FIELD_NOTES.md

Field Notes

These are build field notes: product decisions, model/runtime pivots, eval observations, and packaging notes. They are not ethnographic user research or a claim of completed real-user validation.

2026-06-05

Kickoff confirmed the submission shape:

• Gradio Space under the hackathon organization.
• Demo video and social post proof belong in the Space README.
• Public GitHub repo with Codex-attributed commits matters for the OpenAI Codex Track.
• Sponsor prizes can depend on model or infrastructure choices.

Jawbreaker is scoped for Backyard AI: one real person, one narrow safety task, one clear answer.

Current risk: a generic scam detector will not stand out. The product must show a specific person being helped and a clear small-model fit.

Current differentiator: Scam DNA, a visual breakdown of scam structure rather than a plain label.

Built the first serious evaluation spine: 100 synthetic/sanitized scam, suspicious, needs-check, and safe messages. The eval explicitly tests false positives on legitimate-looking messages because user trust is central to the product.

2026-06-06

Runtime decision: deploy with Qwen/Qwen3-0.6B through Transformers on ZeroGPU.

Why:

• The initial CPU GGUF path was useful for local/eval tooling, but cold-start behavior was too risky for a judge-facing Space.
• A larger GGUF model was not viable for the live demo latency target.
• ZeroGPU kept the project on a public small model without relying on a commercial LLM API.

The product architecture became defense-in-depth:

• The model produces structured scam analysis.
• The parser rejects invalid JSON.
• The schema validator catches malformed fields.
• A deterministic heuristic guard prevents obvious high-risk scams from being rendered as safe when the small model under-calls danger.

The UI direction shifted from "developer demo" to "calm safety card":

• one primary action: check the message
• warm, readable card layout
• visible scam breakdown instead of chat
• copyable message for asking someone trusted
• session memory that auto-saves checked patterns

What was cut:

• Fine-tuning. The project does not have enough separate training data to fine-tune responsibly without overfitting.
• Multi-model switching UI. It adds confusion without helping the person the tool is built for.
• Additional sponsor-specific runtime pivots beyond OpenBMB. A stable Backyard AI submission matters more than chasing every possible badge.

Current remaining field-work need: collect or choose one realistic, sanitized scam story for the demo video. The story should show a person pasting a suspicious message, receiving one safe action, and copying the trusted-person handoff.

2026-06-06 OpenBMB Pivot

Kickoff made clear that MiniCPM needs to be central for OpenBMB award eligibility. Jawbreaker moved the deployed ZeroGPU default from Qwen/Qwen3-0.6B to openbmb/MiniCPM4.1-8B.

Why:

• MiniCPM is now the primary scam-analysis model, not a side mention.
• The model remains under the 32B limit and fits the small-model theme.
• The OpenBMB model family is explicitly positioned for efficient local and edge deployment.
• The heuristic guard remains in place so obvious scam danger is not under-called if the model output is weak or malformed.

Fallback plan: if Space latency or memory behavior is unacceptable, switch JAWBREAKER_TRANSFORMERS_MODEL_ID back to Qwen/Qwen3-0.6B and document the OpenBMB bakeoff result honestly.

2026-06-06 Training Spine

Jawbreaker now has a training path, but deployment remains eval-gated.

Added:

• deterministic generation of 720 train, 120 dev, and 180 test examples
• generated holdout eval in eval/generated_eval.jsonl
• PEFT/LoRA training scaffold for MiniCPM
• Transformers eval backend for direct MiniCPM scoring
• runtime fallback so malformed model JSON falls back to deterministic safety analysis

Deployment rule: publish or deploy a LoRA adapter only if it improves valid JSON and keeps dangerous-as-safe misses at zero. A worse-but-fine-tuned model should not ship.

2026-06-06 Submission Honesty

Latest Discord discussion surfaced MiniCPM4.1 Transformers issues around SDPA attention masks and gibberish output. Jawbreaker now defaults JAWBREAKER_ATTENTION_IMPLEMENTATION=eager for the MiniCPM path, and keeps fallback analysis visible in the documentation.

Guardrail: winning matters, but not by overclaiming. The project should not claim Well-Tuned, real user validation, Modal usage, or llama.cpp deployment unless those things are actually completed and documented.

2026-06-06 Modal Training Plan

Modal credits are the right place to run the MiniCPM LoRA job. Added training/modal_train.py so the same generated train/dev split can run on an A100 with outputs stored in a Modal volume.

Claim rule: Modal usage becomes a submission claim only after a real Modal run completes. Well-Tuned becomes a claim only after the resulting adapter is published and beats the base model or fallback on eval.

2026-06-06 Field Examples

Added two sanitized real-world scam examples from a friend:

• Coinbase account phone-number update callback lure
• TikTok Shop part-time assistant / WhatsApp job lure

Names, timestamps, and phone numbers were removed before committing. These examples are useful demo candidates because they are realistic, recent, and easier to explain than fully synthetic samples.

2026-06-06 MiniCPM LoRA v3

The v1 and v2 LoRA passes proved that fine-tuning could improve JSON reliability, but v2 still had an unacceptable pattern on the hard eval: dangerous scams sometimes became needs_check.

The v3 pass focused on contrastive boundary sharpening:

• dangerous package, bank, tech-support, prize, job, family, and marketplace messages
• legitimate notices that should remain needs_check
• benign but scary-looking messages that should remain safe
• sanitized real-world-inspired Coinbase callback and TikTok Shop recruiter patterns without private phone numbers or chat metadata

Modal A100 training completed and published build-small-hackathon/jawbreaker-minicpm-lora-v3.

Eval decision:

• v2 hard raw: 167/215 risk accuracy (77.7%)
• v2 hard guarded: 183/215 risk accuracy (85.1%)
• v3 hard raw: 210/215 risk accuracy (97.7%)
• v3 hard raw had 0 dangerous-as-safe, 0 dangerous-as-needs-check, 0 safe-as-dangerous-or-suspicious, 0 unsafe action violations, 0 invalid predictions, and 0 model errors

Decision: ship v3 as the default MiniCPM adapter while keeping the deterministic guard as product safety defense-in-depth.

2026-06-07 MiniCPM5-1B Promotion

Jawbreaker promoted the Tiny Titan experiment to the production path.

Final deployed model path:

• Base: openbmb/MiniCPM5-1B
• Adapter: build-small-hackathon/jawbreaker-minicpm5-1b-lora-v4
• Runtime: Hugging Face Transformers on ZeroGPU
• Training/eval infrastructure: Modal

Why this replaced the earlier 8B v3 adapter:

• The 1B model fits the Tiny Titan spirit and the OpenBMB sponsor path.
• The v4 adapter cleared the safety bar on the completed 394-case hard guarded eval.
• It had 0 dangerous-as-safe, 0 dangerous-as-needs-check, 0 suspicious-as-safe, 0 unsafe action violations, 0 invalid predictions, and 0 model errors.
• It beat the 8B v3 adapter on the hard guarded evals while being much smaller.

The 470-case eval attempt timed out around case 341, so it is not used as final evidence. The committed evidence from this phase is the completed 320-case and 394-case guarded reports under eval/reports/.

Decision at this point: ship MiniCPM5-1B LoRA v4 as the default model, keep the deterministic guard as product safety defense-in-depth, and document the 8B path as comparison/history rather than the live model. This was later superseded by the v8 calibration path.

2026-06-07 Hub Packaging

Published the public review artifacts:

• First strong 1B model card: build-small-hackathon/jawbreaker-minicpm5-1b-lora-v4
• Public dataset/eval bundle: build-small-hackathon/jawbreaker-scam-defense-data
• Live app: build-small-hackathon/jawbreaker
• Hub collection: build-small-hackathon/jawbreaker-6a263632dcd0b6d41ca914ff

The dataset bundle includes synthetic/sanitized training and eval JSONL files plus the completed 320-case and 394-case guarded eval reports. It does not include raw private chats, Discord logs, emails, phone numbers, timestamps, or personal message metadata.

The collection linked the live Space, MiniCPM5-1B LoRA v4 adapter, and public dataset/eval bundle so judges could inspect the full submission package from one Hub page. The collection was later updated to place the v8 adapter first.

2026-06-09 MiniCPM5-1B LoRA v8

Fresh public-pattern evals exposed a harder boundary than the v4 suite:

• wrong-number crypto, gold, and trading grooming could look conversational before the investment ask
• some benign family, school, pharmacy, and logistics messages were easy to over-call if the guard was too aggressive

The v8 calibration path added fresh synthetic/sanitized data for those patterns and re-ran the larger hard suite on Modal.

Final judged model path:

• Base: openbmb/MiniCPM5-1B
• Adapter: build-small-hackathon/jawbreaker-minicpm5-1b-lora-v8
• Runtime: Hugging Face Transformers on ZeroGPU
• Training/eval infrastructure: Modal A100

Final guarded eval evidence:

• 632 cases
• 579/632 risk accuracy (91.61%)
• 0 dangerous-as-safe
• 0 dangerous-as-needs-check
• 0 safe-as-dangerous-or-suspicious
• 0 unsafe action violations
• 0 invalid predictions
• 0 model errors

Decision: ship MiniCPM5-1B LoRA v8 as the default model because it clears the broader safety gate and preserves the Tiny Titan/OpenBMB story.

2026-06-09 Off the Grid Claim

The field guide says Off the Grid is about no cloud APIs: "The whole thing runs on the model in front of you." Jawbreaker meets that badge:

• the Space loads openbmb/MiniCPM5-1B and the Jawbreaker LoRA adapter directly through Transformers
• inference happens in the app runtime on Hugging Face ZeroGPU
• there is no OpenAI, Anthropic, hosted MiniCPM, or other external LLM API call in the scam-analysis path
• local/eval tooling also supports GGUF experiments through llama-cpp-python

Precise wording matters: claim Off the Grid confidently as small open model inference inside the app runtime with no external LLM API. Hugging Face ZeroGPU is the judge-facing compute layer, not a hosted model dependency.

2026-06-09 Backyard AI Story

The motivating user story is a friend's grandmother who had already been affected by scam messages. Public docs should keep this privacy-preserving:

• no names
• no phone numbers
• no timestamps
• no raw private chat metadata
• no claim of broad user validation without a direct quote or recorded reaction

This is enough to make the Backyard AI framing concrete: Jawbreaker is for a real family safety workflow, not a generic spam-classification benchmark. The public story should say that the product was shaped around helping someone like her pause before replying, clicking, calling, or paying.

If we later get a quote or demo permission, add it to the article and demo script. Until then, keep the claim as motivation and intended use, not measured user validation.