPM adapters.
  • training/modal_train.py: Modal A100 training launcher used for the MiniCPM LoRA passes.
  • training/modal_eval.py: Modal A100 eval launcher used for guarded hard-suite scoring.
  • HONEST_SUBMISSION.md: guardrails to avoid overclaiming synthetic data, fine-tuning, or runtime behavior.

Prize Eligibility

Prize / Badge Status Evidence
Backyard AI Submitted Practical scam-defense app for someone close, with a focused safety workflow.
Best MiniCPM Build Submitted openbmb/MiniCPM5-1B is the core runtime model, with a published Jawbreaker LoRA adapter.
OpenAI / Best Use of Codex Submitted Public GitHub repo includes Codex-attributed commits plus CODEX_JUDGE_EVIDENCE.md, AGENT_TRACE.md, and CODEX_BUILD_LOG.md.
Best Use of Modal Submitted Modal A100 was used for PEFT/LoRA training and guarded eval runs across the MiniCPM calibration path; see training/modal_train.py, training/modal_eval.py, and the committed 632/394/320-case eval report files.
Community Choice Eligible Public Space, collection, model, and dataset are live; outcome depends on community voting and engagement.
Tiny Titan Submitted The deployed model is openbmb/MiniCPM5-1B, well under the 4B badge threshold.
Well-Tuned Submitted Published MiniCPM5-1B LoRA adapter, generated calibration splits, and 632-case hard eval with zero dangerous undercalls.
Off the Grid Submitted The Space runs the small open model directly through Transformers on ZeroGPU with no external LLM API; local GGUF/Transformers tooling is included.
Off Brand Submitted Custom Gradio UI beyond the stock component look.
Sharing is Caring Submitted Public dataset/eval bundle, model card, build log, Codex trace, and collection are linked from the Space.
Field Notes Submitted FIELD_NOTES.md documents product decisions, model/runtime pivots, eval results, and submission tradeoffs.
Article / Story Published Hugging Face article explains the product story, MiniCPM LoRA path, Modal evals, and demo: https://huggingface.co/blog/build-small-hackathon/jawbreaker-private-scam-defense
Best Demo Submitted Demo video, article, and social posts are published and linked from the Space README.
Bonus Quest Champion Submitted Jawbreaker stacks Well-Tuned, Off Brand, Off the Grid, Tiny Titan, Sharing is Caring, Field Notes, Best Demo evidence, and a published article.
Judges' Wildcard Automatic Every submission is considered.

Bonus badge evidence:

  • Well-Tuned: published MiniCPM5-1B LoRA adapter with guarded 632-case, 394-case, and 320-case eval reports.
  • Off Brand: custom gr.Server app shell instead of stock Gradio component layout.
  • Off the Grid: no external LLM API; inference uses the small open MiniCPM model and Jawbreaker LoRA loaded in the app runtime.
  • Tiny Titan: 1B runtime model with a narrow, safety-critical task.
  • Sharing is Caring: public dataset/eval bundle plus AGENT_TRACE.md and CODEX_BUILD_LOG.md.
  • Field Notes: FIELD_NOTES.md documents model/runtime pivots, eval decisions, and submission tradeoffs.
  • Best Demo: demo video, article, Reddit post, LinkedIn post, and X post are published and linked.

Not claiming:

  • Thousand Token Wood main track: Jawbreaker is entered as Backyard AI.
  • Best Agent: Jawbreaker is not a multi-step agentic app.
  • NVIDIA Nemotron Quest: no NeMoTron model is used.
  • Llama Champion / llama.cpp as live runtime: local/eval tooling supports GGUF experiments, but the judge-facing Space uses Transformers on ZeroGPU.

Limitations / Safety Boundary

Jawbreaker is not legal, financial, or cybersecurity advice. It is a local-first safety aid that helps non-experts slow down and verify suspicious messages. The safest action should never ask the user to click the suspicious link or call a number from the suspicious message.

FIELD_NOTES.md is a build-observation log: product decisions, model/runtime pivots, eval results, and packaging notes. It is not presented as ethnographic user research.