Saya akan cek halaman org/hackathon HF itu dulu dan juga cek kemampuan akses HF token secara aman tanpa mencetak token. Setelah itu saya bisa mulai tahap pertama: model repo public untuk adapter, bukan Space/hardware dulu.


Paham. Saya sudah fetch halaman hackathon org `build-small-hackathon`.

**Rule Kunci Lomba**
- App harus **Gradio app** dan di-host sebagai **Hugging Face Space** di org itu.
- Model constraint: **≤32B parameters**. NeuroBait Gemma 4 26B-A4B masih masuk.
- Submission perlu **Space link, short demo video, social post** sebelum deadline.
- Track yang paling cocok: **Backyard AI**, karena problem spesifik dan real.
- Bonus yang relevan:
  - **Well-Tuned**: model fine-tuned dipublish di HF.
  - **Off-Brand**: custom UI beyond default Gradio.
  - **Sharing is Caring**: agent trace di Hub.
  - **Field Notes**: blog/report behind-the-build.

Sumber halaman org: https://huggingface.co/build-small-hackathon

**Nama yang Dikunci**
- HF model repo: `build-small-hackathon/NeuroBait-AI-ADHD-Assistant`
- HF Space repo: `build-small-hackathon/NeuroBait-AI-ADHD-Assistant-Demo`

**Feasibility**
- Stage 1, upload adapter LoRA ke HF model repo: feasible sekarang.
- Stage 2, HF Space Gradio: feasible, tapi hardware perlu diputuskan dengan trial.
- Direct full bf16 26B likely butuh 80GB+ VRAM. HF L40S 48GB kemungkinan tidak cukup.
- ZeroGPU `xlarge` 96GB secara teoritis feasible untuk 26B, tapi ada queue/compatibility constraints. Kita perlu test langsung di Space.
- Alternatif jika HF Space gagal load full model: quantized/4-bit inference path atau GGUF/llama.cpp path. Ini juga membuka bonus `Llama Champion`, tapi butuh kerja tambahan.

**Design Direction**
Saya akan adapt dari research/mockup:
- English-first.
- Calm earthy palette, low sensory.
- No shame, no streak pressure, no leaderboard.
- Custom UI, bukan default ChatInterface polos.
- Accessibility controls: Calm/Dark/High Contrast/Reduce Motion.
- Main screen langsung chat, bukan landing page.
- Copy should frame it as task-initiation support, not medical treatment.

**Submission Context**
Betul, README root nanti harus jadi “submission anchor”: kategori, story, why small model, research-design, eval results, Codex/Modal/HF flow. Story behind-nya bisa kamu berikan terpisah nanti, lalu saya jahit ke README/model card/Space card.

**Tahap Lanjut Yang Aman**
1. Create/push **public adapter model repo**.
2. Add proper model card draft.
3. Create Space repo scaffold with custom Gradio UI.
4. Try hardware/load path.
5. Only after Space works, polish README submission story.

Saya belum membuat repo HF atau deploy Space pada langkah ini.


holla. saya suka dengna gata to the point dan eksekusi langsung, tapi saya juga suka dengqn akurasi antara eksekusi dan clarify ekspektasi antara kamu sebagai agent dan saya sebagai developer dan reviewer. mulai sekarang kamu adalah agent untuk projek neurobait small model ini. nantinya kamu perlu mengcopy file2 yang dibutuhkan. penyesuaian dengan fine tuning via modal.com dan deployment ke gradio serta consider research-design sesuai persona projek ini. silahkan start with understand this projek. dalam lomba kali ini memang direkomendasikan pakai agent codex agar saat commit dan push ada tag codex di HF maupun di Github.

betul. tapi struktur folder bukankah sebaiknya mengikuti github-hf.svg? consider repo github sendiri, repo hf sendiri, dan deploy di space hf sendiri kan? bagaimana?

ok setuju, coba kamu susun sekarang. nanti akan saya review modularitas dan readibilitynya

wait coba cek di ai-adhd-small-model-hf-2026 apakah ada virtual env python yang sempat digunakan?

oh ok, better kita buat venv sendiri disini atau bagaimana? jika dibutuhkan hanya untuk develop, kamu bisa pakai venv lain yang tersedia disini

ya kamu bisa pakai /media/haris-subrata/Work/llm/agent_analytics/venv/bin/python dan install sesuai yang kamu butuhkan, itu sudah dikhususkan untuk llm development

apakah README.md ini untuk local kita atau sudah sesuai konteks untuk deployment? just ask

oh okkk good. nanti mungkin tetap perlu ada konteks isi projek ini diluar engineering, tapi its ok nanti saja. saya cek dulu strukturnya

ok good. ini folder data masih kosong, kenapa ya? bisa dilanjutkans scafold yang lainnya

github-hf.svg dan HANDOFF.md dimasukkan ke folder local-dev saja, lalu buatkan log_dev.md juga didalamnya untuk trace development kita, agar kamu atau agent berikutnya tinggal baca dari situ apa yang harus dilakukan. folder local-dev tidak perlu dipush di github. btw github repo sudah saya buat https://github.com/Subrata15/NeuroBait-Build-Small-Model git workaround sudah ada disini, jadi harusnya kamu bisa langsung lakukan operasi git. repo tersebut sudah saya buat dengan tanpa readme dan gitignore

ok, saya cek di repo kok tidak ada codex as contributor ya? just ask. kalau claude biasanya otomatis ada

ok untuk next saja berarti. ok then next sepertinya kita perlu retrain ya? tapi modal.com punya cara yang unik dalam menggunakan gpu mereka, berbeda dengan runpod. coba kamu cek, atau menurut kamu ada yang mau dikerjakan dulu sebelum itu?

ok good. bisakah kamu coba dulu koneksi ke modal? misalnya cek gpu atau jalankan gpu by dummy komputasi untuk memastikan? ini seperti sandbox. lalu berikan reportnya disini hasil percobaannya

betul, tapi saya lupa memangnya saya sudah memberikan akun modal saya?

modal token new ini maksudnya gimana? dimana saya taruh tokennya?

ok sudah saya lakukan dan sukses, saya baru tahu ada modal clinya. nanti dokumentasikan ya agar saya bisa eksplre sendiri juga

oh butuh torch dan torch belum ada ya? coba cek di source ~/environment/py10/bin/activate apakah ada? dan feasible dipakai?

oh ok berarti pakai yang ada saja. notes dari saya jika akan install dependencies besar, pastikan makan storage di storage ini, karena home atau root tinggal beberapa GB saja

ok, lalu di modal credit dipakai itu per apa ya? bagaimana ketika sedang idle begini apakah dihitung?

ok good to go. apakah sekarang sudah siap dan ready untuk retrain? semua notes dan parameter train sebelumnya dipakai lagi kan? karena yang beda sekarang hanyalah dataset saja harusnya? konfirmasi kepahaman kamu

oh secret huggingface harus ada? kenapa? bukankah itu untuk deployment? atau ini agar bisa langsung taruh adapter disana? berarti harus targetting org di HF sesuai lomba ini ya>?

ok, nanti saya infokan nama repo di HF dan orgnya. berarti saya akna berikan hf tokennya? saya ambil dimana?

ok sudah saya lakukan update tokennya

jangan lupa nanti dokumentasikan retrain apa saja, download adapter di local. setelah retrain selesai langsung evaluasi model. lalu saya ingin ada evalusi tambahan untuk model ini dengan approach yang umum digunakan untuk ini, jadi setelah eval yang exist ini akan extend eval kedua.

ok jalankan retrain sekarang

Base leak Micro-action: pada app-continuation EN maksudnya di eval EN masih ada micro action yang langsung muncul kah? atau bagaimana?

menarik, nah sekarang bisakah kita langsung deploy ke hf space + gradio dan repo hf? nama repo bisa NeuroBait-AI-ADHD-Assistant atau semacamnya bisa rekomendasikan nama. btw untuk design kamu bisa reference ke hasil research saya di /media/haris-subrata/Work/llm/ai-adhd-small-model-hf-2026/research-design dan contoh mockup di /media/haris-subrata/Work/llm/ai-adhd-small-model-hf-2026/mockup/mockup_v5_research_informed.html (ini contoh dalam bahasa indonesia, depoy priotity bahasa inggris. jangan langsung deploy, konfirmasi kepahaman dan feasibility kondisi sekarang

saya setuju dengan nama app dan reponya. saya setuju bertahap juga, untuk hardware kamu cek saja feasibilitynya langsung ketimbang dari referensi. lalu untuk or ini untuk lomba ya, dan README.md nanti jadi kuncian kategori yang saya submit, dan story behindnya. story sedang saya susun, akan saya beri tahu kamu nanti. sepertinya konteks submission memang harus diberikan terpisah. jadi better betul bertahap saja. nama orgnya: https://huggingface.co/build-small-hackathon repo tentu public karena wajib show. untuk informasi lombanya juga kamu bisa fetch di link tadi


<permissions instructions>
Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `workspace-write`: The sandbox permits reading files, and editing files in `cwd` and `writable_roots`. Editing files in other directories requires approval. Network access is restricted.
# Escalation Requests

Commands are run outside the sandbox if they are approved by the user, or match an existing rule that allows it to run unrestricted. The command string is split into independent command segments at shell control operators, including but not limited to:

- Pipes: |
- Logical operators: &&, ||
- Command separators: ;
- Subshell boundaries: (...), $(...)

Each resulting segment is evaluated independently for sandbox restrictions and approval requirements.

Example:

git pull | tee output.txt

This is treated as two command segments:

["git", "pull"]

["tee", "output.txt"]

Commands that use more advanced shell features like redirection (>, >>, <), substitutions ($(...), ...), environment variables (FOO=bar), or wildcard patterns (*, ?) will not be evaluated against rules, to limit the scope of what an approved rule allows.

## How to request escalation

IMPORTANT: To request approval to execute a command that will require escalated privileges:

- Provide the `sandbox_permissions` parameter with the value `"require_escalated"`
- Include a short question asking the user if they want to allow the action in `justification` parameter. e.g. "Do you want to download and install dependencies for this project?"
- Optionally suggest a `prefix_rule` - this will be shown to the user with an option to persist the rule approval for future sessions.

If you run a command that is important to solving the user's query, but it fails because of sandboxing or with a likely sandbox-related network error (for example DNS/host resolution, registry/index access, or dependency download failure), rerun the command with "require_escalated". ALWAYS proceed to use the `justification` parameter - do not message the user before requesting approval for the command.

## When to request escalation

While commands are running inside the sandbox, here are some scenarios that will require escalation outside the sandbox:

- You need to run a command that writes to a directory that requires it (e.g. running tests that write to /var)
- You need to run a GUI app (e.g., open/xdg-open/osascript) to open browsers or files.
- If you run a command that is important to solving the user's query, but it fails because of sandboxing or with a likely sandbox-related network error (for example DNS/host resolution, registry/index access, or dependency download failure), rerun the command with `require_escalated`. ALWAYS proceed to use the `sandbox_permissions` and `justification` parameters. do not message the user before requesting approval for the command.
- You are about to take a potentially destructive action such as an `rm` or `git reset` that the user did not explicitly ask for.
- Be judicious with escalating, but if completing the user's request requires it, you should do so - don't try and circumvent approvals by using other tools.

## prefix_rule guidance

When choosing a `prefix_rule`, request one that will allow you to fulfill similar requests from the user in the future without re-requesting escalation. It should be categorical and reasonably scoped to similar capabilities. You should rarely pass the entire command into `prefix_rule`.

### Banned prefix_rules
Avoid requesting overly broad prefixes that the user would be ill-advised to approve. For example, do not request ["python3"], ["python", "-"], or other similar prefixes that would allow arbitrary scripting.
NEVER provide a prefix_rule argument for destructive commands like rm.
NEVER provide a prefix_rule if your command uses a heredoc or herestring.

### Examples
Good examples of prefixes:
- ["npm", "run", "dev"]
- ["gh", "pr", "check"]
- ["cargo", "test"]

## Approved command prefixes
The following prefix rules have already been approved: - ["./run.sh"]
- ["./start.sh"]
- ["set", "+a"]
- ["set", "-a"]
- ["curl", "-s"]
- ["git", "add"]
- ["bq", "query"]
- ["nohup", "env"]
- ["source", ".env"]
- ["node", "server.js"]
- ["./wmill_sync.sh", "pull"]
- ["./.venvs/timeseries0/bin/pip", "install"]
- ["../../.venvs/timeseries0/bin/python", "-u"]
- ["source", "~/environment/py09/bin/activate"]
- ["source", "~/environment/py10/bin/activate"]
- ["python", "inspection/08_regression5_dec_train_nov_backtest.py"]
- ["python", "regression-best/handle_high_error_0/src/run_hhe_0.py"]
- ["python", "regression-best/handle_high_error_1/src/run_hhe_1.py"]
- ["python", "regression-best/handle_high_error_2/src/run_hhe_2.py"]
- ["python", "regression-best/handle_high_error_3/src/run_hhe_3.py"]
- ["python", "inspection/01_regression5_train_predict_inspection.py"]
- ["python", "regression-best/handle_high_error_2m/src/run_hhe_2m.py"]
- ["python", "inspection/07_regression5_lag_feature_candidate_inspection.py"]
- ["python", "inspection/06_regression5_current_feature_signal_inspection.py"]
- ["node", "/home/haris-subrata/.codex/skills/.system/openai-docs/scripts/fetch-codex-manual.mjs"]
- ["git", "commit", "-m"]
- ["git", "push", "origin"]
- ["ssh", "-T", "git@github.com"]
- ["mkdir", "-p", "../ratio-modif-0"]
- ["rm", "-f", "neurobait-lora-run3"]
- ["rm", "-f", "tmp_filter_small_rois.py"]
- ["rm", "-f", "/tmp/reterpot_demo.cookies"]
- ["rm", "-f", "/tmp/reterpot_guest.cookies"]
- ["rm", "-f", "/tmp/git-askpass-kontinum.sh"]
- ["python", "-u", "output/06_run_moe1_sql.py"]
- ["pgrep", "-f", "03_timeseries0_train_predict.py"]
- ["pgrep", "-af", "03_timeseries0_train_predict.py"]
- ["python", "-u", "01_ratio_modif0_rolling_median.py"]
- ["python", "-u", "src/01_regression5_predict_1_nov.py"]
- ["python", "-u", "analysis/03_create_table_4_0_moe1.py"]
- ["python", "-u", "estimation/02_run_moe1_estimation.py"]
- ["python", "-u", "src/02_regression5_predict_1_nov_fair.py"]
- ["/bin/bash", "-lc", "source ~/environment/py09/bin/activate"]
- ["/bin/bash", "-lc", "source ~/environment/py10/bin/activate"]
- ["python", "-u", "preparation/01_prepare_moe1_feature_store.py"]
- ["python", "-u", "../mixture-of-experts-0/output/06_run_moe0_sql.py"]
- ["rm", "-rf", "/media/haris-subrata/Work/Download/teachablemachine-0"]
- ["mkdir", "-p", "/media/haris-subrata/Work/Download/teachablemachine-0"]
- ["mkdir", "-p", "../../../notebooks/03_analysis/py/ratio_modif0_rollmed"]
- ["python", "-u", "../mixture-of-experts-0/analysis/03_create_table_4_0_moe0.py"]
- ["python", "-u", "../mixture-of-experts-0/estimation/02_run_moe0_estimation.py"]
- ["python", "-u", "../mixture-of-experts-0/preparation/01_prepare_moe0_feature_store.py"]
- ["mkdir", "-p", "../mixture-of-experts-0/{preparation,estimation,analysis,output,sql,models,logs}"]
- ["/bin/bash", "-lc", "cp .tmp_sagri_concrete_scaffold/log_dev.md ../Sagri-Concrete-2026/log_dev.md"]
- ["/bin/bash", "-lc", "GIT_ASKPASS=/tmp/git-askpass-kontinum.sh GIT_TERMINAL_PROMPT=0 git push origin main"]
- ["python", "-u", "../../../notebooks/03_analysis/py/ratio_modif0_rollmed/04_run_ratio_modif0_rollmed_sql.py"]
- ["/bin/bash", "-lc", "source ~/environment/py10/bin/activate && python -u pipeline/run_inference.py --device cuda"]
- ["python", "-u", "../../../notebooks/03_analysis/py/ratio_modif0_rollmed/01_create_table_4_0_ratio_modif0_rollmed.py"]
- ["/bin/bash", "-c", "set -a; source ./.env; set +a; curl -s \"$CHATERY_API_URL/api/whatsapp/sessions\" -H \"X-Api-Key: $CHATERY_API_KEY\""]
- ["/bin/bash", "-lc", "source ~/environment/py09/bin/activate && python ../Sagri-Concrete-2026/backend/app/pipelines/ingest_actuals.py"]
- ["/bin/bash", "-lc", "set -a; source ./.env; set +a; curl -s \"$CHATERY_API_URL/api/whatsapp/sessions\" -H \"X-Api-Key: $CHATERY_API_KEY\""]
- ["python", "-c", "from pathlib import Path; p=Path('01_ratio_modif0_rolling_median.py'); compile(p.read_text(), str(p), 'exec'); print('python-ok')"]
- ["/bin/bash", "-lc", "cp .tmp_sagri_concrete_scaffold/backend/app/pipelines/ingest_actuals.py ../Sagri-Concrete-2026/backend/app/pipelines/ingest_actuals.py"]
- ["/bin/bash", "-lc", "source ~/environment/py10/bin/activate && python -u train.py --stage 2 --data data/labeled/stage2_manual_aug_v4/dataset.yaml --device cuda"]
- ["pkill", "-f", "python train.py --stage 2 --data data/labeled/stage2_manual_aug_v3/dataset.yaml --model-size m --epochs 120 --imgsz 640 --batch 2 --device cuda"]
- ["python", "-c", "from pathlib import Path; p=Path('../mixture-of-experts-0/output/06_run_moe0_sql.py'); compile(p.read_text(), str(p), 'exec'); print('out-ok')"]
- ["/bin/bash", "-lc", "pgrep -af \"03_timeseries0_train_predict.py\" && ps -p $(pgrep -f \"03_timeseries0_train_predict.py\" | head -n1) -o pid,etime,pcpu,pmem,stat,cmd"]
- ["/bin/bash", "-lc", "source ~/environment/py09/bin/activate && python -u ../../../notebooks/03_analysis/py/ratio_modif0_rollmed/04_run_ratio_modif0_rollmed_sql.py"]
- ["/bin/bash", "-lc", "set -a; source ./.env; set +a; curl -s --max-time 20 ...
[Some commands were truncated]
 The writable roots are `/media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model`, `/tmp`.
</permissions instructions>

<collaboration_mode># Collaboration Mode: Default

You are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active.

Your active mode changes only when new developer instructions with a different `<collaboration_mode>...</collaboration_mode>` change it; user requests or tool descriptions do not change mode by themselves. Known mode names are Default and Plan.

## request_user_input availability

Use the `request_user_input` tool only when it is listed in the available tools for this turn.

In Default mode, strongly prefer making reasonable assumptions and executing the user's request rather than stopping to ask questions. If you absolutely must ask a question because the answer cannot be discovered from local context and a reasonable assumption would be risky, ask the user directly with a concise plain-text question. Never write a multiple choice question as a textual assistant message.
</collaboration_mode>

<skills_instructions>
## Skills
A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.
### Available skills
- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /home/haris-subrata/.codex/skills/.system/imagegen/SKILL.md)
- openai-docs: Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains. (file: /home/haris-subrata/.codex/skills/.system/openai-docs/SKILL.md)
- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: /home/haris-subrata/.codex/skills/.system/plugin-creator/SKILL.md)
- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/haris-subrata/.codex/skills/.system/skill-creator/SKILL.md)
- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/haris-subrata/.codex/skills/.system/skill-installer/SKILL.md)
### How to use skills
- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.
- How to use a skill (progressive disclosure):
  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
  2) When `SKILL.md` references relative paths (e.g., `scripts/foo.py`), resolve them relative to the skill directory listed above first, and only consider other paths if needed.
  3) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  4) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  5) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Coordination and sequencing:
  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.
  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.
</skills_instructions>


<environment_context>
  <cwd>/media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model</cwd>
  <shell>bash</shell>
  <current_date>2026-06-08</current_date>
  <timezone>Asia/Jakarta</timezone>
  <filesystem><workspace_roots><root>/media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model</root></workspace_roots><permission_profile type="managed"><file_system type="restricted"><entry access="read"><special>:root</special></entry><entry access="write"><path>/media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model</path></entry><entry access="write"><special>:slash_tmp</special></entry><entry access="write"><special>:tmpdir</special></entry><entry access="read"><path>/media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/.git</path></entry><entry access="read"><path>/media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/.agents</path></entry><entry access="read"><path>/media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/.codex</path></entry></file_system></permission_profile></filesystem>
</environment_context>


# Collaboration Mode: Default

You are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active.

Your active mode changes only when new developer instructions with a different `<collaboration_mode>...</collaboration_mode>` change it; user requests or tool descriptions do not change mode by themselves. Known mode names are Default and Plan.

## request_user_input availability

Use the `request_user_input` tool only when it is listed in the available tools for this turn.

In Default mode, strongly prefer making reasonable assumptions and executing the user's request rather than stopping to ask questions. If you absolutely must ask a question because the answer cannot be discovered from local context and a reasonable assumption would be risky, ask the user directly with a concise plain-text question. Never write a multiple choice question as a textual assistant message.

Summary: auto


ok tahap berikutnya 1 sampai 4 poin yang kamu mention diatas. btw nanti dari nama repo, kira2 url spacenya apa? ini contoh punya orang lain https://huggingface.co/spaces/build-small-hackathon/Tone-Bridge https://huggingface.co/spaces/build-small-hackathon/Advisor saya khawatir nama repo yang terlalu panjang jadi tidak relevan, namun jika nama repo dan url app akan beda its ok


Saya akan lanjut bertahap: cek struktur deploy/model-card yang ada dulu, lalu saya rapikan nama repo/Space supaya URL tidak terlalu panjang sebelum menyentuh upload publik. Untuk URL, pola Space HF adalah `https://huggingface.co/spaces/{org}/{space-name}`, jadi kalau kita pakai `NeuroBait-AI-ADHD-Assistant-Demo` URL-nya akan sepanjang itu.


Audit current deploy, model-card, and HF upload scripts

Choose shorter HF repo/Space naming and update docs/config

Prepare model repo upload flow and Space app scaffold


Output: Plan updated


git --git-dir=.git-local --work-tree=. status --short --branch


Output:
Chunk ID: 381e68
Wall time: 0.0000 seconds
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Output:
deploy/app.py
deploy/requirements.txt
deploy/README.md
model-card/README.md
README.md
docs/evaluation_plan.md
docs/product_brief.md
docs/modal_cli.md
docs/retrain_runbook.md
train/modal_train.py
train/train_core.py
eval/modal_generation_eval.py
eval/make_report.py
eval/eval_neurobait.py
scripts/validate_dataset.py


Output:
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Output:
## main...origin/main


Output:
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Output:
"""Hugging Face Space app for NeuroBait."""

from __future__ import annotations

import os

import gradio as gr
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = os.environ.get("MODEL_ID", "USER/neurobait-gemma4-26b-a4b")
SYSTEM_PROMPT = """Kamu adalah NeuroBait — asisten AI untuk orang dengan ADHD dan neurodivergent. Tugasmu bukan membuat to-do list. Tugasmu menyalakan dopamin untuk memicu task initiation.

Dari setiap percakapan, identifikasi dua elemen kunci: (1) deadline anchor — momen nyata atau buatan yang bisa jadi batas waktu relevan; dan (2) object/subject motivator — orang atau hal yang paling emosional signifikan bagi user saat ini. Gunakan keduanya sebagai bahan bakar Resep Engagement yang personal, bukan generik.

Setiap Resep Engagement memuat empat elemen berurut natural: validasi hangat singkat tanpa menghakimi → hook yang membangkitkan rasa flow dari minat atau pengalaman user → stakes berbasis deadline atau motivator nyata → satu micro-action super kecil dan spesifik yang bisa langsung dilakukan.

Kalau konteks user belum cukup untuk membuat resep yang personal, ajukan tepat satu pertanyaan ringan yang paling berguna — tentang deadline atau motivator. Kalau konteks sudah ada, langsung berikan resep.

Framing selalu menempatkan user sebagai pelaku aktif dengan agency penuh. Bukan guilt, bukan hutang — selalu agency. Kalimat pendek. Bahasa hidup. Hangat dan padat. Tidak pernah menghakimi. Tidak pernah ceramah. Membuat hal membosankan jadi tak tertahankan."""

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

def _mm(messages: list[dict]) -> list[dict]:
    """Gemma 4 / transformers 5.5 content format normalization."""

    normalized = []
    for message in messages:
        content = message["content"]
        if isinstance(content, str):
            content = [{"type": "text", "text": content}]
        normalized.append({"role": message["role"], "content": content})
    return normalized

def _history_to_messages(history: list) -> list[dict]:
    messages = []
    for item in history:
        if isinstance(item, dict):
            role = item.get("role")
            content = item.get("content")
            if role in {"user", "assistant"} and content:
                messages.append({"role": role, "content": content})
            continue
        if isinstance(item, (tuple, list)) and len(item) == 2:
            user_text, assistant_text = item
            if user_text:
                messages.append({"role": "user", "content": user_text})
            if assistant_text:
                messages.append({"role": "assistant", "content": assistant_text})
    return messages

@spaces.GPU
def chat(message: str, history: list) -> str:
    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
    messages.extend(_history_to_messages(history))
    messages.append({"role": "user", "content": message})

    input_ids = tokenizer.apply_chat_template(
        _mm(messages),
        add_generation_prompt=True,
        return_tensors="pt",
    ).to(model.device)
    output_ids = model.generate(
        input_ids,
        max_new_tokens=400,
        do_sample=True,
        temperature=0.7,
        top_p=0.9,
        repetition_penalty=1.1,
    )
    return tokenizer.decode(output_ids[0][input_ids.shape[-1] :], skip_special_tokens=True).strip()

demo = gr.ChatInterface(
    fn=chat,
    title="NeuroBait",
    description="Asisten task-initiation untuk otak ADHD dan neurodivergent.",
)

if __name__ == "__main__":
    demo.launch()


Output:
Chunk ID: 41c11a
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Output:
"""Modal entrypoints for NeuroBait training, evaluation, merge, and upload."""

from __future__ import annotations

import modal

APP_NAME = "neurobait-small-model"
DEFAULT_LORA_REPO = "USER/neurobait-gemma4-26b-a4b-lora"
DEFAULT_MERGED_REPO = "USER/neurobait-gemma4-26b-a4b"

app = modal.App(APP_NAME)

image = (
    modal.Image.debian_slim(python_version="3.11")
    .pip_install("unsloth", "unsloth_zoo")
    .pip_install(
        "trl==0.24",
        "peft==0.19.1",
        "transformers==5.5.0",
        "datasets",
        "accelerate",
        "bitsandbytes",
        "huggingface_hub",
        extra_options="--no-deps",
    )
    .add_local_file("train/train_core.py", "/root/neurobait/train_core.py")
    .add_local_file("data/train.jsonl", "/data/train.jsonl")
    .add_local_file("data/eval.jsonl", "/data/eval.jsonl")
)

hf_cache = modal.Volume.from_name("hf-cache", create_if_missing=True)
out_vol = modal.Volume.from_name("neurobait-out", create_if_missing=True)

@app.function(
    image=image,
    gpu="H100",
    timeout=10 * 60,
    volumes={"/root/.cache/huggingface": hf_cache, "/out": out_vol},
    secrets=[modal.Secret.from_name("huggingface")],
)
def preflight() -> str:
    """Check the remote Modal runtime before spending a full training run."""

    import json
    import os
    import subprocess

    import unsloth
    import torch
    import datasets
    import peft
    import transformers
    import trl

    nvidia_smi = subprocess.run(
        ["nvidia-smi"],
        check=False,
        capture_output=True,
        text=True,
    )

    gpu = {}
    if torch.cuda.is_available():
        capability = torch.cuda.get_device_capability()
        gpu = {
            "name": torch.cuda.get_device_name(0),
            "capability": f"sm_{capability[0]}{capability[1]}",
            "device_count": torch.cuda.device_count(),
        }

    result = {
        "hf_token_present": bool(os.environ.get("HF_TOKEN")),
        "torch": torch.__version__,
        "cuda": torch.version.cuda,
        "cuda_available": torch.cuda.is_available(),
        "gpu": gpu,
        "transformers": transformers.__version__,
        "trl": trl.__version__,
        "peft": peft.__version__,
        "datasets": datasets.__version__,
        "unsloth": unsloth.__version__,
        "nvidia_smi": nvidia_smi.stdout[-4000:],
        "nvidia_smi_stderr": nvidia_smi.stderr[-1000:],
    }
    return json.dumps(result, indent=2)

@app.function(
    image=image,
    gpu="H100",
    timeout=60 * 60,
    volumes={"/root/.cache/huggingface": hf_cache, "/out": out_vol},
    secrets=[modal.Secret.from_name("huggingface")],
)
def train() -> dict:
    import os
    import sys

    sys.path.insert(0, "/root/neurobait")
    os.environ["HF_HOME"] = "/root/.cache/huggingface"
    os.environ.setdefault("TRAIN_FILE", "/data/train.jsonl")
    os.environ.setdefault("EVAL_FILE", "/data/eval.jsonl")
    os.environ.setdefault("OUT_ADAPTER", "/out/neurobait-lora-run3")
    os.environ.setdefault("OUT_DIR", "/out/outputs")

    from train_core import train_adapter

    summary = train_adapter()
    out_vol.commit()
    return summary

@app.function(
    image=image,
    timeout=30 * 60,
    volumes={"/out": out_vol},
    secrets=[modal.Secret.from_name("huggingface")],
)
def push_lora_to_hub(repo_id: str = DEFAULT_LORA_REPO) -> str:
    from huggingface_hub import HfApi

    HfApi().upload_folder(
        folder_path="/out/neurobait-lora-run3",
        repo_id=repo_id,
        repo_type="model",
    )
    return repo_id

@app.function(
    image=image,
    gpu="H100",
    timeout=60 * 60,
    volumes={"/root/.cache/huggingface": hf_cache, "/out": out_vol},
    secrets=[modal.Secret.from_name("huggingface")],
)
def merge_and_push(repo_id: str = DEFAULT_MERGED_REPO) -> str:
    import os

    os.environ["HF_HOME"] = "/root/.cache/huggingface"

    from unsloth import FastModel
    from unsloth.chat_templates import get_chat_template
    from huggingface_hub import HfApi

    base_model = os.environ.get("BASE_MODEL", "unsloth/gemma-4-26b-a4b-it")
    adapter_path = os.environ.get("OUT_ADAPTER", "/out/neurobait-lora-run3")
    merged_path = "/out/merged-neurobait-gemma4-26b-a4b"

    model, tokenizer = FastModel.from_pretrained(
        model_name=adapter_path,
        max_seq_length=2048,
        load_in_4bit=False,
        load_in_16bit=True,
        full_finetuning=False,
    )
    tokenizer = get_chat_template(tokenizer, chat_template="gemma-4")
    model.save_pretrained_merged(merged_path, tokenizer, save_method="merged_16bit")

    HfApi().upload_folder(folder_path=merged_path, repo_id=repo_id, repo_type="model")
    out_vol.commit()
    return repo_id

@app.local_entrypoint()
def main(
    run_preflight: bool = False,
    run_train: bool = True,
    push_lora: bool = False,
    merge: bool = False,
    lora_repo: str = DEFAULT_LORA_REPO,
    merged_repo: str = DEFAULT_MERGED_REPO,
) -> None:
    if run_preflight:
        print("preflight:", preflight.remote())
    if run_train:
        summary = train.remote()
        print("train:", summary)
    if push_lora:
        print("lora repo:", push_lora_to_hub.remote(lora_repo))
    if merge:
        print("merged repo:", merge_and_push.remote(merged_repo))


Saya lihat deploy saat ini masih asumsi “merged model” dan nama default placeholder. Untuk lomba, lebih rapi kalau adapter repo dan Space punya slug pendek, sementara judul UI tetap “NeuroBait AI ADHD Assistant”.


Output:
Chunk ID: 1191a4
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 416
Output:
# NeuroBait Gemma 4 26B-A4B

Draft model card for the Hugging Face Model repo.

## Model Summary

NeuroBait is a LoRA fine-tune of `unsloth/gemma-4-26b-a4b-it` for ADHD and
neurodivergent task initiation conversations. It is designed to produce warm,
short, agency-preserving prose that helps users start a task without turning the
conversation into a to-do list.

## Base Model

- Base: `unsloth/gemma-4-26b-a4b-it`
- Method: 16-bit LoRA
- License inheritance: verify from the base model card before publishing

## Training Data

The run #3 dataset is bilingual Indonesian/English:

- 270 train conversations
- 30 eval conversations
- multi-turn format with `messages[]`
- official NeuroBait system prompt prepended to each example

Dataset files are not included in the source repo or model repo unless explicitly
published later.

## Intended Use

This model is intended for a Gradio chat demo and research/product exploration
around ADHD-friendly task initiation support.

## Limitations

- Not a medical device.
- Not a replacement for professional mental-health support.
- May fail to infer the right motivator or deadline anchor from sparse context.
- English transfer must be evaluated separately for run #3.
- App-initiated reminders should be handled by app logic, not generated
  spontaneously by the model.

## Training Configuration

- LoRA rank: 16
- LoRA alpha: 16
- LoRA dropout: 0
- Epochs: 3
- Learning rate: 2e-4
- Effective batch size: 8
- Max sequence length: 2048
- Chat template: `gemma-4`
- Response-only markers: `<|turn>user\n` / `<|turn>model\n`

## Evaluation

Add final run #3 metrics and qualitative examples here after Modal eval.


Output:
Chunk ID: 475244
Wall time: 0.0000 seconds
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Output:
# NeuroBait Build Small Model

NeuroBait is a fine-tuned assistant for ADHD / neurodivergent task initiation.
This repo is the engineering source of truth for training, evaluation, and
deployment scaffolding. Dataset and model artifacts are intentionally kept out
of Git.

## Architecture

There are three separate operational artifacts:

1. GitHub repo: training, eval, deploy source code, and docs.
2. Hugging Face Model repo: LoRA adapter and/or merged model weights.
3. Hugging Face Space repo: Gradio app that loads the HF Model repo.

Flow:

```text
GitHub source code
  -> Modal.com fine-tune/eval/merge
  -> HF Model repo
  -> HF Space Gradio app
```

`github-hf.svg` contains the same architecture as a visual diagram.

## Local Development

Use the shared LLM development venv:

```bash
source /media/haris-subrata/Work/llm/agent_analytics/venv/bin/activate
python --version
```

Install only lightweight local tooling here when needed. GPU-heavy training
dependencies are owned by the Modal image in `train/modal_train.py`.

Modal CLI notes are in `docs/modal_cli.md`.
Retraining and evaluation steps are in `docs/retrain_runbook.md` and
`docs/evaluation_plan.md`.

## Data

Copy the upstream final dataset out-of-band:

```text
data/train.jsonl
data/eval.jsonl
```

Expected run #3 dataset:

- `train.jsonl`: 270 conversations
- `eval.jsonl`: 30 conversations
- bilingual ID/EN
- each row has `messages[]` with the official NeuroBait system prompt prepended

`data/` and `*.jsonl` are gitignored.

## Training On Modal

One-time setup:

```bash
pip install modal
modal token new
modal secret create huggingface HF_TOKEN=hf_xxx
```

Run training:

```bash
modal run train/modal_train.py
```

Remote preflight before paid training:

```bash
modal run train/modal_train.py --run-preflight --no-run-train
```

Optional upload/merge:

```bash
modal run train/modal_train.py --push-lora
modal run train/modal_train.py --push-lora --merge
```

Locked training design:

- Base: `unsloth/gemma-4-26b-a4b-it`
- Method: 16-bit LoRA, not QLoRA
- LoRA: `r=16`, `alpha=16`, `dropout=0`
- Epochs: `3`
- LR: `2e-4`
- Batch: `1 x grad_accum 8`
- Max sequence: `2048`
- Chat template: `gemma-4`
- Response markers: `<|turn>user\n` and `<|turn>model\n`
- `save_strategy="no"` to avoid the known Unsloth/TRL checkpoint pickle bug

Expected step count for 270 train examples:

```text
ceil(270 / 8) * 3 = 102
```

## Evaluation

Local heuristic check:

```bash
python eval/eval_neurobait.py data/eval.jsonl --output outputs/eval_reference.json
python eval/make_report.py outputs/eval_reference.json
```

This is not a replacement for GPU qualitative eval. It gives a fast local check
for label leaks, rough response length, and action-cue coverage. Run #3 must
include separate English-transfer review on held-out EN examples.

## HF Space

`deploy/` is the source for the HF Space repo:

```text
deploy/app.py
deploy/requirements.txt
```

Set `MODEL_ID` in the Space environment to the merged HF Model repo, for example:

```text
USER/neurobait-gemma4-26b-a4b
```

The Space uses Gradio and `@spaces.GPU`. Hardware must be chosen based on the
final model format. A merged bf16 26B model needs a large GPU; a quantized or
4-bit loading path may be needed for smaller Space hardware.

## Repository Boundaries

Commit to GitHub:

- `train/`
- `eval/`
- `deploy/`
- docs and diagrams

Do not commit:

- dataset JSONL
- LoRA adapters
- merged models
- GGUF files
- generated eval outputs
- tokens or `.env`


Output:
Chunk ID: dfbed4
Wall time: 0.0000 seconds
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Original token count: 222
Output:
# NeuroBait HF Space

This directory is the source for the Hugging Face Space repo. The Space should
remain separate from the GitHub source repo and the HF Model repo.

## Files

- `app.py`: Gradio chat app
- `requirements.txt`: Space dependencies

## Space Configuration

Set this environment variable in the HF Space:

```text
MODEL_ID=USER/neurobait-gemma4-26b-a4b
```

The model repo should point to the merged model intended for inference.

## Hardware

A merged bf16 Gemma 4 26B-A4B model is large. Use a GPU tier that can actually
load the final model, or switch the app to a quantized loading path before using
smaller hardware.

## App-Initiated Openers

Proactive openers belong in application logic. If the app wants to start with an
assistant message, inject that opener into chat history before the next user
turn. Do not ask the model to decide when to initiate contact.


Output:
Chunk ID: 411d86
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Process exited with code 0
Original token count: 697
Output:
# Retrain Runbook

This runbook is the execution checklist for NeuroBait run #3 on Modal.

## Preconditions

- Modal auth is configured with `modal token new`.
- Modal secret exists:

```bash
modal secret list
```

Expected secret:

```text
huggingface
```

- Dataset exists locally but is not committed:

```text
data/train.jsonl
data/eval.jsonl
```

- Dataset validation passes:

```bash
python scripts/validate_dataset.py
```

- Modal preflight passes:

```bash
modal run train/modal_train.py --run-preflight --no-run-train
```

## Training

Run:

```bash
modal run train/modal_train.py
```

Expected run #3 training steps:

```text
ceil(270 / 8) * 3 = 102
```

Locked training settings:

- Base: `unsloth/gemma-4-26b-a4b-it`
- Method: 16-bit LoRA
- LoRA: r=16, alpha=16, dropout=0
- Epochs: 3
- LR: 2e-4
- Batch: 1 x grad_accum 8
- Max sequence: 2048
- Chat template: `gemma-4`
- Response markers: `<|turn>user\n` / `<|turn>model\n`
- Save strategy: `no`

## Artifact Handling

Training saves adapter to Modal Volume:

```text
/out/neurobait-lora-run3
```

Download adapter locally after training:

```bash
modal volume get neurobait-out neurobait-lora-run3 ./neurobait-lora-run3
```

Do not commit the adapter. It is ignored by `.gitignore`.

Keep the Modal Volume copy until:

- adapter is downloaded locally,
- eval artifacts are generated,
- final HF upload strategy is confirmed,
- model has been pushed or otherwise backed up.

## Immediate Post-Train Checks

Check `train_summary.json` in the adapter directory:

- `n_train` should be `270`
- `n_eval` should be `30`
- `expected_steps` should be `102`
- confirm final train/eval metrics are present

## Evaluation Order

1. Existing/local heuristic eval:

```bash
python eval/eval_neurobait.py data/eval.jsonl --output outputs/eval_reference.json
python eval/make_report.py outputs/eval_reference.json
```

2. GPU generation eval:

- load base + LoRA once,
- compare base vs fine-tuned,
- use held-out eval turns,
- include novel prompts,
- include English-transfer prompts,
- save JSON output,
- render report.

3. Additional general chat-model eval:

Use a judge-based pairwise eval inspired by MT-Bench/AlpacaEval-style practice:

- compare base vs fine-tuned responses blind,
- judge with a fixed rubric,
- report win/tie/loss rate,
- evaluate helpfulness, persona fit, instruction following, concision, safety,
  and language consistency,
- include bilingual ID/EN prompt sets.

The judge eval should not replace qualitative review; it adds a standardized
second signal after the project-specific NeuroBait eval.

## Upload And Deployment

Do not push to Hugging Face until target org/repo names are confirmed.

Expected repos:

- adapter model repo
- merged model repo
- Space repo

Push only after eval is acceptable.


Saya akan pakai rekomendasi slug pendek: `build-small-hackathon/NeuroBait` untuk model repo dan `build-small-hackathon/NeuroBait` juga untuk Space repo. Di Hugging Face, model dan Space beda namespace, jadi URL-nya tidak bentrok: model di `/build-small-hackathon/NeuroBait`, app di `/spaces/build-small-hackathon/NeuroBait`.


Output:
Chunk ID: ec46fd
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 16
Output:
spaces
gradio
transformers==5.5.0
accelerate
torch
bitsandbytes


Saya akan menyiapkan publish lewat Modal, karena HF token sudah aman di Modal secret dan adapter juga ada di Modal Volume. Itu menghindari token lokal dan tidak memindahkan file 1GB lewat GitHub.


/media/haris-subrata/Work/llm/agent_analytics/venv/bin/python -c "import huggingface_hub, inspect; from huggingface_hub import HfApi; print(huggingface_hub.__version__); print(hasattr(HfApi, 'request_space_hardware')); print(inspect.signature(HfApi.create_repo)); print(inspect.signature(HfApi.upload_folder))"


sed -n '1,260p' eval/modal_generation_eval.py


Output:
Chunk ID: a39f1a
Wall time: 0.0000 seconds
Process exited with code 1
Original token count: 33
Output:
Traceback (most recent call last):
  File "<string>", line 1, in <module>
ModuleNotFoundError: No module named 'huggingface_hub'


Output:
Chunk ID: 8c4bcd
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 160
Output:
total 995M
-rw-rw-r-- 1 haris-subrata haris-subrata 5.2K Jun  8 21:23 README.md
-rw-rw-r-- 1 haris-subrata haris-subrata 1.3K Jun  8 21:23 adapter_config.json
-rw-rw-r-- 1 haris-subrata haris-subrata 965M Jun  8 21:25 adapter_model.safetensors
-rw-rw-r-- 1 haris-subrata haris-subrata 2.4K Jun  8 21:23 chat_template.jinja
-rw-rw-r-- 1 haris-subrata haris-subrata 1.7K Jun  8 21:22 processor_config.json
-rw-rw-r-- 1 haris-subrata haris-subrata  31M Jun  8 21:23 tokenizer.json
-rw-rw-r-- 1 haris-subrata haris-subrata 6.8K Jun  8 21:22 tokenizer_config.json
-rw-rw-r-- 1 haris-subrata haris-subrata  512 Jun  8 21:22 train_summary.json


Output:
Chunk ID: 218c7a
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2477
Output:
"""Modal generation eval for NeuroBait run #3.

This is a compact base-vs-LoRA eval intended to run immediately after training.
It writes JSON and Markdown reports to the Modal output volume.
"""

from __future__ import annotations

import modal

app = modal.App("neurobait-generation-eval")

image = (
    modal.Image.debian_slim(python_version="3.11")
    .pip_install("unsloth", "unsloth_zoo")
    .pip_install(
        "trl==0.24",
        "peft==0.19.1",
        "transformers==5.5.0",
        "datasets",
        "accelerate",
        "bitsandbytes",
        "huggingface_hub",
        extra_options="--no-deps",
    )
    .add_local_file("data/eval.jsonl", "/data/eval.jsonl")
)

hf_cache = modal.Volume.from_name("hf-cache", create_if_missing=True)
out_vol = modal.Volume.from_name("neurobait-out", create_if_missing=True)

SYSTEM_PROMPT = """Kamu adalah NeuroBait — asisten AI untuk orang dengan ADHD dan neurodivergent. Tugasmu bukan membuat to-do list. Tugasmu menyalakan dopamin untuk memicu task initiation.

Dari setiap percakapan, identifikasi dua elemen kunci: (1) deadline anchor — momen nyata atau buatan yang bisa jadi batas waktu relevan; dan (2) object/subject motivator — orang atau hal yang paling emosional signifikan bagi user saat ini. Gunakan keduanya sebagai bahan bakar Resep Engagement yang personal, bukan generik.

Setiap Resep Engagement memuat empat elemen berurut natural: validasi hangat singkat tanpa menghakimi → hook yang membangkitkan rasa flow dari minat atau pengalaman user → stakes berbasis deadline atau motivator nyata → satu micro-action super kecil dan spesifik yang bisa langsung dilakukan.

Kalau konteks user belum cukup untuk membuat resep yang personal, ajukan tepat satu pertanyaan ringan yang paling berguna — tentang deadline atau motivator. Kalau konteks sudah ada, langsung berikan resep.

Framing selalu menempatkan user sebagai pelaku aktif dengan agency penuh. Bukan guilt, bukan hutang — selalu agency. Kalimat pendek. Bahasa hidup. Hangat dan padat. Tidak pernah menghakimi. Tidak pernah ceramah. Membuat hal membosankan jadi tak tertahankan."""

@app.function(
    image=image,
    gpu="H100",
    timeout=45 * 60,
    volumes={"/root/.cache/huggingface": hf_cache, "/out": out_vol},
    secrets=[modal.Secret.from_name("huggingface")],
)
def evaluate(adapter_path: str = "/out/neurobait-lora-run3") -> str:
    import json
    import os
    import re
    import time
    from pathlib import Path

    import torch
    from unsloth import FastModel

    os.environ["HF_HOME"] = "/root/.cache/huggingface"

    def mm(messages):
        normalized = []
        for message in messages:
            content = message["content"]
            if isinstance(content, str):
                content = [{"type": "text", "text": content}]
            normalized.append({"role": message["role"], "content": content})
        return normalized

    def words(text: str) -> int:
        return len(re.findall(r"[\w']+", text.lower()))

    def sentences(text: str) -> int:
        return len([s for s in re.split(r"[.!?]+", text) if s.strip()])

    def label_leak(text: str) -> bool:
        low = text.lower()
        return any(x in low for x in ["micro-action", "hook:", "stakes:", "validasi:", "langkah 1"])

    action_cues = [
        "coba",
        "buka",
        "tulis",
        "ambil",
        "pilih",
        "taruh",
        "letak",
        "berdiri",
        "singkir",
        "pakai",
        "set timer",
        "open",
        "write",
        "pick",
        "stand",
        "move",
    ]

    def has_action(text: str) -> bool:
        low = text.lower()
        toks = re.findall(r"[\w']+", low)
        return any(cue in low if " " in cue else any(tok.startswith(cue) for tok in toks) for cue in action_cues)

    def persona_score(text: str) -> int:
        score = 0
        if not label_leak(text) and "\n-" not in text and "\n•" not in text:
            score += 1
        if 1 <= sentences(text) <= 7:
            score += 1
        if has_action(text) or "?" in text:
            score += 1
        if words(text) <= 95:
            score += 1
        return score

    def generate(messages, use_base: bool) -> dict:
        input_ids = tokenizer.apply_chat_template(
            mm(messages),
            tokenize=True,
            add_generation_prompt=True,
            return_tensors="pt",
        )
        if not torch.is_tensor(input_ids):
            input_ids = input_ids["input_ids"]
        input_ids = input_ids.to("cuda")
        start = input_ids.shape[-1]
        ctx = model.disable_adapter() if use_base and hasattr(model, "disable_adapter") else nullcontext()
        t0 = time.time()
        with torch.no_grad(), ctx:
            output = model.generate(
                input_ids=input_ids,
                max_new_tokens=260,
                do_sample=True,
                temperature=0.7,
                top_p=0.9,
                repetition_penalty=1.1,
                pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id,
            )
        dt = time.time() - t0
        generated = output[0][start:]
        text = tokenizer.decode(generated, skip_special_tokens=True).strip()
        return {
            "text": text,
            "tokens": int(generated.shape[-1]),
            "seconds": round(dt, 2),
            "tok_s": round(int(generated.shape[-1]) / dt, 2) if dt > 0 else 0,
            "words": words(text),
            "sentences": sentences(text),
            "label_leak": label_leak(text),
            "has_action": has_action(text),
            "persona": persona_score(text),
        }

    from contextlib import nullcontext

    print(f">>> loading adapter {adapter_path}", flush=True)
    model, tokenizer = FastModel.from_pretrained(
        model_name=adapter_path,
        max_seq_length=2048,
        load_in_4bit=False,
        load_in_16bit=True,
        full_finetuning=False,
    )
    try:
        FastModel.for_inference(model)
    except Exception as exc:
        print(f">>> for_inference skipped: {exc}", flush=True)

    prompts = [
        {"id": "id_deadline", "lang": "id", "prompt": "Aku harus revisi proposal malam ini tapi dari tadi cuma buka tutup dokumen."},
        {"id": "id_overwhelm", "lang": "id", "prompt": "Kamarku berantakan banget dan aku malu sendiri lihatnya."},
        {"id": "id_sparse", "lang": "id", "prompt": "Aku stuck banget."},
        {"id": "en_deadline", "lang": "en", "prompt": "I need to finish a presentation tomorrow, but I keep avoiding the first slide."},
        {"id": "en_overwhelm", "lang": "en", "prompt": "My inbox is so messy that I freeze every time I open it."},
        {"id": "en_sparse", "lang": "en", "prompt": "I feel stuck and I don't know where to start."},
        {"id": "app_cont_id", "lang": "id", "messages": [
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "assistant", "content": "Eh, si laporan yang kemarin kamu tinggal di paragraf dua itu masih nyangkut di situ atau udah pindah?"},
            {"role": "user", "content": "masih nyangkut, belum kubuka lagi dari kemarin."},
        ]},
        {"id": "app_cont_en", "lang": "en", "messages": [
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "assistant", "content": "That tiny email you were going to answer yesterday, is it still sitting there giving you side-eye?"},
            {"role": "user", "content": "yes, and now I feel ridiculous about it."},
        ]},
    ]

    rows = []
    for item in prompts:
        messages = item.get("messages") or [
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": item["prompt"]},
        ]
        base = generate(messages, use_base=True)
        ft = generate(messages, use_base=False)
        rows.append({**item, "base": base, "fine_tuned": ft})
        print(f">>> {item['id']} base={base['persona']} ft={ft['persona']}", flush=True)

    summary = {
        "n": len(rows),
        "base_persona_avg": round(sum(r["base"]["persona"] for r in rows) / len(rows), 3),
        "ft_persona_avg": round(sum(r["fine_tuned"]["persona"] for r in rows) / len(rows), 3),
        "base_words_avg": round(sum(r["base"]["words"] for r in rows) / len(rows), 3),
        "ft_words_avg": round(sum(r["fine_tuned"]["words"] for r in rows) / len(rows), 3),
        "base_label_leaks": sum(1 for r in rows if r["base"]["label_leak"]),
        "ft_label_leaks": sum(1 for r in rows if r["fine_tuned"]["label_leak"]),
        "base_actions": sum(1 for r in rows if r["base"]["has_action"]),
        "ft_actions": sum(1 for r in rows if r["fine_tuned"]["has_action"]),
    }

    out_dir = Path("/out/outputs")
    out_dir.mkdir(parents=True, exist_ok=True)
    payload = {"adapter": adapter_path, "summary": summary, "rows": rows}
    json_path = out_dir / "run3_generation_eval.json"
    json_path.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")

    lines = ["# NeuroBait Run 3 Generation Eval", "", "## Summary", ""]
    for key, value in summary.items():
        lines.append(f"- {key}: `{value}`")
    lines.append("")
    for row in rows:
        lines.extend([
            f"## {row['id']} ({row['lang']})",
            "",
            f"Prompt: {row.get('prompt') or row['messages'][-1]['content']}",
            "",
            f"Base ({row['base']['persona']}/4): {row['base']['text']}",
            "",
            f"Fine-tuned ({row['fine_tuned']['persona']}/4): {row['fine_tuned']['text']}",
            "",
        ])
    md_path = out_dir / "run3_generation_eval.md"
    md_path.write_text("\n".join(lines), encoding="utf-8")
    out_vol.commit()
    return json.dumps({"summary": summary, "json": str(json_path), "markdown": str(md_path)}, indent=2)

@app.local_entrypoint()
def main() -> None:
    print(evaluate.remote())


Output:
Chunk ID: 8fde1f
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 318
Output:
# NeuroBait Product Brief

NeuroBait is an AI companion for ADHD and neurodivergent task initiation. The
core job is not planning, productivity coaching, or generating a full to-do
list. The core job is creating enough emotional and attentional momentum for the
user to start.

The assistant should feel like a warm conversational partner:

- short, natural prose
- no labels such as `Micro-action`, `Hook`, or `Stakes`
- no guilt framing
- no generic motivational cliches
- always preserves user agency
- asks exactly one light question when context is insufficient
- gives one tiny concrete next action when context is sufficient

The model is trained for multi-turn momentum. The app layer may initiate
proactive openers from user state, but the model itself should not be responsible
for deciding when to initiate contact.

## Design Anchors

Every response should infer or ask for:

- deadline anchor: a real or artificial moment that can make action feel timely
- object/subject motivator: the emotionally meaningful person, object, or reason

Those anchors are used as raw material for a natural engagement recipe:

1. warm validation
2. interest or flow hook
3. relevant stakes
4. one tiny action

The recipe should not be exposed as structure in the final text.


Saya akan edit tiga area: `train/modal_train.py` untuk publish model/Space ke org lomba, `deploy/` untuk app Gradio English-first yang memuat base+LoRA adapter, dan README/model card untuk mengunci slug pendek serta hasil retrain/eval run #3.


Input:
*** Begin Patch
*** Update File: train/modal_train.py
@@
 APP_NAME = "neurobait-small-model"
-DEFAULT_LORA_REPO = "USER/neurobait-gemma4-26b-a4b-lora"
-DEFAULT_MERGED_REPO = "USER/neurobait-gemma4-26b-a4b"
+DEFAULT_LORA_REPO = "build-small-hackathon/NeuroBait"
+DEFAULT_MERGED_REPO = "build-small-hackathon/NeuroBait-Merged"
+DEFAULT_SPACE_REPO = "build-small-hackathon/NeuroBait"
@@
     .add_local_file("train/train_core.py", "/root/neurobait/train_core.py")
     .add_local_file("data/train.jsonl", "/data/train.jsonl")
     .add_local_file("data/eval.jsonl", "/data/eval.jsonl")
+    .add_local_file("model-card/README.md", "/root/neurobait/model-card/README.md")
+    .add_local_dir("deploy", "/root/neurobait/deploy")
 )
@@
 def push_lora_to_hub(repo_id: str = DEFAULT_LORA_REPO) -> str:
+    import shutil
+
     from huggingface_hub import HfApi

-    HfApi().upload_folder(
+    api = HfApi()
+    api.create_repo(repo_id=repo_id, repo_type="model", private=False, exist_ok=True)
+    shutil.copyfile("/root/neurobait/model-card/README.md", "/out/neurobait-lora-run3/README.md")
+    HfApi().upload_folder(
         folder_path="/out/neurobait-lora-run3",
         repo_id=repo_id,
         repo_type="model",
@@
     return repo_id

+@app.function(
+    image=image,
+    timeout=30 * 60,
+    secrets=[modal.Secret.from_name("huggingface")],
+)
+def push_space_to_hub(repo_id: str = DEFAULT_SPACE_REPO) -> str:
+    from huggingface_hub import HfApi
+
+    api = HfApi()
+    api.create_repo(
+        repo_id=repo_id,
+        repo_type="space",
+        space_sdk="gradio",
+        private=False,
+        exist_ok=True,
+    )
+    api.upload_folder(
+        folder_path="/root/neurobait/deploy",
+        repo_id=repo_id,
+        repo_type="space",
+    )
+    return repo_id
+
+
 @app.function(
     image=image,
     gpu="H100",
@@
     push_lora: bool = False,
+    push_space: bool = False,
     merge: bool = False,
     lora_repo: str = DEFAULT_LORA_REPO,
+    space_repo: str = DEFAULT_SPACE_REPO,
     merged_repo: str = DEFAULT_MERGED_REPO,
 ) -> None:
@@
     if push_lora:
         print("lora repo:", push_lora_to_hub.remote(lora_repo))
+    if push_space:
+        print("space repo:", push_space_to_hub.remote(space_repo))
     if merge:
         print("merged repo:", merge_and_push.remote(merged_repo))
*** End Patch


Success. Updated the following files:
M train/modal_train.py

@@ -8,4 +8,5 @@
 APP_NAME = "neurobait-small-model"
-DEFAULT_LORA_REPO = "USER/neurobait-gemma4-26b-a4b-lora"
-DEFAULT_MERGED_REPO = "USER/neurobait-gemma4-26b-a4b"
+DEFAULT_LORA_REPO = "build-small-hackathon/NeuroBait"
+DEFAULT_MERGED_REPO = "build-small-hackathon/NeuroBait-Merged"
+DEFAULT_SPACE_REPO = "build-small-hackathon/NeuroBait"

@@ -29,2 +30,4 @@
     .add_local_file("data/eval.jsonl", "/data/eval.jsonl")
+    .add_local_file("model-card/README.md", "/root/neurobait/model-card/README.md")
+    .add_local_dir("deploy", "/root/neurobait/deploy")
 )
@@ -121,4 +124,9 @@
 def push_lora_to_hub(repo_id: str = DEFAULT_LORA_REPO) -> str:
+    import shutil
+
     from huggingface_hub import HfApi

+    api = HfApi()
+    api.create_repo(repo_id=repo_id, repo_type="model", private=False, exist_ok=True)
+    shutil.copyfile("/root/neurobait/model-card/README.md", "/out/neurobait-lora-run3/README.md")
     HfApi().upload_folder(
@@ -133,2 +141,26 @@
     image=image,
+    timeout=30 * 60,
+    secrets=[modal.Secret.from_name("huggingface")],
+)
+def push_space_to_hub(repo_id: str = DEFAULT_SPACE_REPO) -> str:
+    from huggingface_hub import HfApi
+
+    api = HfApi()
+    api.create_repo(
+        repo_id=repo_id,
+        repo_type="space",
+        space_sdk="gradio",
+        private=False,
+        exist_ok=True,
+    )
+    api.upload_folder(
+        folder_path="/root/neurobait/deploy",
+        repo_id=repo_id,
+        repo_type="space",
+    )
+    return repo_id
+
+
+@app.function(
+    image=image,
     gpu="H100",
@@ -171,4 +203,6 @@
     push_lora: bool = False,
+    push_space: bool = False,
     merge: bool = False,
     lora_repo: str = DEFAULT_LORA_REPO,
+    space_repo: str = DEFAULT_SPACE_REPO,
     merged_repo: str = DEFAULT_MERGED_REPO,
@@ -182,2 +216,4 @@
         print("lora repo:", push_lora_to_hub.remote(lora_repo))
+    if push_space:
+        print("space repo:", push_space_to_hub.remote(space_repo))
     if merge:


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
Success. Updated the following files:
M train/modal_train.py


Slug pendek ini juga membuat nama model dan app konsisten untuk submission. Saya sekarang ubah Space app supaya default-nya memuat adapter `build-small-hackathon/NeuroBait`, bukan menunggu model merged.


Input:
*** Begin Patch
*** Delete File: deploy/app.py
*** End Patch


Success. Updated the following files:
D deploy/app.py

"""Hugging Face Space app for NeuroBait."""

from __future__ import annotations

import os

import gradio as gr
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = os.environ.get("MODEL_ID", "USER/neurobait-gemma4-26b-a4b")
SYSTEM_PROMPT = """Kamu adalah NeuroBait — asisten AI untuk orang dengan ADHD dan neurodivergent. Tugasmu bukan membuat to-do list. Tugasmu menyalakan dopamin untuk memicu task initiation.

Dari setiap percakapan, identifikasi dua elemen kunci: (1) deadline anchor — momen nyata atau buatan yang bisa jadi batas waktu relevan; dan (2) object/subject motivator — orang atau hal yang paling emosional signifikan bagi user saat ini. Gunakan keduanya sebagai bahan bakar Resep Engagement yang personal, bukan generik.

Setiap Resep Engagement memuat empat elemen berurut natural: validasi hangat singkat tanpa menghakimi → hook yang membangkitkan rasa flow dari minat atau pengalaman user → stakes berbasis deadline atau motivator nyata → satu micro-action super kecil dan spesifik yang bisa langsung dilakukan.

Kalau konteks user belum cukup untuk membuat resep yang personal, ajukan tepat satu pertanyaan ringan yang paling berguna — tentang deadline atau motivator. Kalau konteks sudah ada, langsung berikan resep.

Framing selalu menempatkan user sebagai pelaku aktif dengan agency penuh. Bukan guilt, bukan hutang — selalu agency. Kalimat pendek. Bahasa hidup. Hangat dan padat. Tidak pernah menghakimi. Tidak pernah ceramah. Membuat hal membosankan jadi tak tertahankan."""

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

def _mm(messages: list[dict]) -> list[dict]:
    """Gemma 4 / transformers 5.5 content format normalization."""

    normalized = []
    for message in messages:
        content = message["content"]
        if isinstance(content, str):
            content = [{"type": "text", "text": content}]
        normalized.append({"role": message["role"], "content": content})
    return normalized

def _history_to_messages(history: list) -> list[dict]:
    messages = []
    for item in history:
        if isinstance(item, dict):
            role = item.get("role")
            content = item.get("content")
            if role in {"user", "assistant"} and content:
                messages.append({"role": role, "content": content})
            continue
        if isinstance(item, (tuple, list)) and len(item) == 2:
            user_text, assistant_text = item
            if user_text:
                messages.append({"role": "user", "content": user_text})
            if assistant_text:
                messages.append({"role": "assistant", "content": assistant_text})
    return messages

@spaces.GPU
def chat(message: str, history: list) -> str:
    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
    messages.extend(_history_to_messages(history))
    messages.append({"role": "user", "content": message})

    input_ids = tokenizer.apply_chat_template(
        _mm(messages),
        add_generation_prompt=True,
        return_tensors="pt",
    ).to(model.device)
    output_ids = model.generate(
        input_ids,
        max_new_tokens=400,
        do_sample=True,
        temperature=0.7,
        top_p=0.9,
        repetition_penalty=1.1,
    )
    return tokenizer.decode(output_ids[0][input_ids.shape[-1] :], skip_special_tokens=True).strip()

demo = gr.ChatInterface(
    fn=chat,
    title="NeuroBait",
    description="Asisten task-initiation untuk otak ADHD dan neurodivergent.",
)

if __name__ == "__main__":
    demo.launch()


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
Success. Updated the following files:
D deploy/app.py


Input:
*** Begin Patch
*** Add File: deploy/app.py
+"""Hugging Face Space app for NeuroBait."""
+
+from __future__ import annotations
+
+import os
+import re
+from threading import Lock
+
+import gradio as gr
+import spaces
+import torch
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+
+BASE_MODEL = os.environ.get("BASE_MODEL", "unsloth/gemma-4-26b-a4b-it")
+ADAPTER_ID = os.environ.get("ADAPTER_ID", os.environ.get("MODEL_ID", "build-small-hackathon/NeuroBait"))
+MAX_NEW_TOKENS = int(os.environ.get("MAX_NEW_TOKENS", "260"))
+LOAD_IN_4BIT = os.environ.get("LOAD_IN_4BIT", "1").lower() not in {"0", "false", "no"}
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+SYSTEM_PROMPT = """Kamu adalah NeuroBait — asisten AI untuk orang dengan ADHD dan neurodivergent. Tugasmu bukan membuat to-do list. Tugasmu menyalakan dopamin untuk memicu task initiation.
+
+Dari setiap percakapan, identifikasi dua elemen kunci: (1) deadline anchor — momen nyata atau buatan yang bisa jadi batas waktu relevan; dan (2) object/subject motivator — orang atau hal yang paling emosional signifikan bagi user saat ini. Gunakan keduanya sebagai bahan bakar Resep Engagement yang personal, bukan generik.
+
+Setiap Resep Engagement memuat empat elemen berurut natural: validasi hangat singkat tanpa menghakimi → hook yang membangkitkan rasa flow dari minat atau pengalaman user → stakes berbasis deadline atau motivator nyata → satu micro-action super kecil dan spesifik yang bisa langsung dilakukan.
+
+Kalau konteks user belum cukup untuk membuat resep yang personal, ajukan tepat satu pertanyaan ringan yang paling berguna — tentang deadline atau motivator. Kalau konteks sudah ada, langsung berikan resep.
+
+Framing selalu menempatkan user sebagai pelaku aktif dengan agency penuh. Bukan guilt, bukan hutang — selalu agency. Kalimat pendek. Bahasa hidup. Hangat dan padat. Tidak pernah menghakimi. Tidak pernah ceramah. Membuat hal membosankan jadi tak tertahankan."""
+
+
+_model = None
+_tokenizer = None
+_load_lock = Lock()
+
+
+def _content_blocks(messages: list[dict]) -> list[dict]:
+    normalized = []
+    for message in messages:
+        content = message["content"]
+        if isinstance(content, str):
+            content = [{"type": "text", "text": content}]
+        normalized.append({"role": message["role"], "content": content})
+    return normalized
+
+
+def _load_model():
+    global _model, _tokenizer
+
+    with _load_lock:
+        if _model is not None and _tokenizer is not None:
+            return _model, _tokenizer
+
+        tokenizer = AutoTokenizer.from_pretrained(ADAPTER_ID, token=HF_TOKEN)
+        quantization_config = None
+        torch_dtype = torch.bfloat16
+        if LOAD_IN_4BIT:
+            quantization_config = BitsAndBytesConfig(
+                load_in_4bit=True,
+                bnb_4bit_compute_dtype=torch.bfloat16,
+                bnb_4bit_quant_type="nf4",
+            )
+            torch_dtype = None
+
+        base = AutoModelForCausalLM.from_pretrained(
+            BASE_MODEL,
+            token=HF_TOKEN,
+            torch_dtype=torch_dtype,
+            quantization_config=quantization_config,
+            device_map="auto",
+        )
+        model = PeftModel.from_pretrained(base, ADAPTER_ID, token=HF_TOKEN)
+        model.eval()
+
+        _model = model
+        _tokenizer = tokenizer
+        return _model, _tokenizer
+
+
+def _history_to_messages(history: list[dict]) -> list[dict]:
+    messages = []
+    for item in history:
+        role = item.get("role")
+        content = item.get("content")
+        if role in {"user", "assistant"} and isinstance(content, str) and content.strip():
+            messages.append({"role": role, "content": content.strip()})
+    return messages
+
+
+def _clean_response(text: str) -> str:
+    text = text.strip()
+    text = re.sub(r"(?im)^\s*(micro-action|hook|stakes|validasi|validation)\s*:\s*", "", text)
+    return text.strip()
+
+
+@spaces.GPU(duration=120)
+def respond(message: str, history: list[dict], temperature: float, top_p: float) -> str:
+    model, tokenizer = _load_model()
+    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
+    messages.extend(_history_to_messages(history))
+    messages.append({"role": "user", "content": message.strip()})
+
+    input_ids = tokenizer.apply_chat_template(
+        _content_blocks(messages),
+        add_generation_prompt=True,
+        return_tensors="pt",
+    ).to(model.device)
+    start = input_ids.shape[-1]
+
+    with torch.no_grad():
+        output_ids = model.generate(
+            input_ids,
+            max_new_tokens=MAX_NEW_TOKENS,
+            do_sample=True,
+            temperature=temperature,
+            top_p=top_p,
+            repetition_penalty=1.1,
+            pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id,
+        )
+    text = tokenizer.decode(output_ids[0][start:], skip_special_tokens=True)
+    return _clean_response(text)
+
+
+CSS = """
+:root {
+  --nb-ink: #202624;
+  --nb-muted: #61706b;
+  --nb-paper: #faf7f0;
+  --nb-panel: #fffdf8;
+  --nb-line: #d8ded2;
+  --nb-sage: #6f8f7a;
+  --nb-clay: #b36a4c;
+  --nb-blue: #536d89;
+}
+body, .gradio-container {
+  background: var(--nb-paper);
+  color: var(--nb-ink);
+  font-family: Inter, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
+}
+.nb-shell {
+  max-width: 1120px;
+  margin: 0 auto;
+}
+.nb-title {
+  display: flex;
+  align-items: baseline;
+  justify-content: space-between;
+  gap: 16px;
+  padding: 18px 0 10px;
+  border-bottom: 1px solid var(--nb-line);
+}
+.nb-title h1 {
+  margin: 0;
+  font-size: 28px;
+  line-height: 1.1;
+  letter-spacing: 0;
+}
+.nb-title p {
+  margin: 0;
+  color: var(--nb-muted);
+  font-size: 14px;
+}
+.nb-layout {
+  display: grid;
+  grid-template-columns: minmax(0, 1fr) 280px;
+  gap: 18px;
+  margin-top: 18px;
+}
+.nb-side {
+  border-left: 1px solid var(--nb-line);
+  padding-left: 18px;
+}
+.nb-side h2 {
+  font-size: 14px;
+  margin: 0 0 10px;
+}
+.nb-side p {
+  color: var(--nb-muted);
+  font-size: 13px;
+  line-height: 1.45;
+}
+.nb-chatbot {
+  min-height: 580px;
+  border: 1px solid var(--nb-line);
+  background: var(--nb-panel);
+}
+.nb-chatbot .message {
+  border-radius: 8px !important;
+}
+.nb-input textarea {
+  border-radius: 8px !important;
+}
+.nb-examples button {
+  border-radius: 8px !important;
+  min-height: 38px;
+  text-align: left;
+}
+.nb-controls {
+  gap: 10px;
+}
+.nb-controls .wrap {
+  border-radius: 8px !important;
+}
+button.primary {
+  background: var(--nb-sage) !important;
+  border-color: var(--nb-sage) !important;
+}
+@media (max-width: 860px) {
+  .nb-title {
+    display: block;
+  }
+  .nb-title p {
+    margin-top: 8px;
+  }
+  .nb-layout {
+    grid-template-columns: 1fr;
+  }
+  .nb-side {
+    border-left: 0;
+    border-top: 1px solid var(--nb-line);
+    padding-left: 0;
+    padding-top: 14px;
+  }
+}
+"""
+
+
+with gr.Blocks(css=CSS, theme=gr.themes.Soft(primary_hue="green", neutral_hue="stone")) as demo:
+    gr.HTML(
+        """
+

+

+
NeuroBait

+
ADHD-friendly task initiation, tuned for one tiny next move.

+

+

+        """
+    )
+    with gr.Row(elem_classes=["nb-shell", "nb-layout"]):
+        with gr.Column(scale=1):
+            chatbot = gr.Chatbot(
+                type="messages",
+                height=580,
+                show_copy_button=True,
+                elem_classes=["nb-chatbot"],
+            )
+            message = gr.Textbox(
+                placeholder="What are you avoiding right now?",
+                lines=2,
+                max_lines=5,
+                show_label=False,
+                elem_classes=["nb-input"],
+            )
+            with gr.Row():
+                submit = gr.Button("Start", variant="primary")
+                clear = gr.Button("Clear")
+            gr.Examples(
+                examples=[
+                    "I need to finish a presentation tomorrow, but I keep avoiding the first slide.",
+                    "My inbox is so messy that I freeze every time I open it.",
+                    "I feel stuck and I don't know where to start.",
+                ],
+                inputs=message,
+                elem_classes=["nb-examples"],
+            )
+        with gr.Column(scale=0, min_width=260, elem_classes=["nb-side"]):
+            gr.HTML(
+                """
+
Session feel

+
No streaks, shame, or productivity theatre. NeuroBait looks for the emotional anchor and turns it into a small action you can start now.

+                """
+            )
+            with gr.Accordion("Generation", open=False):
+                temperature = gr.Slider(0.2, 1.0, value=0.7, step=0.05, label="Temperature")
+                top_p = gr.Slider(0.6, 1.0, value=0.9, step=0.05, label="Top p")
+
+    def user_turn(user_message: str, chat_history: list[dict]) -> tuple[str, list[dict]]:
+        if not user_message.strip():
+            return "", chat_history
+        return "", chat_history + [{"role": "user", "content": user_message.strip()}]
+
+    def bot_turn(chat_history: list[dict], temperature_value: float, top_p_value: float) -> list[dict]:
+        user_message = chat_history[-1]["content"]
+        assistant_message = respond(user_message, chat_history[:-1], temperature_value, top_p_value)
+        return chat_history + [{"role": "assistant", "content": assistant_message}]
+
+    submit.click(user_turn, [message, chatbot], [message, chatbot], queue=False).then(
+        bot_turn,
+        [chatbot, temperature, top_p],
+        chatbot,
+    )
+    message.submit(user_turn, [message, chatbot], [message, chatbot], queue=False).then(
+        bot_turn,
+        [chatbot, temperature, top_p],
+        chatbot,
+    )
+    clear.click(lambda: [], outputs=chatbot, queue=False)
+
+
+if __name__ == "__main__":
+    demo.launch()
*** End Patch


Success. Updated the following files:
A deploy/app.py

"""Hugging Face Space app for NeuroBait."""

from __future__ import annotations

import os
import re
from threading import Lock

import gradio as gr
import spaces
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

BASE_MODEL = os.environ.get("BASE_MODEL", "unsloth/gemma-4-26b-a4b-it")
ADAPTER_ID = os.environ.get("ADAPTER_ID", os.environ.get("MODEL_ID", "build-small-hackathon/NeuroBait"))
MAX_NEW_TOKENS = int(os.environ.get("MAX_NEW_TOKENS", "260"))
LOAD_IN_4BIT = os.environ.get("LOAD_IN_4BIT", "1").lower() not in {"0", "false", "no"}
HF_TOKEN = os.environ.get("HF_TOKEN")

SYSTEM_PROMPT = """Kamu adalah NeuroBait — asisten AI untuk orang dengan ADHD dan neurodivergent. Tugasmu bukan membuat to-do list. Tugasmu menyalakan dopamin untuk memicu task initiation.

Dari setiap percakapan, identifikasi dua elemen kunci: (1) deadline anchor — momen nyata atau buatan yang bisa jadi batas waktu relevan; dan (2) object/subject motivator — orang atau hal yang paling emosional signifikan bagi user saat ini. Gunakan keduanya sebagai bahan bakar Resep Engagement yang personal, bukan generik.

Setiap Resep Engagement memuat empat elemen berurut natural: validasi hangat singkat tanpa menghakimi → hook yang membangkitkan rasa flow dari minat atau pengalaman user → stakes berbasis deadline atau motivator nyata → satu micro-action super kecil dan spesifik yang bisa langsung dilakukan.

Kalau konteks user belum cukup untuk membuat resep yang personal, ajukan tepat satu pertanyaan ringan yang paling berguna — tentang deadline atau motivator. Kalau konteks sudah ada, langsung berikan resep.

Framing selalu menempatkan user sebagai pelaku aktif dengan agency penuh. Bukan guilt, bukan hutang — selalu agency. Kalimat pendek. Bahasa hidup. Hangat dan padat. Tidak pernah menghakimi. Tidak pernah ceramah. Membuat hal membosankan jadi tak tertahankan."""

_model = None
_tokenizer = None
_load_lock = Lock()

def _content_blocks(messages: list[dict]) -> list[dict]:
    normalized = []
    for message in messages:
        content = message["content"]
        if isinstance(content, str):
            content = [{"type": "text", "text": content}]
        normalized.append({"role": message["role"], "content": content})
    return normalized

def _load_model():
    global _model, _tokenizer

    with _load_lock:
        if _model is not None and _tokenizer is not None:
            return _model, _tokenizer

        tokenizer = AutoTokenizer.from_pretrained(ADAPTER_ID, token=HF_TOKEN)
        quantization_config = None
        torch_dtype = torch.bfloat16
        if LOAD_IN_4BIT:
            quantization_config = BitsAndBytesConfig(
                load_in_4bit=True,
                bnb_4bit_compute_dtype=torch.bfloat16,
                bnb_4bit_quant_type="nf4",
            )
            torch_dtype = None

        base = AutoModelForCausalLM.from_pretrained(
            BASE_MODEL,
            token=HF_TOKEN,
            torch_dtype=torch_dtype,
            quantization_config=quantization_config,
            device_map="auto",
        )
        model = PeftModel.from_pretrained(base, ADAPTER_ID, token=HF_TOKEN)
        model.eval()

        _model = model
        _tokenizer = tokenizer
        return _model, _tokenizer

def _history_to_messages(history: list[dict]) -> list[dict]:
    messages = []
    for item in history:
        role = item.get("role")
        content = item.get("content")
        if role in {"user", "assistant"} and isinstance(content, str) and content.strip():
            messages.append({"role": role, "content": content.strip()})
    return messages

def _clean_response(text: str) -> str:
    text = text.strip()
    text = re.sub(r"(?im)^\s*(micro-action|hook|stakes|validasi|validation)\s*:\s*", "", text)
    return text.strip()

@spaces.GPU(duration=120)
def respond(message: str, history: list[dict], temperature: float, top_p: float) -> str:
    model, tokenizer = _load_model()
    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
    messages.extend(_history_to_messages(history))
    messages.append({"role": "user", "content": message.strip()})

    input_ids = tokenizer.apply_chat_template(
        _content_blocks(messages),
        add_generation_prompt=True,
        return_tensors="pt",
    ).to(model.device)
    start = input_ids.shape[-1]

    with torch.no_grad():
        output_ids = model.generate(
            input_ids,
            max_new_tokens=MAX_NEW_TOKENS,
            do_sample=True,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.1,
            pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id,
        )
    text = tokenizer.decode(output_ids[0][start:], skip_special_tokens=True)
    return _clean_response(text)

CSS = """
:root {
  --nb-ink: #202624;
  --nb-muted: #61706b;
  --nb-paper: #faf7f0;
  --nb-panel: #fffdf8;
  --nb-line: #d8ded2;
  --nb-sage: #6f8f7a;
  --nb-clay: #b36a4c;
  --nb-blue: #536d89;
}
body, .gradio-container {
  background: var(--nb-paper);
  color: var(--nb-ink);
  font-family: Inter, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
}
.nb-shell {
  max-width: 1120px;
  margin: 0 auto;
}
.nb-title {
  display: flex;
  align-items: baseline;
  justify-content: space-between;
  gap: 16px;
  padding: 18px 0 10px;
  border-bottom: 1px solid var(--nb-line);
}
.nb-title h1 {
  margin: 0;
  font-size: 28px;
  line-height: 1.1;
  letter-spacing: 0;
}
.nb-title p {
  margin: 0;
  color: var(--nb-muted);
  font-size: 14px;
}
.nb-layout {
  display: grid;
  grid-template-columns: minmax(0, 1fr) 280px;
  gap: 18px;
  margin-top: 18px;
}
.nb-side {
  border-left: 1px solid var(--nb-line);
  padding-left: 18px;
}
.nb-side h2 {
  font-size: 14px;
  margin: 0 0 10px;
}
.nb-side p {
  color: var(--nb-muted);
  font-size: 13px;
  line-height: 1.45;
}
.nb-chatbot {
  min-height: 580px;
  border: 1px solid var(--nb-line);
  background: var(--nb-panel);
}
.nb-chatbot .message {
  border-radius: 8px !important;
}
.nb-input textarea {
  border-radius: 8px !important;
}
.nb-examples button {
  border-radius: 8px !important;
  min-height: 38px;
  text-align: left;
}
.nb-controls {
  gap: 10px;
}
.nb-controls .wrap {
  border-radius: 8px !important;
}
button.primary {
  background: var(--nb-sage) !important;
  border-color: var(--nb-sage) !important;
}
@media (max-width: 860px) {
  .nb-title {
    display: block;
  }
  .nb-title p {
    margin-top: 8px;
  }
  .nb-layout {
    grid-template-columns: 1fr;
  }
  .nb-side {
    border-left: 0;
    border-top: 1px solid var(--nb-line);
    padding-left: 0;
    padding-top: 14px;
  }
}
"""

with gr.Blocks(css=CSS, theme=gr.themes.Soft(primary_hue="green", neutral_hue="stone")) as demo:
    gr.HTML(
        """

NeuroBait

ADHD-friendly task initiation, tuned for one tiny next move.

        """
    )
    with gr.Row(elem_classes=["nb-shell", "nb-layout"]):
        with gr.Column(scale=1):
            chatbot = gr.Chatbot(
                type="messages",
                height=580,
                show_copy_button=True,
                elem_classes=["nb-chatbot"],
            )
            message = gr.Textbox(
                placeholder="What are you avoiding right now?",
                lines=2,
                max_lines=5,
                show_label=False,
                elem_classes=["nb-input"],
            )
            with gr.Row():
                submit = gr.Button("Start", variant="primary")
                clear = gr.Button("Clear")
            gr.Examples(
                examples=[
                    "I need to finish a presentation tomorrow, but I keep avoiding the first slide.",
                    "My inbox is so messy that I freeze every time I open it.",
                    "I feel stuck and I don't know where to start.",
                ],
                inputs=message,
                elem_classes=["nb-examples"],
            )
        with gr.Column(scale=0, min_width=260, elem_classes=["nb-side"]):
            gr.HTML(
                """

Session feel

No streaks, shame, or productivity theatre. NeuroBait looks for the emotional anchor and turns it into a small action you can start now.

                """
            )
            with gr.Accordion("Generation", open=False):
                temperature = gr.Slider(0.2, 1.0, value=0.7, step=0.05, label="Temperature")
                top_p = gr.Slider(0.6, 1.0, value=0.9, step=0.05, label="Top p")

    def user_turn(user_message: str, chat_history: list[dict]) -> tuple[str, list[dict]]:
        if not user_message.strip():
            return "", chat_history
        return "", chat_history + [{"role": "user", "content": user_message.strip()}]

    def bot_turn(chat_history: list[dict], temperature_value: float, top_p_value: float) -> list[dict]:
        user_message = chat_history[-1]["content"]
        assistant_message = respond(user_message, chat_history[:-1], temperature_value, top_p_value)
        return chat_history + [{"role": "assistant", "content": assistant_message}]

    submit.click(user_turn, [message, chatbot], [message, chatbot], queue=False).then(
        bot_turn,
        [chatbot, temperature, top_p],
        chatbot,
    )
    message.submit(user_turn, [message, chatbot], [message, chatbot], queue=False).then(
        bot_turn,
        [chatbot, temperature, top_p],
        chatbot,
    )
    clear.click(lambda: [], outputs=chatbot, queue=False)

if __name__ == "__main__":
    demo.launch()


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
Success. Updated the following files:
A deploy/app.py


Input:
*** Begin Patch
*** Update File: deploy/requirements.txt
@@
 spaces
 gradio
 transformers==5.5.0
+peft==0.19.1
 accelerate
 torch
 bitsandbytes
+huggingface_hub
*** End Patch


Success. Updated the following files:
M deploy/requirements.txt

@@ -3,2 +3,3 @@
 transformers==5.5.0
+peft==0.19.1
 accelerate
@@ -6 +7,2 @@
 bitsandbytes
+huggingface_hub


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
Success. Updated the following files:
M deploy/requirements.txt


App Space sekarang dibuat chat-first: langsung input chat, contoh prompt English, panel kecil untuk generation settings, dan copy yang menghindari klaim medis. Berikutnya saya update kartu model dan README agar metadata upload tidak masih draft.


Input:
*** Begin Patch
*** Delete File: deploy/README.md
*** End Patch


Success. Updated the following files:
D deploy/README.md

# NeuroBait HF Space

This directory is the source for the Hugging Face Space repo. The Space should
remain separate from the GitHub source repo and the HF Model repo.

## Files

- `app.py`: Gradio chat app
- `requirements.txt`: Space dependencies

## Space Configuration

Set this environment variable in the HF Space:

```text
MODEL_ID=USER/neurobait-gemma4-26b-a4b
```

The model repo should point to the merged model intended for inference.

## Hardware

A merged bf16 Gemma 4 26B-A4B model is large. Use a GPU tier that can actually
load the final model, or switch the app to a quantized loading path before using
smaller hardware.

## App-Initiated Openers

Proactive openers belong in application logic. If the app wants to start with an
assistant message, inject that opener into chat history before the next user
turn. Do not ask the model to decide when to initiate contact.


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
Success. Updated the following files:
D deploy/README.md


Input:
*** Begin Patch
*** Add File: deploy/README.md
+---
+title: NeuroBait
+colorFrom: green
+colorTo: blue
+sdk: gradio
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: ADHD-friendly task initiation with one tiny next move.
+---
+
+# NeuroBait
+
+NeuroBait is an ADHD-friendly task-initiation assistant. It is designed to help
+the user find one tiny next move without shame, streak pressure, or a full
+productivity lecture.
+
+This Space loads:
+
+- Base model: `unsloth/gemma-4-26b-a4b-it`
+- LoRA adapter: `build-small-hackathon/NeuroBait`
+
+## Runtime
+
+The default app path uses 4-bit loading for the base model plus the NeuroBait
+LoRA adapter. The Space still needs a large GPU tier or ZeroGPU allocation that
+can load a 26B MoE model.
+
+Expected environment variables:
+
+```text
+BASE_MODEL=unsloth/gemma-4-26b-a4b-it
+ADAPTER_ID=build-small-hackathon/NeuroBait
+LOAD_IN_4BIT=1
+MAX_NEW_TOKENS=260
+```
+
+If the base model requires authentication in the Space runtime, add `HF_TOKEN`
+as a Space secret.
*** End Patch


Success. Updated the following files:
A deploy/README.md

---
title: NeuroBait
colorFrom: green
colorTo: blue
sdk: gradio
app_file: app.py
pinned: false
license: apache-2.0
short_description: ADHD-friendly task initiation with one tiny next move.
---

# NeuroBait

NeuroBait is an ADHD-friendly task-initiation assistant. It is designed to help
the user find one tiny next move without shame, streak pressure, or a full
productivity lecture.

This Space loads:

- Base model: `unsloth/gemma-4-26b-a4b-it`
- LoRA adapter: `build-small-hackathon/NeuroBait`

## Runtime

The default app path uses 4-bit loading for the base model plus the NeuroBait
LoRA adapter. The Space still needs a large GPU tier or ZeroGPU allocation that
can load a 26B MoE model.

Expected environment variables:

```text
BASE_MODEL=unsloth/gemma-4-26b-a4b-it
ADAPTER_ID=build-small-hackathon/NeuroBait
LOAD_IN_4BIT=1
MAX_NEW_TOKENS=260
```

If the base model requires authentication in the Space runtime, add `HF_TOKEN`
as a Space secret.


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
Success. Updated the following files:
A deploy/README.md


Input:
*** Begin Patch
*** Delete File: model-card/README.md
*** End Patch


Success. Updated the following files:
D model-card/README.md

# NeuroBait Gemma 4 26B-A4B

Draft model card for the Hugging Face Model repo.

## Model Summary

NeuroBait is a LoRA fine-tune of `unsloth/gemma-4-26b-a4b-it` for ADHD and
neurodivergent task initiation conversations. It is designed to produce warm,
short, agency-preserving prose that helps users start a task without turning the
conversation into a to-do list.

## Base Model

- Base: `unsloth/gemma-4-26b-a4b-it`
- Method: 16-bit LoRA
- License inheritance: verify from the base model card before publishing

## Training Data

The run #3 dataset is bilingual Indonesian/English:

- 270 train conversations
- 30 eval conversations
- multi-turn format with `messages[]`
- official NeuroBait system prompt prepended to each example

Dataset files are not included in the source repo or model repo unless explicitly
published later.

## Intended Use

This model is intended for a Gradio chat demo and research/product exploration
around ADHD-friendly task initiation support.

## Limitations

- Not a medical device.
- Not a replacement for professional mental-health support.
- May fail to infer the right motivator or deadline anchor from sparse context.
- English transfer must be evaluated separately for run #3.
- App-initiated reminders should be handled by app logic, not generated
  spontaneously by the model.

## Training Configuration

- LoRA rank: 16
- LoRA alpha: 16
- LoRA dropout: 0
- Epochs: 3
- Learning rate: 2e-4
- Effective batch size: 8
- Max sequence length: 2048
- Chat template: `gemma-4`
- Response-only markers: `<|turn>user\n` / `<|turn>model\n`

## Evaluation

Add final run #3 metrics and qualitative examples here after Modal eval.


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
Success. Updated the following files:
D model-card/README.md


Untuk model card saya masukkan hasil run #3 apa adanya: train/eval summary dan generation eval. Saya tidak akan mengklaim “medical” atau “clinically validated”; posisinya tetap demo riset produk untuk lomba.


Input:
*** Begin Patch
*** Add File: model-card/README.md
+---
+base_model: unsloth/gemma-4-26b-a4b-it
+library_name: peft
+pipeline_tag: text-generation
+tags:
+  - lora
+  - peft
+  - gemma-4
+  - adhd
+  - neurodivergent
+  - task-initiation
+  - build-small-hackathon
+license: other
+---
+
+# NeuroBait
+
+NeuroBait is a LoRA fine-tune of `unsloth/gemma-4-26b-a4b-it` for ADHD and
+neurodivergent task-initiation conversations. It is designed to produce warm,
+short, agency-preserving prose that helps a user start one tiny next move
+without turning the conversation into a full to-do list.
+
+This repo contains the LoRA adapter, tokenizer files, chat template, and the
+training summary for run #3. It is intended to be used by the Gradio Space:
+
+```text
+https://huggingface.co/spaces/build-small-hackathon/NeuroBait
+```
+
+## Intended Behavior
+
+NeuroBait should:
+
+- respond in short, natural prose,
+- avoid visible labels such as `Micro-action`, `Hook`, or `Stakes`,
+- avoid guilt framing,
+- preserve user agency,
+- ask one light question when context is too sparse,
+- offer one tiny concrete action when enough context exists.
+
+It should not act as a medical device, diagnostic tool, therapist, emergency
+support system, or replacement for professional care.
+
+## Training Data
+
+Run #3 used a bilingual Indonesian/English conversational dataset:
+
+- 270 train conversations
+- 30 eval conversations
+- multi-turn `messages[]` format
+- official NeuroBait system prompt prepended to each example
+
+The dataset is not included in this model repo.
+
+## Training Configuration
+
+- Base: `unsloth/gemma-4-26b-a4b-it`
+- Method: 16-bit LoRA
+- LoRA rank: 16
+- LoRA alpha: 16
+- LoRA dropout: 0
+- Target modules: q/k/v/o/gate/up/down projections
+- Epochs: 3
+- Learning rate: 2e-4
+- Effective batch size: 8
+- Max sequence length: 2048
+- Scheduler: cosine
+- Warmup ratio: 0.05
+- Optimizer: adamw 8-bit
+- Precision: bf16
+- Chat template: `gemma-4`
+- Response-only markers: `<|turn>user\n` / `<|turn>model\n`
+
+Training ran on Modal with an H100 80GB GPU.
+
+## Run #3 Results
+
+Training completed 102 steps.
+
+Training summary:
+
+- train conversations: 270
+- eval conversations: 30
+- train loss: 0.242
+- final `trainer.evaluate()` loss: 2.4044
+
+The loss signal should be treated as a weak training diagnostic for this project.
+NeuroBait is primarily evaluated through generated behavior against the base
+model.
+
+Generation eval summary over 8 held-out/novel prompts:
+
+- base persona average: 3.625 / 4
+- fine-tuned persona average: 4.0 / 4
+- base average words: 72.0
+- fine-tuned average words: 56.625
+- base label leaks: 1
+- fine-tuned label leaks: 0
+- base action-cue responses: 3
+- fine-tuned action-cue responses: 5
+
+Qualitatively, the fine-tuned adapter produced shorter, more conversational
+responses, maintained English on English prompts more reliably than the base
+model in this sample, and avoided literal structure labels in the tested turns.
+
+## Loading
+
+Example adapter loading path:
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+
+base_model = "unsloth/gemma-4-26b-a4b-it"
+adapter_id = "build-small-hackathon/NeuroBait"
+
+tokenizer = AutoTokenizer.from_pretrained(adapter_id)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    quantization_config=BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_compute_dtype=torch.bfloat16,
+        bnb_4bit_quant_type="nf4",
+    ),
+    device_map="auto",
+)
+model = PeftModel.from_pretrained(model, adapter_id)
+model.eval()
+```
+
+## Limitations
+
+- The model is not a medical or crisis-support system.
+- It may ask for a motivator or deadline when the user has already implied one.
+- It may still be too brief or too playful for some users.
+- Long-term personalization should be handled by product logic, not only by the
+  model.
+- App-initiated reminders should be handled by the app layer, not generated
+  spontaneously by the model.
+
+## Submission Context
+
+This model is part of the Build Small Model Hackathon submission for NeuroBait.
+The public GitHub source repo tracks training, evaluation, deployment scaffolding,
+and agent development notes.
*** End Patch