Output:
Chunk ID: e9994f
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2980
Output:
../ai-adhd-small-model-hf-2026/Engineering.md:13:[2] RunPod 1x RTX PRO 6000 96GB → Unsloth 16-bit LoRA  Gemma 4 26B-A4B   (~12 menit)
../ai-adhd-small-model-hf-2026/Engineering.md:31:| Base model | `unsloth/gemma-4-26b-a4b-it` | MoE 25.2B total / **3.8B aktif**, near-31B quality, **Apache 2.0** |
../ai-adhd-small-model-hf-2026/Engineering.md:34:| Lib | `transformers` 5.5.0, `trl` 0.24, `peft` 0.19.1, `unsloth` 2026.5.10 | + `bitsandbytes`, `datasets` |
../ai-adhd-small-model-hf-2026/Engineering.md:35:| torch | **2.10.0+cu128** | ⚠ WAJIB cu128 utk Blackwell **sm_120**; torch bawaan pod cu124 tak support |
../ai-adhd-small-model-hf-2026/Engineering.md:36:| Cloud GPU | **RunPod** 1x RTX PRO 6000 Blackwell 96GB | 188GB RAM, 32 vCPU |
../ai-adhd-small-model-hf-2026/Engineering.md:56:## 3. Fine-Tuning 16-bit LoRA di RunPod (step-by-step)
../ai-adhd-small-model-hf-2026/Engineering.md:58:### 3.1 Setup pod & venv
../ai-adhd-small-model-hf-2026/Engineering.md:60:⚠ **`/` (container) itu EPHEMERAL** — restart/resize pod meng-wipe `/root` (venv + cache base).
../ai-adhd-small-model-hf-2026/Engineering.md:62:reinstall venv via **`scripts/pod_setup_venv.sh`** (idempotent):
../ai-adhd-small-model-hf-2026/Engineering.md:65:# inti pod_setup_venv.sh — Blackwell sm_120 butuh torch cu128
../ai-adhd-small-model-hf-2026/Engineering.md:66:python3 -m venv /root/nb && source /root/nb/bin/activate
../ai-adhd-small-model-hf-2026/Engineering.md:67:pip install torch==2.10.0 --index-url https://download.pytorch.org/whl/cu128
../ai-adhd-small-model-hf-2026/Engineering.md:68:pip install --upgrade unsloth unsloth_zoo
../ai-adhd-small-model-hf-2026/Engineering.md:69:pip install --no-deps trl peft accelerate bitsandbytes transformers datasets
../ai-adhd-small-model-hf-2026/Engineering.md:70:# verifikasi: torch.cuda.get_device_capability() harus (12, x) = sm_120
../ai-adhd-small-model-hf-2026/Engineering.md:79:```python
../ai-adhd-small-model-hf-2026/Engineering.md:80:from unsloth import FastModel        # import unsloth SEBELUM transformers/trl/peft
../ai-adhd-small-model-hf-2026/Engineering.md:81:import torch
../ai-adhd-small-model-hf-2026/Engineering.md:85:    model_name = "unsloth/gemma-4-26b-a4b-it",
../ai-adhd-small-model-hf-2026/Engineering.md:94:    use_gradient_checkpointing="unsloth", random_state=42,
../ai-adhd-small-model-hf-2026/Engineering.md:97:from unsloth.chat_templates import get_chat_template
../ai-adhd-small-model-hf-2026/Engineering.md:108:from transformers import TrainingArguments
../ai-adhd-small-model-hf-2026/Engineering.md:122:from unsloth.chat_templates import train_on_responses_only
../ai-adhd-small-model-hf-2026/Engineering.md:162:⚠ **Bug wajib-tahu (transformers 5.5 / processor Gemma-4):** `apply_chat_template` minta `content`
../ai-adhd-small-model-hf-2026/Engineering.md:163:**multimodal** (list of parts), bukan string polos → `TypeError: string indices must be integers`.
../ai-adhd-small-model-hf-2026/Engineering.md:165:`eval_neurobait.py`). Wajib utk semua inference Gemma-4 di transformers 5.5+.
../ai-adhd-small-model-hf-2026/Engineering.md:172:Tarik JSON ke lokal, lalu: `python3 scripts/make_report.py outputs/eval_results_<stamp>.json` →
../ai-adhd-small-model-hf-2026/Engineering.md:192:```python
../ai-adhd-small-model-hf-2026/Engineering.md:199:> kalau nanti deploy via llama.cpp server; jalur utama deploy = HF Space GPU via transformers (§7).
../ai-adhd-small-model-hf-2026/Engineering.md:214:`transformers`. Push merged ke HF Hub dulu (mis. `USER/neurobait-gemma4-26b-a4b`).
../ai-adhd-small-model-hf-2026/Engineering.md:216:`requirements.txt`: `spaces gradio transformers accelerate torch`
../ai-adhd-small-model-hf-2026/Engineering.md:219:```python
../ai-adhd-small-model-hf-2026/Engineering.md:220:import spaces, torch, gradio as gr
../ai-adhd-small-model-hf-2026/Engineering.md:221:from transformers import AutoModelForCausalLM, AutoTokenizer
../ai-adhd-small-model-hf-2026/Engineering.md:225:model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype=torch.bfloat16, device_map="auto")
../ai-adhd-small-model-hf-2026/Engineering.md:227:def _mm(msgs):   # transformers 5.5 / Gemma-4: content harus list-of-parts
../ai-adhd-small-model-hf-2026/Engineering.md:255:- [ ] Deploy HF Space GPU (transformers + `@spaces.GPU`)
../ai-adhd-small-model-hf-2026/Engineering.md:261:- Unsloth docs: https://docs.unsloth.ai
../ai-adhd-small-model-hf-2026/Engineering.md:262:- Gemma 4 (model card / Unsloth): cari `unsloth/gemma-4-26b-a4b-it` di HF Hub
../ai-adhd-small-model-hf-2026/Engineering.md:263:- torch cu128 (Blackwell): https://download.pytorch.org/whl/cu128
../ai-adhd-small-model-hf-2026/log_train.md:10:**Status:** dataset v1 terkurasi & ter-split. Siap upload ke RunPod untuk QLoRA (M2).
../ai-adhd-small-model-hf-2026/log_train.md:65:python3 scripts/build_batch3.py     # regenerate raw_generated.jsonl (200, builtin)
../ai-adhd-small-model-hf-2026/log_train.md:66:python3 scripts/curate.py           # QC builtin+external (exit 0 = bersih) + tulis coverage.txt
../ai-adhd-small-model-hf-2026/log_train.md:67:python3 scripts/split_dataset.py    # train.jsonl + eval.jsonl
../ai-adhd-small-model-hf-2026/log_train.md:74:- Base model: `unsloth/gemma-3-12b-it`, QLoRA 4-bit (Engineering.md §3).
../ai-adhd-small-model-hf-2026/log_train.md:94:4. **QC:** `python3 scripts/curate.py` → harus `0 FAIL`. Perbaiki kalau ada pelanggaran.
../ai-adhd-small-model-hf-2026/log_train.md:95:5. **Split:** `python3 scripts/split_dataset.py` → `train.jsonl` + `eval.jsonl` ter-update
../ai-adhd-small-model-hf-2026/log_train.md:102:- *Split jalanin apa?* → `python3 scripts/split_dataset.py` (setelah `curate.py`). Tidak perlu
../ai-adhd-small-model-hf-2026/log_train.md:110:- **Base / metode:** `unsloth/gemma-4-26b-a4b-it` (MoE 25.2B total / 3.8B aktif, near-31B, **Apache 2.0**) — **16-bit LoRA** (`load_in_16bit=True`, BUKAN QLoRA 4-bit; Unsloth: QLoRA tak disarankan utk MoE).
../ai-adhd-small-model-hf-2026/log_train.md:113:- **Hardware (RunPod):** 1x **RTX PRO 6000 Blackwell 96GB**, 188GB RAM, 32 vCPU. Disk: container `/` 65GB + volume `/workspace` 20GB (margin ketat ~8GB; base bf16 ~50GB ke `/`, `/workspace` tak muat). Akses `ssh root@<ip> -p <port>`.
../ai-adhd-small-model-hf-2026/log_train.md:114:- **Env:** venv `/root/nb`. ⚠ torch bawaan pod 2.4.1+cu124 **TIDAK support Blackwell sm_120** → wajib reinstall **torch 2.10.0+cu128**. unsloth 2026.5.10, transformers 5.5.0, trl 0.24, peft 0.19.1.
../ai-adhd-small-model-hf-2026/log_train.md:120:- **Insiden:** setelah training selesai, user resize container disk 65→95GB → pod restart → `/` (ephemeral) ke-wipe (venv `/root/nb` + cache base 50GB HILANG). Adapter selamat (di `/workspace`). SSH port berubah 11545→11284. Untuk eval/lanjut: reinstall venv (torch cu128 + unsloth) + re-download base.
../ai-adhd-small-model-hf-2026/log_train.md:128:**Konteks:** lanjutan setelah laptop mati saat pod idle (training run #1 sudah selesai, adapter aman di `/workspace`). Pod di-resume: SSH `root@69.8.146.79 -p 11284` hidup, adapter utuh, tapi venv `/root/nb` ke-wipe (ephemeral `/`). **Reinstall venv** (torch 2.10.0+cu128 sm_120 Blackwell, unsloth 2026.5.10, transformers 5.5.0, trl 0.24, peft 0.19.1) via `scripts/pod_setup_venv.sh` → terverifikasi sm_120 OK. Base `unsloth/gemma-4-26b-a4b-it` re-download (~50GB ke `/`).
../ai-adhd-small-model-hf-2026/log_train.md:132:**Fix penting:** transformers 5.5 / processor Gemma-4 minta **content multimodal** (list of parts), bukan string polos → `TypeError: string indices must be integers`. Diperbaiki via helper `_mm()` (bungkus tiap content string → `[{"type":"text","text":...}]`). Patch wajib untuk semua inference Gemma-4 di transformers 5.5+.
../ai-adhd-small-model-hf-2026/log_train.md:179:  16-bit LoRA, torch cu128/sm_120, train_on_responses_only marker `<|turn>`, eval `eval_neurobait.py`
../ai-adhd-small-model-hf-2026/log_train.md:180:  + fix multimodal transformers 5.5, report `make_report.py` md/ipynb/pdf, deploy HF Space GPU).
../ai-adhd-small-model-hf-2026/log_train.md:181:- Script baru: `scripts/make_report.py`, `scripts/pod_setup_venv.sh`, `scripts/build_seeds.py`.
../ai-adhd-small-model-hf-2026/log_train.md:182:  `scripts/eval_neurobait.py` += dump JSON + helper `_mm()` (multimodal content fix).
../ai-adhd-small-model-hf-2026/log_train.md:217:**Konteks:** retrain pakai dataset conversational multi-turn (train 318 / eval 35) di **pod RunPod BARU**
../ai-adhd-small-model-hf-2026/log_train.md:218:(`ssh root@157.254.50.93 -p 11746`; pod lama di-migrasi RunPod → IP/volume baru). ⚠ **Volume `/workspace`
../ai-adhd-small-model-hf-2026/log_train.md:219:ikut KOSONG** (bukan cuma `/` ke-wipe) → venv, adapter, data, script semua hilang. Adapter run #1 aman
../ai-adhd-small-model-hf-2026/log_train.md:221:- **Setup ulang dari nol:** `pod_setup_venv.sh` → venv (torch 2.10.0+cu128 sm_120 OK; unsloth **2026.6.1**,
../ai-adhd-small-model-hf-2026/log_train.md:222:  transformers 5.5.0, trl 0.24, peft 0.19.1; FA2 broken→Xformers, harmless). Pre-download base ~50GB via
../ai-adhd-small-model-hf-2026/log_train.md:228:- **Base / metode:** `unsloth/gemma-4-26b-a4b-it`, 16-bit LoRA, r=16 alpha=16 dropout=0, 3 epoch (120 step),
../ai-adhd-small-model-hf-2026/log_train.md:232:- **⚠ BUG (unsloth 2026.6.1 + trl 0.24):** di akhir `trainer.train()` crash `_pickle.PicklingError:
../ai-adhd-small-model-hf-2026/log_train.md:233:  Can't pickle SFTConfig (not the same object)` saat `_save_checkpoint`→`torch.save(args)` → `model.
../ai-adhd-small-model-hf-2026/log_train.md:234:  save_pretrained()` final TAK jalan. **Penyelamatan:** transformers tulis bobot adapter SEBELUM baris
../ai-adhd-small-model-hf-2026/log_train.md:293:3. **Engineering — coba `vast.ai` (lebih murah dari RunPod).** Pindah/uji provider GPU vast.ai untuk hemat
../ai-adhd-small-model-hf-2026/log_train.md:294:   biaya. Catatan portabilitas: pipeline sudah idempotent — `pod_setup_venv.sh` (torch cu128 sm_120),
../ai-adhd-small-model-hf-2026/log_train.md:319:4. **QC:** `python3 scripts/curate.py` → wajib **0 FAIL** (cek: newline, label, alternating, type tag).
../ai-adhd-small-model-hf-2026/log_train.md:320:5. **INSERT ke dataset lama:** `python3 scripts/split_dataset.py` → regenerasi `train.jsonl` + `eval.jsonl`
../ai-adhd-small-model-hf-2026/log_train.md:322:6. **Retrain** (Engineering.md §3): resume pod RunPod → `bash pod_setup_venv.sh` (venv ephemeral,
../ai-adhd-small-model-hf-2026/log_train.md:323:   torch cu128) → scp `data/*.jsonl` ke `/workspace/data/` → jalankan `train_neurobait.py` (tmux) →
../ai-adhd-small-model-hf-2026/log_train.md:326:**Reminder infra:** pod RunPod **di-pause** (`ssh root@69.8.146.79 -p <port>` — port bisa berubah,
../ai-adhd-small-model-hf-2026/log_train.md:327:cek dashboard). venv `/root/nb` ke-wipe tiap restart → reinstall via `pod_setup_venv.sh`. Adapter run #1
../ai-adhd-small-model-hf-2026/log_train.md:339:- Hardware (RunPod): GPU= durasi= biaya=
../ai-adhd-small-model-hf-2026/Prepare_Dataset.md:527:- Unsloth Notebooks (resmi, Colab Qwen2.5): https://unsloth.ai/docs/get-started/unsloth-notebooks
../ai-adhd-small-model-hf-2026/Prepare_Dataset.md:528:- Qwen × Unsloth (format data & chat template): https://qwen.readthedocs.io/en/latest/training/unsloth.html
../ai-adhd-small-model-hf-2026/Prepare_Dataset.md:529:- QLoRA + Unsloth Complete Guide (Medium): https://medium.com/@matteo28/qlora-fine-tuning-with-unsloth-a-complete-guide-8652c9c7edb3
../ai-adhd-small-model-hf-2026/Prepare_Dataset.md:530:- Stable QLoRA Pipeline w/ Unsloth (MarkTechPost, 2026): https://www.marktechpost.com/2026/03/03/how-to-build-a-stable-and-efficient-qlora-fine-tuning-pipeline-using-unsloth-for-large-language-models/
../ai-adhd-small-model-hf-2026/Prepare_Dataset.md:556:6. Lanjut ke Engineering.md §3 (training QLoRA di RunPod).


Output:
Chunk ID: aa5f51
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1340
Output:
../ai-adhd-small-model-hf-2026/.claude/settings.local.json
../ai-adhd-small-model-hf-2026/.marscode/deviceInfo.json
../ai-adhd-small-model-hf-2026/Engineering.md
../ai-adhd-small-model-hf-2026/Framework_adjust_nuance_kimi.md
../ai-adhd-small-model-hf-2026/Plan.md
../ai-adhd-small-model-hf-2026/Prepare_Dataset.md
../ai-adhd-small-model-hf-2026/archive/Prepare_Dataset.md
../ai-adhd-small-model-hf-2026/archive/sample_train.jsonl
../ai-adhd-small-model-hf-2026/data/coverage.txt
../ai-adhd-small-model-hf-2026/data/eval.jsonl
../ai-adhd-small-model-hf-2026/data/eval_run2.jsonl.bak
../ai-adhd-small-model-hf-2026/data/prompts_situasi.txt
../ai-adhd-small-model-hf-2026/data/raw_generated.jsonl
../ai-adhd-small-model-hf-2026/data/sample_train.jsonl
../ai-adhd-small-model-hf-2026/data/train.jsonl
../ai-adhd-small-model-hf-2026/data/train_run2.jsonl.bak
../ai-adhd-small-model-hf-2026/log_train.md
../ai-adhd-small-model-hf-2026/mockup/comparison_desktop.png
../ai-adhd-small-model-hf-2026/mockup/comparison_mockup.html
../ai-adhd-small-model-hf-2026/mockup/comparison_mockup_social.html
../ai-adhd-small-model-hf-2026/mockup/comparison_social.png
../ai-adhd-small-model-hf-2026/mockup/mockup_v3_foundation.html
../ai-adhd-small-model-hf-2026/mockup/mockup_v3_foundation.png
../ai-adhd-small-model-hf-2026/mockup/mockup_v4_autopsy.html
../ai-adhd-small-model-hf-2026/mockup/mockup_v4_autopsy.png
../ai-adhd-small-model-hf-2026/mockup/mockup_v5_research_informed.html
../ai-adhd-small-model-hf-2026/neurobait-lora-run2/README.md
../ai-adhd-small-model-hf-2026/neurobait-lora-run2/adapter_config.json
../ai-adhd-small-model-hf-2026/neurobait-lora-run2/adapter_model.safetensors
../ai-adhd-small-model-hf-2026/neurobait-lora-run2/chat_template.jinja
../ai-adhd-small-model-hf-2026/neurobait-lora-run2/processor_config.json
../ai-adhd-small-model-hf-2026/neurobait-lora-run2/tokenizer.json
../ai-adhd-small-model-hf-2026/neurobait-lora-run2/tokenizer_config.json
../ai-adhd-small-model-hf-2026/neurobait-lora-run2/training_args.bin
../ai-adhd-small-model-hf-2026/neurobait-lora/README.md
../ai-adhd-small-model-hf-2026/neurobait-lora/adapter_config.json
../ai-adhd-small-model-hf-2026/neurobait-lora/adapter_model.safetensors
../ai-adhd-small-model-hf-2026/neurobait-lora/chat_template.jinja
../ai-adhd-small-model-hf-2026/neurobait-lora/processor_config.json
../ai-adhd-small-model-hf-2026/neurobait-lora/tokenizer.json
../ai-adhd-small-model-hf-2026/neurobait-lora/tokenizer_config.json
../ai-adhd-small-model-hf-2026/neurobait_logo_1.png
../ai-adhd-small-model-hf-2026/outputs/eval_report.md
../ai-adhd-small-model-hf-2026/outputs/eval_report_20260603_0409.ipynb
../ai-adhd-small-model-hf-2026/outputs/eval_report_20260603_0409.pdf
../ai-adhd-small-model-hf-2026/outputs/eval_report_20260603_2218.ipynb
../ai-adhd-small-model-hf-2026/outputs/eval_report_20260603_2218.pdf
../ai-adhd-small-model-hf-2026/outputs/eval_results_20260603_0409.csv
../ai-adhd-small-model-hf-2026/outputs/eval_results_20260603_0409.json
../ai-adhd-small-model-hf-2026/outputs/eval_results_20260603_2218.json
../ai-adhd-small-model-hf-2026/prompts/generate_dataset.md
../ai-adhd-small-model-hf-2026/qwen_agent_finetuned/README.md
../ai-adhd-small-model-hf-2026/qwen_agent_finetuned/adapter_config.json
../ai-adhd-small-model-hf-2026/qwen_agent_finetuned/adapter_model.safetensors
../ai-adhd-small-model-hf-2026/qwen_agent_finetuned/added_tokens.json
../ai-adhd-small-model-hf-2026/qwen_agent_finetuned/agent_metadata.json
../ai-adhd-small-model-hf-2026/qwen_agent_finetuned/chat_template.jinja
../ai-adhd-small-model-hf-2026/qwen_agent_finetuned/knowledge_combined.csv
../ai-adhd-small-model-hf-2026/qwen_agent_finetuned/knowledge_extended.csv
../ai-adhd-small-model-hf-2026/qwen_agent_finetuned/knowledge_original.csv
../ai-adhd-small-model-hf-2026/qwen_agent_finetuned/merges.txt
../ai-adhd-small-model-hf-2026/qwen_agent_finetuned/soul_system_prompt.md
../ai-adhd-small-model-hf-2026/qwen_agent_finetuned/special_tokens_map.json
../ai-adhd-small-model-hf-2026/qwen_agent_finetuned/tokenizer.json
../ai-adhd-small-model-hf-2026/qwen_agent_finetuned/tokenizer_config.json
../ai-adhd-small-model-hf-2026/qwen_agent_finetuned/vocab.json
../ai-adhd-small-model-hf-2026/research-design/Section-1-Usia.md
../ai-adhd-small-model-hf-2026/research-design/Section-2-Gender.md
../ai-adhd-small-model-hf-2026/research-design/Section-3-Budaya.md
../ai-adhd-small-model-hf-2026/research-design/Section-4-Zaman.md
../ai-adhd-small-model-hf-2026/scripts/batch2_data.py
../ai-adhd-small-model-hf-2026/scripts/batch3_data.py
../ai-adhd-small-model-hf-2026/scripts/build_batch1.py
../ai-adhd-small-model-hf-2026/scripts/build_batch2.py
../ai-adhd-small-model-hf-2026/scripts/build_batch3.py
../ai-adhd-small-model-hf-2026/scripts/build_sample.py
../ai-adhd-small-model-hf-2026/scripts/build_seeds.py
../ai-adhd-small-model-hf-2026/scripts/curate.py
../ai-adhd-small-model-hf-2026/scripts/dataset_common.py
../ai-adhd-small-model-hf-2026/scripts/eval_neurobait.py
../ai-adhd-small-model-hf-2026/scripts/extend_short_convos.py
../ai-adhd-small-model-hf-2026/scripts/make_report.py
../ai-adhd-small-model-hf-2026/scripts/pod_setup_venv.sh
../ai-adhd-small-model-hf-2026/scripts/split_dataset.py
../ai-adhd-small-model-hf-2026/scripts/train_neurobait.py
../ai-adhd-small-model-hf-2026/tmp/adjust_nuance.py


Temuannya: upstream tidak menyimpan virtualenv lokal di repo, tapi ada script setup venv RunPod dan log mencatat environment yang dipakai. Saya baca scriptnya sebentar supaya jawabannya presisi.


sed -n '1,220p' ../ai-adhd-small-model-hf-2026/scripts/pod_setup_venv.sh


sed -n '56,75p' ../ai-adhd-small-model-hf-2026/Engineering.md && sed -n '110,130p' ../ai-adhd-small-model-hf-2026/log_train.md && sed -n '217,224p' ../ai-adhd-small-model-hf-2026/log_train.md


Output:
Chunk ID: abfdab
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 400
Output:
#!/usr/bin/env bash
# Reinstall venv di pod RunPod (RTX PRO 6000 Blackwell sm_120) untuk eval NeuroBait.
# Blackwell WAJIB torch cu128; torch bawaan pod (cu124) tak support sm_120.
# Idempotent-ish: hapus venv lama lalu bangun ulang. Log ke stdout (dipanggil via nohup).
set -euo pipefail

VENV=/root/nb
echo "=== [$(date +%T)] setup venv mulai ==="

# tmux untuk menjalankan eval tahan-putus (laptop bisa mati lagi)
command -v tmux >/dev/null 2>&1 || { apt-get update -qq && apt-get install -y -qq tmux; }

rm -rf "$VENV"
python3 -m venv "$VENV"
# shellcheck disable=SC1091
source "$VENV/bin/activate"
python -m pip install --upgrade -q pip wheel

echo "=== [$(date +%T)] install torch 2.10.0 cu128 ==="
pip install -q torch==2.10.0 --index-url https://download.pytorch.org/whl/cu128

echo "=== [$(date +%T)] install unsloth stack ==="
pip install -q --upgrade unsloth unsloth_zoo
pip install -q --no-deps trl peft accelerate bitsandbytes transformers datasets

echo "=== [$(date +%T)] verifikasi Blackwell ==="
python - <<'PY'
import torch
print("torch", torch.__version__, "cuda", torch.version.cuda)
print("cuda available:", torch.cuda.is_available())
if torch.cuda.is_available():
    cap = torch.cuda.get_device_capability()
    print("device:", torch.cuda.get_device_name(0), "sm_%d%d" % cap)
    assert cap[0] >= 12, "WARNING: torch tak mendukung sm_120 (Blackwell)"
import transformers, trl, peft, unsloth
print("transformers", transformers.__version__, "trl", trl.__version__,
      "peft", peft.__version__, "unsloth", unsloth.__version__)
PY

echo "=== [$(date +%T)] SETUP_VENV_DONE ==="


Output:
Chunk ID: 71b70a
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1263
Output:
## 3. Fine-Tuning 16-bit LoRA di RunPod (step-by-step)

### 3.1 Setup pod & venv

⚠ **`/` (container) itu EPHEMERAL** — restart/resize pod meng-wipe `/root` (venv + cache base).
Volume **`/workspace` persistent** (adapter & data aman di sana). Sesudah resume pod:
reinstall venv via **`scripts/pod_setup_venv.sh`** (idempotent):

```bash
# inti pod_setup_venv.sh — Blackwell sm_120 butuh torch cu128
python3 -m venv /root/nb && source /root/nb/bin/activate
pip install torch==2.10.0 --index-url https://download.pytorch.org/whl/cu128
pip install --upgrade unsloth unsloth_zoo
pip install --no-deps trl peft accelerate bitsandbytes transformers datasets
# verifikasi: torch.cuda.get_device_capability() harus (12, x) = sm_120
```

- Disk: base bf16 ~50GB ke `/` (set container disk cukup, ~95GB). Base auto-download saat load.
- Akses: `ssh root@<ip> -p <port>` (port bisa berubah tiap restart — cek dashboard).
- Jalankan training/eval dalam **`tmux`** supaya tahan putus koneksi.
- **Base / metode:** `unsloth/gemma-4-26b-a4b-it` (MoE 25.2B total / 3.8B aktif, near-31B, **Apache 2.0**) — **16-bit LoRA** (`load_in_16bit=True`, BUKAN QLoRA 4-bit; Unsloth: QLoRA tak disarankan utk MoE).
- **Hyperparam:** r=16 alpha=16 dropout=0 epoch=3 lr=2e-4 batch=1 grad_accum=8 (eff 8) max_seq=2048, optim=adamw_8bit, cosine, warmup_ratio=0.05.
- **Chat template:** `gemma-4`; `train_on_responses_only` marker Gemma 4 = `<|turn>user\n` / `<|turn>model\n` (⚠ BUKAN `<start_of_turn>` spt Gemma 3 — diverifikasi via pre-flight). System role didukung.
- **Hardware (RunPod):** 1x **RTX PRO 6000 Blackwell 96GB**, 188GB RAM, 32 vCPU. Disk: container `/` 65GB + volume `/workspace` 20GB (margin ketat ~8GB; base bf16 ~50GB ke `/`, `/workspace` tak muat). Akses `ssh root@<ip> -p <port>`.
- **Env:** venv `/root/nb`. ⚠ torch bawaan pod 2.4.1+cu124 **TIDAK support Blackwell sm_120** → wajib reinstall **torch 2.10.0+cu128**. unsloth 2026.5.10, transformers 5.5.0, trl 0.24, peft 0.19.1.
- **Data:** `/workspace/data/{train.jsonl 180, eval.jsonl 20}`. Script: `/workspace/train_neurobait.py`. Output adapter → `/workspace/neurobait-lora`.
- **Loss:** train_final=**0.257**  eval_loss=**1.83** (69 steps, 3 epoch). ⚠ Gap lebar → indikasi **overfit/memorisasi**; eval_loss lemah utk task gaya, jadi WAJIB tes kualitatif sebelum percaya. Kandidat fix kalau kaku: turunkan ke 1-2 epoch / tambah data.
- **Durasi:** ~11.6 menit (698s) training murni. Biaya GPU ~$0.3-0.5 utk training (di luar waktu setup/idle).
- **Deploy:** 26B GGUF ~15GB → TIDAK jalan di Ollama lokal (4GB VRAM) → deploy **HF Space ber-GPU** (`@spaces.GPU`).
- **Artefak:** adapter `/workspace/neurobait-lora` (1.98GB) + **backup lokal** `./neurobait-lora/`. Belum push HF Hub.
- **Insiden:** setelah training selesai, user resize container disk 65→95GB → pod restart → `/` (ephemeral) ke-wipe (venv `/root/nb` + cache base 50GB HILANG). Adapter selamat (di `/workspace`). SSH port berubah 11545→11284. Untuk eval/lanjut: reinstall venv (torch cu128 + unsloth) + re-download base.
- **Catatan teknis:** dataset format `messages` (system/user/assistant) langsung dipakai; `.removeprefix("<bos>")` pada hasil apply_chat_template.
- **Eval:** script siap di `scripts/eval_neurobait.py` (before/after base vs FT via `disable_adapter()`, held-out eval.jsonl + prompt ADHD baru, metrik gaya NeuroBait). Meniru `qwen_agent_finetuned/notebooks/Haris_Qwen7B_Eval_Agent_v4.ipynb`.

---

## 2026-06-03 — Eval run #1 (SELESAI ✅) — base vs fine-tuned

**Konteks:** lanjutan setelah laptop mati saat pod idle (training run #1 sudah selesai, adapter aman di `/workspace`). Pod di-resume: SSH `root@69.8.146.79 -p 11284` hidup, adapter utuh, tapi venv `/root/nb` ke-wipe (ephemeral `/`). **Reinstall venv** (torch 2.10.0+cu128 sm_120 Blackwell, unsloth 2026.5.10, transformers 5.5.0, trl 0.24, peft 0.19.1) via `scripts/pod_setup_venv.sh` → terverifikasi sm_120 OK. Base `unsloth/gemma-4-26b-a4b-it` re-download (~50GB ke `/`).

**Eval:** `scripts/eval_neurobait.py` (1x load, toggle base via `disable_adapter()`), 26 prompt = 20 held-out (`eval.jsonl`, ada ref) + 6 novel. Output di `/workspace/outputs/`: `eval_report.md`, `eval_results_20260603_0409.{csv,json}`.
**Konteks:** retrain pakai dataset conversational multi-turn (train 318 / eval 35) di **pod RunPod BARU**
(`ssh root@157.254.50.93 -p 11746`; pod lama di-migrasi RunPod → IP/volume baru). ⚠ **Volume `/workspace`
ikut KOSONG** (bukan cuma `/` ke-wipe) → venv, adapter, data, script semua hilang. Adapter run #1 aman
di backup lokal `./neurobait-lora/`.
- **Setup ulang dari nol:** `pod_setup_venv.sh` → venv (torch 2.10.0+cu128 sm_120 OK; unsloth **2026.6.1**,
  transformers 5.5.0, trl 0.24, peft 0.19.1; FA2 broken→Xformers, harmless). Pre-download base ~50GB via
  `snapshot_download` (Xet, ~3 mnt). ⚠ **KOREKSI layout disk (kebalik dari pod lama):** pod baru `/`=95GB
  vs `/workspace`=20GB → base bf16 ke `/` (default HF cache); **JANGAN set HF_HOME=/workspace** (tak muat).


Ada, tapi **bukan venv lokal yang tersimpan di repo**.

Hasil cek di `../ai-adhd-small-model-hf-2026`:
- Tidak ada `.venv/`, `venv/`, `env/`, `pyvenv.cfg`, `requirements.txt`, `pyproject.toml`, `poetry.lock`, atau `uv.lock`.
- Environment yang pernah dipakai ada di **RunPod**, path-nya: `/root/nb`.
- Setup-nya direkam di [scripts/pod_setup_venv.sh](/media/haris-subrata/Work/llm/ai-adhd-small-model-hf-2026/scripts/pod_setup_venv.sh:1).

Isi penting venv RunPod lama:
```bash
python3 -m venv /root/nb
source /root/nb/bin/activate
pip install torch==2.10.0 --index-url https://download.pytorch.org/whl/cu128
pip install --upgrade unsloth unsloth_zoo
pip install --no-deps trl peft accelerate bitsandbytes transformers datasets
```

Versi yang tercatat:
- Run #1: `torch 2.10.0+cu128`, `unsloth 2026.5.10`, `transformers 5.5.0`, `trl 0.24`, `peft 0.19.1`
- Run #2: `unsloth 2026.6.1`, `transformers 5.5.0`, `trl 0.24`, `peft 0.19.1`

Catatan penting: venv `/root/nb` itu ephemeral di RunPod, dan pernah hilang saat pod restart/resize. Jadi untuk repo baru ini, kita sebaiknya **tidak bergantung pada venv lama**, tapi mengambil dependency spec-nya sebagai referensi. Untuk Modal H100 nanti juga tidak perlu `torch cu128`; itu khusus Blackwell RunPod `sm_120`.


# 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


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


Saya cek cepat interpreter Python dan venv yang tersedia di mesin ini, lalu saya rekomendasikan opsi paling praktis untuk development lokal. Untuk training GPU tetap akan dikunci di image Modal, jadi venv lokal hanya untuk edit, lint, validasi dataset, dan script ringan.


find /media/haris-subrata/Work/llm -maxdepth 3 \( -type f -name pyvenv.cfg -o -type d -name '.venv' -o -type d -name 'venv' -o -type d -name 'env' \)


ls -la ~/environment 2>/dev/null || true


Output:
Chunk ID: 32eadd
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 8
Output:
/usr/bin/python3
Python 3.10.12


Output:
Chunk ID: 85419b
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 29
Output:
/media/haris-subrata/Work/llm/agent_analytics/venv
/media/haris-subrata/Work/llm/agent_analytics/venv/pyvenv.cfg


Output:
Chunk ID: 32babf
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 66
Output:
total 16
drwxrwxr-x  4 haris-subrata haris-subrata 4096 Aug  8  2025 .
drwxr-x--- 85 haris-subrata haris-subrata 4096 Jun  8 17:01 ..
drwxrwxr-x 10 haris-subrata haris-subrata 4096 Sep 20  2022 py09
drwxrwxr-x  8 haris-subrata haris-subrata 4096 Jun 26  2024 py10


Ada `python3` sistem 3.10.12, satu venv proyek lain, dan dua environment global user (`py09`, `py10`). Saya cek versi dan paket kunci di env yang tersedia sebelum memutuskan apakah perlu venv baru.


/media/haris-subrata/Work/llm/agent_analytics/venv/bin/python --version && /media/haris-subrata/Work/llm/agent_analytics/venv/bin/python -m pip list | sed -n '1,80p'


~/environment/py10/bin/python --version && ~/environment/py10/bin/python -m pip list | rg 'modal|gradio|torch|transformers|datasets|reportlab|matplotlib|huggingface|unsloth|trl|peft'


~/environment/py09/bin/python --version && ~/environment/py09/bin/python -m pip list | rg 'modal|gradio|torch|transformers|datasets|reportlab|matplotlib|huggingface|unsloth|trl|peft'


Output:
Chunk ID: 9cb49b
Wall time: 0.8197 seconds
Process exited with code 0
Original token count: 266
Output:
Python 3.10.12
WARNING: The directory '/home/haris-subrata/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you should use sudo's -H flag.
Package           Version
----------------- -----------
annotated-doc     0.0.4
annotated-types   0.7.0
anyio             4.12.1
click             8.3.1
exceptiongroup    1.3.1
fastapi           0.128.0
h11               0.16.0
idna              3.11
Jinja2            3.1.6
MarkupSafe        3.0.3
numpy             2.2.6
pandas            2.3.3
pip               22.0.2
pydantic          2.12.5
pydantic_core     2.41.5
pydantic-settings 2.12.0
python-dateutil   2.9.0.post0
python-dotenv     1.2.1
python-multipart  0.0.22
pytz              2025.2
PyYAML            6.0.3
scipy             1.15.3
setuptools        59.6.0
six               1.17.0
starlette         0.50.0
typing_extensions 4.15.0
typing-inspection 0.4.2
tzdata            2025.3
uvicorn           0.40.0


Output:
Chunk ID: d1db3d
Wall time: 1.6403 seconds
Process exited with code 0
Original token count: 168
Output:
Python 3.10.12
WARNING: The directory '/home/haris-subrata/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you should use sudo's -H flag.
datasets                        3.0.1
huggingface-hub                 0.25.2
matplotlib                      3.6.1
matplotlib-inline               0.1.6
reportlab                       4.5.1
sentence-transformers           3.2.0
torch                           1.12.1
torchreid                       1.4.0
torchvision                     0.13.1
transformers                    4.45.2


Output:
Chunk ID: ae7f23
Wall time: 2.3324 seconds
Process exited with code 0
Original token count: 195
Output:
Python 3.9.18
WARNING: The directory '/home/haris-subrata/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you should use sudo's -H flag.
datasets                           3.3.2
gradio                             3.4
huggingface-hub                    0.25.2
matplotlib                         3.5.3
matplotlib-inline                  0.1.6
matplotlib-venn                    1.1.1
pytorch_revgrad                    0.2.0
reportlab                          4.4.10
sentence-transformers              2.6.1
torch                              2.0.0
torchvision                        0.15.2
transformers                       4.45.2


Untuk proyek ini, **lebih baik buat venv lokal sendiri** di repo ini: `.venv/`.

Alasannya:
- Env yang tersedia (`~/environment/py09`, `~/environment/py10`) paketnya sudah lama untuk stack ini: `transformers 4.45.2`, sementara training target pakai `transformers 5.5.0`.
- `agent_analytics/venv` milik proyek lain, sebaiknya tidak dicampur.
- Venv lokal cukup untuk development ringan: syntax check, dataset validation, report rendering, Modal CLI, HF Hub tooling.
- Training sebenarnya tetap jalan di **Modal image**, jadi venv lokal tidak perlu install full GPU stack berat seperti Unsloth + torch CUDA kecuali diperlukan.

Rekomendasi setup lokal:
```bash
python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip wheel
pip install modal huggingface_hub datasets gradio reportlab matplotlib
```

Untuk `unsloth`, `torch`, `trl`, `peft`, `bitsandbytes`, `transformers==5.5.0`, saya akan pin di `modal_train.py` / dokumen Modal, bukan wajib dipasang lokal. Ini menjaga development env ringan dan tidak bentrok dengan CUDA lokal.

Saya bisa lanjut scaffold dengan asumsi `.venv/` lokal dipakai untuk dev, dan `.venv/` masuk `.gitignore`.


Summary: auto


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


Oke, saya akan pakai `/media/haris-subrata/Work/llm/agent_analytics/venv/bin/python` sebagai Python dev env proyek ini. Saya tidak akan install paket dulu kecuali ada check yang benar-benar butuh dependency baru; sekarang cukup susun file dan validasi syntax ringan.


sed -n '1,220p' ../ai-adhd-small-model-hf-2026/scripts/train_neurobait.py


Output:
Chunk ID: 153cd0
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 4
Output:
Python 3.10.12


Output:
Chunk ID: f6c0dc
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 50
Output:
  119 ../ai-adhd-small-model-hf-2026/scripts/train_neurobait.py
  375 ../ai-adhd-small-model-hf-2026/scripts/eval_neurobait.py
  186 ../ai-adhd-small-model-hf-2026/scripts/make_report.py
  680 total


Output:
Chunk ID: 3eebe1
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1451
Output:
"""NeuroBait — fine-tune 16-bit LoRA Gemma 4 26B-A4B di RunPod (Unsloth).

Direkonstruksi dari Engineering.md §3.2 (spec LOCKED) + log_train.md M2 run #1.
Keputusan LOCKED (jangan diubah tanpa alasan — lihat Engineering.md §1/§3.4):
  - base `unsloth/gemma-4-26b-a4b-it`, 16-bit LoRA (BUKAN QLoRA 4-bit; MoE)
  - r=16 alpha=16 dropout=0, 7 target modules
  - 3 epoch, lr=2e-4, batch=1 grad_accum=8 (eff 8), max_seq=2048, cosine, warmup 0.05
  - chat_template "gemma-4"; train_on_responses_only marker <|turn>user / <|turn>model
    (⚠ BUKAN <start_of_turn> spt Gemma 3 — diverifikasi via pre-flight di bawah)

Jalankan di pod (dalam tmux):
  source /root/nb/bin/activate
  python /workspace/train_neurobait.py
Output adapter → /workspace/neurobait-lora (lalu backup ke lokal via scp).
"""
import os, json
from unsloth import FastModel            # import unsloth SEBELUM transformers/trl/peft
import torch

# ---------- config (env-overridable; default = layout pod /workspace) ----------
BASE_MODEL  = os.environ.get("BASE_MODEL",  "unsloth/gemma-4-26b-a4b-it")
TRAIN_FILE  = os.environ.get("TRAIN_FILE",  "/workspace/data/train.jsonl")
EVAL_FILE   = os.environ.get("EVAL_FILE",   "/workspace/data/eval.jsonl")
OUT_ADAPTER = os.environ.get("OUT_ADAPTER", "/workspace/neurobait-lora")
OUT_DIR     = os.environ.get("OUT_DIR",     "/workspace/outputs")
EPOCHS      = float(os.environ.get("EPOCHS", "3"))
MAX_SEQ     = int(os.environ.get("MAX_SEQ", "2048"))
os.makedirs(OUT_DIR, exist_ok=True)

# ---------- 1) load base (16-bit LoRA) ----------
print(f">>> loading base {BASE_MODEL} (16-bit LoRA)", flush=True)
model, tokenizer = FastModel.from_pretrained(
    model_name      = BASE_MODEL,
    max_seq_length  = MAX_SEQ,
    load_in_4bit    = False,
    load_in_16bit   = True,              # MoE: jangan QLoRA 4-bit (Unsloth)
    full_finetuning = False,
)
model = FastModel.get_peft_model(
    model, r=16, lora_alpha=16, lora_dropout=0,
    target_modules=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"],
    use_gradient_checkpointing="unsloth", random_state=42,
)

from unsloth.chat_templates import get_chat_template
tokenizer = get_chat_template(tokenizer, chat_template="gemma-4")   # ⚠ gemma-4, bukan gemma-3

# ---------- 2) data ----------
from datasets import load_dataset
def fmt(ex):
    txt = tokenizer.apply_chat_template(ex["messages"], tokenize=False, add_generation_prompt=False)
    return {"text": txt.removeprefix("<bos>")}     # hindari <bos> ganda

train_ds = load_dataset("json", data_files=TRAIN_FILE, split="train").map(fmt)
eval_ds  = None
if os.path.exists(EVAL_FILE):
    eval_ds = load_dataset("json", data_files=EVAL_FILE, split="train").map(fmt)
print(f">>> train={len(train_ds)}" + (f" eval={len(eval_ds)}" if eval_ds is not None else " (tanpa eval set)"), flush=True)

# ---------- pre-flight: verifikasi marker train_on_responses_only ----------
# Gemma 4 pakai <|turn>user / <|turn>model. Kalau template berubah, mask salah → loss kacau.
_sample = train_ds[0]["text"]
INSTR_PART, RESP_PART = "<|turn>user\n", "<|turn>model\n"
assert INSTR_PART in _sample and RESP_PART in _sample, (
    "PRE-FLIGHT GAGAL: marker <|turn>user/<|turn>model tak ditemukan di sample ter-render. "
    "Cek chat_template Gemma 4 sebelum lanjut (jangan pakai <start_of_turn>).")
print(">>> pre-flight marker OK:", repr(INSTR_PART), repr(RESP_PART), flush=True)

# ---------- 3) trainer ----------
from trl import SFTTrainer
from transformers import TrainingArguments

targs = dict(
    per_device_train_batch_size=1, gradient_accumulation_steps=8,   # eff batch 8
    num_train_epochs=EPOCHS, learning_rate=2e-4,
    lr_scheduler_type="cosine", warmup_ratio=0.05,
    bf16=True, optim="adamw_8bit", logging_steps=1,
    output_dir=OUT_DIR, report_to="none", seed=42,
    # ⚠ save_strategy="no": cegah checkpoint mid-training. unsloth 2026.6.1 + trl 0.24
    # crash di _save_checkpoint → torch.save(SFTConfig) ("Can't pickle SFTConfig: not the
    # same object"). Bobot final tetap aman lewat model.save_pretrained() di akhir (PEFT,
    # tak pickle args). Run #2 (2026-06-04) kena bug ini; adapter terselamatkan dari checkpoint-120.
    save_strategy="no",
)
if eval_ds is not None:
    targs.update(per_device_eval_batch_size=1, eval_strategy="epoch")

trainer = SFTTrainer(
    model=model, tokenizer=tokenizer,
    train_dataset=train_ds, eval_dataset=eval_ds,
    dataset_text_field="text", max_seq_length=MAX_SEQ,
    args=TrainingArguments(**targs),
)

# train_on_responses_only — mask: hanya token sesudah <|turn>model\n yang dihitung loss.
# Untuk multi-turn, SETIAP giliran assistant jadi target (lihat Prepare_Dataset §2.7).
from unsloth.chat_templates import train_on_responses_only
trainer = train_on_responses_only(
    trainer, instruction_part=INSTR_PART, response_part=RESP_PART)

# ---------- 4) train ----------
stats = trainer.train()
train_loss = stats.metrics.get("train_loss")
print(f">>> train selesai. train_loss={train_loss}", flush=True)

eval_metrics = {}
if eval_ds is not None:
    eval_metrics = trainer.evaluate()
    print(">>> eval:", json.dumps(eval_metrics, indent=2), flush=True)

# ---------- 5) save adapter ----------
model.save_pretrained(OUT_ADAPTER)
tokenizer.save_pretrained(OUT_ADAPTER)
with open(os.path.join(OUT_ADAPTER, "train_summary.json"), "w", encoding="utf-8") as f:
    json.dump({"base": BASE_MODEL, "epochs": EPOCHS, "max_seq": MAX_SEQ,
               "n_train": len(train_ds), "n_eval": len(eval_ds) if eval_ds is not None else 0,
               "train_loss": train_loss, "eval": eval_metrics}, f, ensure_ascii=False, indent=2)
print(f">>> adapter tersimpan → {OUT_ADAPTER}", flush=True)
print(">>> JANGAN lupa scp backup ke lokal & pause pod setelah eval.", flush=True)


Saya akan mulai dari file inti dulu: `.gitignore`, training core, dan wrapper Modal. Desainnya: `train_core.py` tidak menjalankan apa pun saat di-import, sehingga bisa dipakai ulang oleh Modal train/eval/merge function tanpa efek samping.


Input:
*** Begin Patch
*** Add File: .gitignore
+# Dataset is handed over out-of-band.
+data/
+*.jsonl
+
+# Model artifacts.
+*.safetensors
+*.gguf
+neurobait-lora*/
+merged*/
+outputs/
+
+# Local development.
+.venv/
+__pycache__/
+*.pyc
+.env
+
+# Hugging Face / Modal local caches.
+.cache/
+.modal/
*** Add File: train/train_core.py
+"""Core NeuroBait fine-tuning logic.
+
+This module is intentionally import-safe: call ``train_adapter()`` from Modal or
+from a GPU box. Unsloth and other heavy libraries are imported inside the
+function so local development can still run syntax checks without the GPU stack.
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from dataclasses import asdict, dataclass
+from pathlib import Path
+from typing import Any
+
+
+BASE_MODEL = "unsloth/gemma-4-26b-a4b-it"
+INSTRUCTION_PART = "<|turn>user\n"
+RESPONSE_PART = "<|turn>model\n"
+
+
+@dataclass(frozen=True)
+class TrainConfig:
+    base_model: str = BASE_MODEL
+    train_file: str = "/data/train.jsonl"
+    eval_file: str = "/data/eval.jsonl"
+    out_adapter: str = "/out/neurobait-lora-run3"
+    out_dir: str = "/out/outputs"
+    epochs: float = 3.0
+    max_seq: int = 2048
+    seed: int = 42
+
+    @classmethod
+    def from_env(cls) -> "TrainConfig":
+        return cls(
+            base_model=os.environ.get("BASE_MODEL", cls.base_model),
+            train_file=os.environ.get("TRAIN_FILE", cls.train_file),
+            eval_file=os.environ.get("EVAL_FILE", cls.eval_file),
+            out_adapter=os.environ.get("OUT_ADAPTER", cls.out_adapter),
+            out_dir=os.environ.get("OUT_DIR", cls.out_dir),
+            epochs=float(os.environ.get("EPOCHS", str(cls.epochs))),
+            max_seq=int(os.environ.get("MAX_SEQ", str(cls.max_seq))),
+            seed=int(os.environ.get("SEED", str(cls.seed))),
+        )
+
+
+def train_adapter(config: TrainConfig | None = None) -> dict[str, Any]:
+    """Fine-tune Gemma 4 26B-A4B with 16-bit LoRA and save the adapter."""
+
+    cfg = config or TrainConfig.from_env()
+    Path(cfg.out_dir).mkdir(parents=True, exist_ok=True)
+    Path(cfg.out_adapter).mkdir(parents=True, exist_ok=True)
+
+    from unsloth import FastModel  # import before transformers / trl / peft
+
+    print(f">>> loading base {cfg.base_model} with 16-bit LoRA", flush=True)
+    model, tokenizer = FastModel.from_pretrained(
+        model_name=cfg.base_model,
+        max_seq_length=cfg.max_seq,
+        load_in_4bit=False,
+        load_in_16bit=True,
+        full_finetuning=False,
+    )
+    model = FastModel.get_peft_model(
+        model,
+        r=16,
+        lora_alpha=16,
+        lora_dropout=0,
+        target_modules=[
+            "q_proj",
+            "k_proj",
+            "v_proj",
+            "o_proj",
+            "gate_proj",
+            "up_proj",
+            "down_proj",
+        ],
+        use_gradient_checkpointing="unsloth",
+        random_state=cfg.seed,
+    )
+
+    from unsloth.chat_templates import get_chat_template
+
+    tokenizer = get_chat_template(tokenizer, chat_template="gemma-4")
+
+    from datasets import load_dataset
+
+    def format_example(example: dict[str, Any]) -> dict[str, str]:
+        text = tokenizer.apply_chat_template(
+            example["messages"],
+            tokenize=False,
+            add_generation_prompt=False,
+        )
+        return {"text": text.removeprefix("<bos>")}
+
+    train_ds = load_dataset("json", data_files=cfg.train_file, split="train").map(format_example)
+    eval_ds = None
+    if Path(cfg.eval_file).exists():
+        eval_ds = load_dataset("json", data_files=cfg.eval_file, split="train").map(format_example)
+
+    print(
+        f">>> train={len(train_ds)}"
+        + (f" eval={len(eval_ds)}" if eval_ds is not None else " eval=none"),
+        flush=True,
+    )
+
+    sample = train_ds[0]["text"]
+    if INSTRUCTION_PART not in sample or RESPONSE_PART not in sample:
+        raise RuntimeError(
+            "Gemma 4 train_on_responses_only markers were not found. "
+            "Expected '<|turn>user\\n' and '<|turn>model\\n'."
+        )
+    print(">>> pre-flight marker OK", repr(INSTRUCTION_PART), repr(RESPONSE_PART), flush=True)
+
+    from transformers import TrainingArguments
+    from trl import SFTTrainer
+
+    training_args = dict(
+        per_device_train_batch_size=1,
+        gradient_accumulation_steps=8,
+        num_train_epochs=cfg.epochs,
+        learning_rate=2e-4,
+        lr_scheduler_type="cosine",
+        warmup_ratio=0.05,
+        bf16=True,
+        optim="adamw_8bit",
+        logging_steps=1,
+        output_dir=cfg.out_dir,
+        report_to="none",
+        seed=cfg.seed,
+        save_strategy="no",
+    )
+    if eval_ds is not None:
+        training_args.update(per_device_eval_batch_size=1, eval_strategy="epoch")
+
+    trainer = SFTTrainer(
+        model=model,
+        tokenizer=tokenizer,
+        train_dataset=train_ds,
+        eval_dataset=eval_ds,
+        dataset_text_field="text",
+        max_seq_length=cfg.max_seq,
+        args=TrainingArguments(**training_args),
+    )
+
+    from unsloth.chat_templates import train_on_responses_only
+
+    trainer = train_on_responses_only(
+        trainer,
+        instruction_part=INSTRUCTION_PART,
+        response_part=RESPONSE_PART,
+    )
+
+    stats = trainer.train()
+    eval_metrics = trainer.evaluate() if eval_ds is not None else {}
+
+    model.save_pretrained(cfg.out_adapter)
+    tokenizer.save_pretrained(cfg.out_adapter)
+
+    summary = {
+        **asdict(cfg),
+        "n_train": len(train_ds),
+        "n_eval": len(eval_ds) if eval_ds is not None else 0,
+        "train_loss": stats.metrics.get("train_loss"),
+        "eval": eval_metrics,
+        "expected_steps": ((len(train_ds) + 7) // 8) * int(cfg.epochs),
+    }
+    summary_path = Path(cfg.out_adapter) / "train_summary.json"
+    summary_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8")
+    print(f">>> adapter saved to {cfg.out_adapter}", flush=True)
+    return summary
+
+
+def main() -> None:
+    train_adapter()
+
+
+if __name__ == "__main__":
+    main()
*** Add File: train/modal_train.py
+"""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("torch==2.6.0", index_url="https://download.pytorch.org/whl/cu124")
+    .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=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(
+    push_lora: bool = False,
+    merge: bool = False,
+    lora_repo: str = DEFAULT_LORA_REPO,
+    merged_repo: str = DEFAULT_MERGED_REPO,
+) -> None:
+    summary = train.remote()
+    print(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))
*** End Patch


Success. Updated the following files:
A .gitignore
A train/train_core.py
A train/modal_train.py

# Dataset is handed over out-of-band.
data/
*.jsonl

# Model artifacts.
*.safetensors
*.gguf
neurobait-lora*/
merged*/
outputs/

# Local development.
.venv/
__pycache__/
*.pyc
.env

# Hugging Face / Modal local caches.
.cache/
.modal/

"""Core NeuroBait fine-tuning logic.

This module is intentionally import-safe: call ``train_adapter()`` from Modal or
from a GPU box. Unsloth and other heavy libraries are imported inside the
function so local development can still run syntax checks without the GPU stack.
"""

from __future__ import annotations

import json
import os
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any

BASE_MODEL = "unsloth/gemma-4-26b-a4b-it"
INSTRUCTION_PART = "<|turn>user\n"
RESPONSE_PART = "<|turn>model\n"

@dataclass(frozen=True)
class TrainConfig:
    base_model: str = BASE_MODEL
    train_file: str = "/data/train.jsonl"
    eval_file: str = "/data/eval.jsonl"
    out_adapter: str = "/out/neurobait-lora-run3"
    out_dir: str = "/out/outputs"
    epochs: float = 3.0
    max_seq: int = 2048
    seed: int = 42

    @classmethod
    def from_env(cls) -> "TrainConfig":
        return cls(
            base_model=os.environ.get("BASE_MODEL", cls.base_model),
            train_file=os.environ.get("TRAIN_FILE", cls.train_file),
            eval_file=os.environ.get("EVAL_FILE", cls.eval_file),
            out_adapter=os.environ.get("OUT_ADAPTER", cls.out_adapter),
            out_dir=os.environ.get("OUT_DIR", cls.out_dir),
            epochs=float(os.environ.get("EPOCHS", str(cls.epochs))),
            max_seq=int(os.environ.get("MAX_SEQ", str(cls.max_seq))),
            seed=int(os.environ.get("SEED", str(cls.seed))),
        )

def train_adapter(config: TrainConfig | None = None) -> dict[str, Any]:
    """Fine-tune Gemma 4 26B-A4B with 16-bit LoRA and save the adapter."""

    cfg = config or TrainConfig.from_env()
    Path(cfg.out_dir).mkdir(parents=True, exist_ok=True)
    Path(cfg.out_adapter).mkdir(parents=True, exist_ok=True)

    from unsloth import FastModel  # import before transformers / trl / peft

    print(f">>> loading base {cfg.base_model} with 16-bit LoRA", flush=True)
    model, tokenizer = FastModel.from_pretrained(
        model_name=cfg.base_model,
        max_seq_length=cfg.max_seq,
        load_in_4bit=False,
        load_in_16bit=True,
        full_finetuning=False,
    )
    model = FastModel.get_peft_model(
        model,
        r=16,
        lora_alpha=16,
        lora_dropout=0,
        target_modules=[
            "q_proj",
            "k_proj",
            "v_proj",
            "o_proj",
            "gate_proj",
            "up_proj",
            "down_proj",
        ],
        use_gradient_checkpointing="unsloth",
        random_state=cfg.seed,
    )

    from unsloth.chat_templates import get_chat_template

    tokenizer = get_chat_template(tokenizer, chat_template="gemma-4")

    from datasets import load_dataset

    def format_example(example: dict[str, Any]) -> dict[str, str]:
        text = tokenizer.apply_chat_template(
            example["messages"],
            tokenize=False,
            add_generation_prompt=False,
        )
        return {"text": text.removeprefix("<bos>")}

    train_ds = load_dataset("json", data_files=cfg.train_file, split="train").map(format_example)
    eval_ds = None
    if Path(cfg.eval_file).exists():
        eval_ds = load_dataset("json", data_files=cfg.eval_file, split="train").map(format_example)

    print(
        f">>> train={len(train_ds)}"
        + (f" eval={len(eval_ds)}" if eval_ds is not None else " eval=none"),
        flush=True,
    )

    sample = train_ds[0]["text"]
    if INSTRUCTION_PART not in sample or RESPONSE_PART not in sample:
        raise RuntimeError(
            "Gemma 4 train_on_responses_only markers were not found. "
            "Expected '<|turn>user\\n' and '<|turn>model\\n'."
        )
    print(">>> pre-flight marker OK", repr(INSTRUCTION_PART), repr(RESPONSE_PART), flush=True)

    from transformers import TrainingArguments
    from trl import SFTTrainer

    training_args = dict(
        per_device_train_batch_size=1,
        gradient_accumulation_steps=8,
        num_train_epochs=cfg.epochs,
        learning_rate=2e-4,
        lr_scheduler_type="cosine",
        warmup_ratio=0.05,
        bf16=True,
        optim="adamw_8bit",
        logging_steps=1,
        output_dir=cfg.out_dir,
        report_to="none",
        seed=cfg.seed,
        save_strategy="no",
    )
    if eval_ds is not None:
        training_args.update(per_device_eval_batch_size=1, eval_strategy="epoch")

    trainer = SFTTrainer(
        model=model,
        tokenizer=tokenizer,
        train_dataset=train_ds,
        eval_dataset=eval_ds,
        dataset_text_field="text",
        max_seq_length=cfg.max_seq,
        args=TrainingArguments(**training_args),
    )

    from unsloth.chat_templates import train_on_responses_only

    trainer = train_on_responses_only(
        trainer,
        instruction_part=INSTRUCTION_PART,
        response_part=RESPONSE_PART,
    )

    stats = trainer.train()
    eval_metrics = trainer.evaluate() if eval_ds is not None else {}

    model.save_pretrained(cfg.out_adapter)
    tokenizer.save_pretrained(cfg.out_adapter)

    summary = {
        **asdict(cfg),
        "n_train": len(train_ds),
        "n_eval": len(eval_ds) if eval_ds is not None else 0,
        "train_loss": stats.metrics.get("train_loss"),
        "eval": eval_metrics,
        "expected_steps": ((len(train_ds) + 7) // 8) * int(cfg.epochs),
    }
    summary_path = Path(cfg.out_adapter) / "train_summary.json"
    summary_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8")
    print(f">>> adapter saved to {cfg.out_adapter}", flush=True)
    return summary

def main() -> None:
    train_adapter()

if __name__ == "__main__":
    main()

"""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("torch==2.6.0", index_url="https://download.pytorch.org/whl/cu124")
    .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=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(
    push_lora: bool = False,
    merge: bool = False,
    lora_repo: str = DEFAULT_LORA_REPO,
    merged_repo: str = DEFAULT_MERGED_REPO,
) -> None:
    summary = train.remote()
    print(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))


Output:
Exit code: 0
Wall time: 0.4 seconds
Output:
Success. Updated the following files:
A .gitignore
A train/train_core.py
A train/modal_train.py


Training scaffold sudah masuk. Ada satu catatan untuk review nanti: `modal_train.py` sengaja akan gagal saat `modal run` kalau `data/train.jsonl` dan `data/eval.jsonl` belum disalin, karena dataset memang prerequisite eksplisit untuk image build.


Add eval/report placeholders with useful CLI behavior

Add HF Space Gradio app and requirements

Write README with GitHub/HF/Modal architecture


Output: Plan updated


Sekarang saya tambahkan evaluasi ringan yang bisa berjalan lokal untuk validasi dataset/persona heuristik, plus report renderer sederhana. Evaluasi GPU penuh bisa dikembangkan dari script upstream, tapi file ini sudah punya CLI dan output JSON/Markdown yang mudah dibaca.


Input:
*** Begin Patch
*** Add File: eval/eval_neurobait.py
+"""Lightweight NeuroBait dataset/eval helpers.
+
+GPU generation is intentionally left to Modal in a later pass. This script gives
+us a local, dependency-light way to validate held-out conversations and compute
+persona heuristics for references or generated outputs.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import re
+from dataclasses import asdict, dataclass
+from pathlib import Path
+from statistics import mean
+from typing import Iterable
+
+
+LABEL_RE = re.compile(r"\b(Micro-action|Hook|Stakes|Validasi|Resep Engagement)\b", re.I)
+ACTION_CUES = (
+    "ambil",
+    "buka",
+    "berdiri",
+    "catat",
+    "klik",
+    "letakkan",
+    "pakai",
+    "pilih",
+    "singkir",
+    "tulis",
+    "taruh",
+    "open",
+    "pick",
+    "write",
+    "stand",
+    "move",
+    "set",
+)
+
+
+@dataclass
+class TurnScore:
+    conversation_id: int
+    turn_index: int
+    role: str
+    words: int
+    sentences: int
+    has_action_cue: bool
+    has_label_leak: bool
+    text: str
+
+
+def load_jsonl(path: Path) -> list[dict]:
+    rows = []
+    with path.open("r", encoding="utf-8") as fh:
+        for line_no, line in enumerate(fh, start=1):
+            if line.strip():
+                try:
+                    rows.append(json.loads(line))
+                except json.JSONDecodeError as exc:
+                    raise ValueError(f"{path}:{line_no}: invalid JSONL") from exc
+    return rows
+
+
+def assistant_turns(rows: Iterable[dict]) -> Iterable[tuple[int, int, str]]:
+    for conv_id, row in enumerate(rows):
+        for turn_index, message in enumerate(row.get("messages", [])):
+            if message.get("role") == "assistant":
+                content = message.get("content", "")
+                if isinstance(content, str):
+                    yield conv_id, turn_index, content
+
+
+def count_sentences(text: str) -> int:
+    chunks = [part for part in re.split(r"[.!?]+", text) if part.strip()]
+    return max(1, len(chunks)) if text.strip() else 0
+
+
+def score_text(conv_id: int, turn_index: int, text: str) -> TurnScore:
+    lowered = text.lower()
+    words = re.findall(r"\b[\w'-]+\b", text)
+    return TurnScore(
+        conversation_id=conv_id,
+        turn_index=turn_index,
+        role="assistant",
+        words=len(words),
+        sentences=count_sentences(text),
+        has_action_cue=any(cue in lowered for cue in ACTION_CUES),
+        has_label_leak=bool(LABEL_RE.search(text)),
+        text=text,
+    )
+
+
+def summarize(scores: list[TurnScore]) -> dict:
+    if not scores:
+        return {
+            "assistant_turns": 0,
+            "avg_words": 0,
+            "avg_sentences": 0,
+            "action_turn_rate": 0,
+            "label_leak_turn_rate": 0,
+        }
+    return {
+        "assistant_turns": len(scores),
+        "avg_words": round(mean(s.words for s in scores), 2),
+        "avg_sentences": round(mean(s.sentences for s in scores), 2),
+        "action_turn_rate": round(mean(1 for s in scores if s.has_action_cue), 4),
+        "label_leak_turn_rate": round(mean(1 for s in scores if s.has_label_leak), 4),
+    }
+
+
+def evaluate_file(input_path: Path, output_path: Path | None = None) -> dict:
+    rows = load_jsonl(input_path)
+    scores = [score_text(*turn) for turn in assistant_turns(rows)]
+    result = {
+        "input": str(input_path),
+        "conversations": len(rows),
+        "summary": summarize(scores),
+        "turns": [asdict(score) for score in scores],
+    }
+    if output_path:
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+        output_path.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8")
+    return result
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Score NeuroBait JSONL conversations.")
+    parser.add_argument("input", type=Path, help="JSONL file with messages[] conversations")
+    parser.add_argument("--output", type=Path, default=None, help="Optional JSON output path")
+    args = parser.parse_args()
+
+    result = evaluate_file(args.input, args.output)
+    print(json.dumps(result["summary"], ensure_ascii=False, indent=2))
+
+
+if __name__ == "__main__":
+    main()
*** Add File: eval/make_report.py
+"""Render a compact Markdown report from eval/eval_neurobait.py JSON output."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+
+def render_markdown(result: dict) -> str:
+    summary = result.get("summary", {})
+    lines = [
+        "# NeuroBait Eval Report",
+        "",
+        f"- Input: `{result.get('input', '-')}`",
+        f"- Conversations: `{result.get('conversations', 0)}`",
+        f"- Assistant turns: `{summary.get('assistant_turns', 0)}`",
+        f"- Avg words/turn: `{summary.get('avg_words', 0)}`",
+        f"- Avg sentences/turn: `{summary.get('avg_sentences', 0)}`",
+        f"- Action cue turn rate: `{summary.get('action_turn_rate', 0)}`",
+        f"- Label leak turn rate: `{summary.get('label_leak_turn_rate', 0)}`",
+        "",
+        "## Review Notes",
+        "",
+        "- Target label leak rate is `0`.",
+        "- Action cues are heuristic; use qualitative review for final pass.",
+        "- English transfer should be reviewed separately on EN held-out examples.",
+        "",
+    ]
+    return "\n".join(lines)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Render Markdown from NeuroBait eval JSON.")
+    parser.add_argument("input", type=Path, help="Eval JSON path")
+    parser.add_argument("--output", type=Path, default=None, help="Markdown output path")
+    args = parser.parse_args()
+
+    result = json.loads(args.input.read_text(encoding="utf-8"))
+    output = args.output or args.input.with_suffix(".md")
+    output.write_text(render_markdown(result), encoding="utf-8")
+    print(output)
+
+
+if __name__ == "__main__":
+    main()
*** End Patch


Success. Updated the following files:
A eval/eval_neurobait.py
A eval/make_report.py

"""Lightweight NeuroBait dataset/eval helpers.

GPU generation is intentionally left to Modal in a later pass. This script gives
us a local, dependency-light way to validate held-out conversations and compute
persona heuristics for references or generated outputs.
"""

from __future__ import annotations

import argparse
import json
import re
from dataclasses import asdict, dataclass
from pathlib import Path
from statistics import mean
from typing import Iterable

LABEL_RE = re.compile(r"\b(Micro-action|Hook|Stakes|Validasi|Resep Engagement)\b", re.I)
ACTION_CUES = (
    "ambil",
    "buka",
    "berdiri",
    "catat",
    "klik",
    "letakkan",
    "pakai",
    "pilih",
    "singkir",
    "tulis",
    "taruh",
    "open",
    "pick",
    "write",
    "stand",
    "move",
    "set",
)

@dataclass
class TurnScore:
    conversation_id: int
    turn_index: int
    role: str
    words: int
    sentences: int
    has_action_cue: bool
    has_label_leak: bool
    text: str

def load_jsonl(path: Path) -> list[dict]:
    rows = []
    with path.open("r", encoding="utf-8") as fh:
        for line_no, line in enumerate(fh, start=1):
            if line.strip():
                try:
                    rows.append(json.loads(line))
                except json.JSONDecodeError as exc:
                    raise ValueError(f"{path}:{line_no}: invalid JSONL") from exc
    return rows

def assistant_turns(rows: Iterable[dict]) -> Iterable[tuple[int, int, str]]:
    for conv_id, row in enumerate(rows):
        for turn_index, message in enumerate(row.get("messages", [])):
            if message.get("role") == "assistant":
                content = message.get("content", "")
                if isinstance(content, str):
                    yield conv_id, turn_index, content

def count_sentences(text: str) -> int:
    chunks = [part for part in re.split(r"[.!?]+", text) if part.strip()]
    return max(1, len(chunks)) if text.strip() else 0

def score_text(conv_id: int, turn_index: int, text: str) -> TurnScore:
    lowered = text.lower()
    words = re.findall(r"\b[\w'-]+\b", text)
    return TurnScore(
        conversation_id=conv_id,
        turn_index=turn_index,
        role="assistant",
        words=len(words),
        sentences=count_sentences(text),
        has_action_cue=any(cue in lowered for cue in ACTION_CUES),
        has_label_leak=bool(LABEL_RE.search(text)),
        text=text,
    )

def summarize(scores: list[TurnScore]) -> dict:
    if not scores:
        return {
            "assistant_turns": 0,
            "avg_words": 0,
            "avg_sentences": 0,
            "action_turn_rate": 0,
            "label_leak_turn_rate": 0,
        }
    return {
        "assistant_turns": len(scores),
        "avg_words": round(mean(s.words for s in scores), 2),
        "avg_sentences": round(mean(s.sentences for s in scores), 2),
        "action_turn_rate": round(mean(1 for s in scores if s.has_action_cue), 4),
        "label_leak_turn_rate": round(mean(1 for s in scores if s.has_label_leak), 4),
    }

def evaluate_file(input_path: Path, output_path: Path | None = None) -> dict:
    rows = load_jsonl(input_path)
    scores = [score_text(*turn) for turn in assistant_turns(rows)]
    result = {
        "input": str(input_path),
        "conversations": len(rows),
        "summary": summarize(scores),
        "turns": [asdict(score) for score in scores],
    }
    if output_path:
        output_path.parent.mkdir(parents=True, exist_ok=True)
        output_path.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8")
    return result

def main() -> None:
    parser = argparse.ArgumentParser(description="Score NeuroBait JSONL conversations.")
    parser.add_argument("input", type=Path, help="JSONL file with messages[] conversations")
    parser.add_argument("--output", type=Path, default=None, help="Optional JSON output path")
    args = parser.parse_args()

    result = evaluate_file(args.input, args.output)
    print(json.dumps(result["summary"], ensure_ascii=False, indent=2))

if __name__ == "__main__":
    main()

"""Render a compact Markdown report from eval/eval_neurobait.py JSON output."""

from __future__ import annotations

import argparse
import json
from pathlib import Path

def render_markdown(result: dict) -> str:
    summary = result.get("summary", {})
    lines = [
        "# NeuroBait Eval Report",
        "",
        f"- Input: `{result.get('input', '-')}`",
        f"- Conversations: `{result.get('conversations', 0)}`",
        f"- Assistant turns: `{summary.get('assistant_turns', 0)}`",
        f"- Avg words/turn: `{summary.get('avg_words', 0)}`",
        f"- Avg sentences/turn: `{summary.get('avg_sentences', 0)}`",
        f"- Action cue turn rate: `{summary.get('action_turn_rate', 0)}`",
        f"- Label leak turn rate: `{summary.get('label_leak_turn_rate', 0)}`",
        "",
        "## Review Notes",
        "",
        "- Target label leak rate is `0`.",
        "- Action cues are heuristic; use qualitative review for final pass.",
        "- English transfer should be reviewed separately on EN held-out examples.",
        "",
    ]
    return "\n".join(lines)

def main() -> None:
    parser = argparse.ArgumentParser(description="Render Markdown from NeuroBait eval JSON.")
    parser.add_argument("input", type=Path, help="Eval JSON path")
    parser.add_argument("--output", type=Path, default=None, help="Markdown output path")
    args = parser.parse_args()

    result = json.loads(args.input.read_text(encoding="utf-8"))
    output = args.output or args.input.with_suffix(".md")
    output.write_text(render_markdown(result), encoding="utf-8")
    print(output)

if __name__ == "__main__":
    main()


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
Success. Updated the following files:
A eval/eval_neurobait.py
A eval/make_report.py


Saya lanjut ke folder `deploy/`: Space app dibuat sebagai repo HF Space mandiri, tapi tetap disimpan di GitHub sebagai source/template. Model ID sengaja via environment variable supaya nanti bisa diarahkan ke repo HF final tanpa edit kode.


Input:
*** Begin Patch
*** Add File: 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()
*** Add File: deploy/requirements.txt
+spaces
+gradio
+transformers==5.5.0
+accelerate
+torch
+bitsandbytes
*** End Patch


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

spaces
gradio
transformers==5.5.0
accelerate
torch
bitsandbytes

"""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: 0.4 seconds
Output:
Success. Updated the following files:
A deploy/app.py
A deploy/requirements.txt


Deploy scaffold sudah ada. Saya akan tulis README yang menegaskan batas tanggung jawab tiga repo: GitHub untuk kode, HF Model untuk bobot, HF Space untuk aplikasi, supaya saat review modularitasnya tidak ambigu.


Input:
*** Begin Patch
*** Add File: README.md
+# 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`.
+
+## 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
+```
+
+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`
*** End Patch


Success. Updated the following files:
A README.md

# 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`.

## 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
```

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:
Exit code: 0
Wall time: 0.2 seconds
Output:
Success. Updated the following files:
A README.md


Saya melihat satu bug kecil di ringkasan eval heuristik: rate action/label harus dibagi seluruh turn, bukan hanya turn yang match. Saya patch sekarang sebelum validasi syntax.


Input:
*** Begin Patch
*** Update File: eval/eval_neurobait.py
@@
     return {
         "assistant_turns": len(scores),
         "avg_words": round(mean(s.words for s in scores), 2),
         "avg_sentences": round(mean(s.sentences for s in scores), 2),
-        "action_turn_rate": round(mean(1 for s in scores if s.has_action_cue), 4),
-        "label_leak_turn_rate": round(mean(1 for s in scores if s.has_label_leak), 4),
+        "action_turn_rate": round(mean(1 if s.has_action_cue else 0 for s in scores), 4),
+        "label_leak_turn_rate": round(mean(1 if s.has_label_leak else 0 for s in scores), 4),
     }
*** End Patch


Success. Updated the following files:
M eval/eval_neurobait.py

@@ -106,4 +106,4 @@
         "avg_sentences": round(mean(s.sentences for s in scores), 2),
-        "action_turn_rate": round(mean(1 for s in scores if s.has_action_cue), 4),
-        "label_leak_turn_rate": round(mean(1 for s in scores if s.has_label_leak), 4),
+        "action_turn_rate": round(mean(1 if s.has_action_cue else 0 for s in scores), 4),
+        "label_leak_turn_rate": round(mean(1 if s.has_label_leak else 0 for s in scores), 4),
     }


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
Success. Updated the following files:
M eval/eval_neurobait.py


Saya akan jalankan check ringan dengan venv yang kamu tunjuk: compile Python files dan lihat struktur akhir. Ini tidak mengimpor Modal/Transformers, jadi aman tanpa install dependency baru.


Output: Plan updated


/media/haris-subrata/Work/llm/agent_analytics/venv/bin/python -m py_compile train/train_core.py train/modal_train.py eval/eval_neurobait.py eval/make_report.py deploy/app.py


Output:
Chunk ID: 983bb6
Wall time: 0.0065 seconds
Process exited with code 0
Original token count: 0
Output:


Output:
Chunk ID: 257d82
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 47
Output:
./.gitignore
./HANDOFF.md
./README.md
./deploy/app.py
./deploy/requirements.txt
./eval/eval_neurobait.py
./eval/make_report.py
./github-hf.svg
./train/modal_train.py
./train/train_core.py


Output:
Chunk ID: aaffb6
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 810
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`.

## 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
```

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`


Syntax check lulus. Saya juga akan jalankan evaluasi heuristik terhadap `eval.jsonl` upstream hanya untuk memastikan CLI-nya bekerja pada format dataset aktual; ini tidak menyalin dataset dan tidak menulis output.


find train eval deploy -maxdepth 2 -type d -name __pycache__ -o -type f | sort


Output:
Chunk ID: 72ef4f
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 34
Output:
{
  "assistant_turns": 164,
  "avg_words": 61.11,
  "avg_sentences": 3.76,
  "action_turn_rate": 0.6463,
  "label_leak_turn_rate": 0
}


Output:
Chunk ID: 99be88
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 100
Output:
deploy/__pycache__
deploy/__pycache__/app.cpython-310.pyc
deploy/app.py
deploy/requirements.txt
eval/__pycache__
eval/__pycache__/eval_neurobait.cpython-310.pyc
eval/__pycache__/make_report.cpython-310.pyc
eval/eval_neurobait.py
eval/make_report.py
train/__pycache__
train/__pycache__/modal_train.cpython-310.pyc
train/__pycache__/train_core.cpython-310.pyc
train/modal_train.py
train/train_core.py


Output: Plan updated


Sudah saya susun scaffold awalnya.

Yang dibuat:
- [README.md](/media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/README.md:1): menjelaskan pemisahan GitHub repo, HF Model repo, HF Space repo, Modal flow, data boundary, dan command awal.
- [.gitignore](/media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/.gitignore:1): exclude dataset, adapter/model artifact, outputs, cache, env.
- [train/train_core.py](/media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/train/train_core.py:1): core fine-tuning import-safe, config via env, marker preflight Gemma 4, locked hyperparam.
- [train/modal_train.py](/media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/train/modal_train.py:1): Modal app untuk train, push LoRA, merge+push merged model.
- [eval/eval_neurobait.py](/media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/eval/eval_neurobait.py:1): local heuristic evaluator untuk JSONL conversation.
- [eval/make_report.py](/media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/eval/make_report.py:1): renderer Markdown sederhana dari hasil eval JSON.
- [deploy/app.py](/media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/deploy/app.py:1): HF Space Gradio app, `MODEL_ID` via env, `_mm()` fix Gemma 4.
- [deploy/requirements.txt](/media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/deploy/requirements.txt:1): dependency Space.

Validasi:
- `py_compile` lulus untuk semua file Python pakai `/media/haris-subrata/Work/llm/agent_analytics/venv/bin/python`.
- Eval heuristic berhasil jalan pada upstream `eval.jsonl`: `164` assistant turns, avg `61.11` words, label leak rate `0`.

Catatan review:
- `modal_train.py` memang akan butuh `data/train.jsonl` dan `data/eval.jsonl` tersedia sebelum `modal run`, karena file itu dibake ke image.
- GPU eval penuh belum saya port dari upstream; yang ada sekarang adalah evaluator lokal ringan untuk readability dan baseline checks.
- `__pycache__` terbentuk dari compile check, tapi sudah masuk `.gitignore`.


Summary: auto


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


README.md sekarang **untuk GitHub/source repo lokal ini**, bukan README khusus deployment HF Space atau HF Model repo.

Konteksnya:
- Menjelaskan arsitektur 3 artefak: GitHub source repo, HF Model repo, HF Space repo.
- Menjelaskan cara local dev, Modal training, eval ringan, dan folder `deploy/` sebagai source untuk Space.
- Cocok untuk reviewer/developer yang membuka repo GitHub.

Belum ideal sebagai:
- **HF Model card**: perlu README/model card terpisah berisi model description, base model, training data summary, intended use, limitations, eval, license.
- **HF Space README**: biasanya lebih singkat, fokus app usage, hardware, env var `MODEL_ID`, dan maybe link ke model/source.
- **Competition submission README**: mungkin perlu narasi “built with Codex”, Modal training, Gradio deployment, links GitHub/HF.

Jadi nanti sebaiknya ada:
- `README.md` di root GitHub: engineering source repo.
- `deploy/README.md` atau generated Space README: untuk HF Space.
- `model-card/README.md` atau `train/model_card.md`: untuk HF Model repo.


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


Oke. Struktur sekarang memang sengaja engineering-first supaya modularitasnya gampang direview dulu.

Nanti kalau sudah cocok, kita bisa tambahkan konteks non-engineering secara terpisah tanpa mengganggu flow teknis, misalnya:
- persona/product brief NeuroBait
- research-design summary
- competition submission notes
- model card untuk HF

Saya tunggu review struktur dan readability-nya.


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


Folder `data/` kosong karena saya sengaja belum menyalin dataset: sesuai handoff, dataset diserahkan out-of-band dan `.gitignore` sudah mengecualikan `data/`. Karena upstream dataset ada di sibling repo, saya akan salin `train.jsonl` dan `eval.jsonl` ke sini sekarang, lalu lanjut scaffold dokumen deployment/model-card dan validator dataset ringan.


Output:
Chunk ID: 74d444
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:


Output:
Chunk ID: 101905
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:


Output:
Chunk ID: 992d5a
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:


Dataset sudah disalin ke `data/` lokal, tetap aman dari Git karena `data/` gitignored. Saya tambahkan sekarang scaffold pendukung: validator dataset, model card draft untuk HF Model repo, README Space, dan brief produk/research context awal.