Output:
Chunk ID: 3647c2
Wall time: 85.6581 seconds
Process exited with code 1
Original token count: 2710
Output:
      Successfully uninstalled torch-2.6.0+cu124

Successfully installed accelerate-1.13.0 aiohappyeyeballs-2.6.2 aiohttp-3.14.1 aiosignal-1.4.0 annotated-doc-0.0.4 annotated-types-0.7.0 anyio-4.13.0 attrs-26.1.0 bitsandbytes-0.49.2 certifi-2026.5.20 charset_normalizer-3.4.7 click-8.4.1 cuda-bindings-12.9.4 cuda-pathfinder-1.5.5 cut_cross_entropy-25.1.1 datasets-4.3.0 diffusers-0.38.0 dill-0.4.0 docstring-parser-0.18.0 frozenlist-1.8.0 fsspec-2025.9.0 h11-0.16.0 hf-xet-1.5.0 hf_transfer-0.1.9 httpcore-1.0.9 httpx-0.28.1 huggingface_hub-1.18.0 idna-3.18 importlib_metadata-9.0.0 markdown-it-py-4.2.0 mdurl-0.1.2 msgspec-0.21.1 multidict-6.7.1 multiprocess-0.70.16 nest-asyncio-1.6.0 numpy-2.4.6 nvidia-cublas-cu12-12.8.4.1 nvidia-cuda-cupti-cu12-12.8.90 nvidia-cuda-nvrtc-cu12-12.8.93 nvidia-cuda-runtime-cu12-12.8.90 nvidia-cudnn-cu12-9.10.2.21 nvidia-cufft-cu12-11.3.3.83 nvidia-cufile-cu12-1.13.1.3 nvidia-curand-cu12-10.3.9.90 nvidia-cusolver-cu12-11.7.3.90 nvidia-cusparse-cu12-12.5.8.93 nvidia-cusparselt-cu12-0.7.1 nvidia-nccl-cu12-2.27.5 nvidia-nvjitlink-cu12-12.8.93 nvidia-nvshmem-cu12-3.4.5 nvidia-nvtx-cu12-12.8.90 packaging-26.2 pandas-3.0.3 peft-0.19.1 pillow-12.2.0 propcache-0.5.2 protobuf-7.35.0 psutil-7.2.2 pyarrow-24.0.0 pydantic-2.13.4 pydantic-core-2.46.4 pygments-2.20.0 python-dateutil-2.9.0.post0 pyyaml-6.0.3 regex-2026.5.9 requests-2.34.2 rich-15.0.0 safetensors-0.8.0rc1 sentencepiece-0.2.1 shellingham-1.5.4 six-1.17.0 sympy-1.14.0 tokenizers-0.22.2 torch-2.10.0 torchao-0.17.0 torchvision-0.25.0 tqdm-4.68.1 transformers-5.5.0 triton-3.6.0 trl-0.24.0 typeguard-4.5.2 typer-0.25.1 typing-inspection-0.4.2 tyro-1.0.13 unsloth-2026.6.1 unsloth_zoo-2026.6.1 urllib3-2.7.0 xformers-0.0.35 xxhash-3.7.0 yarl-1.24.2 zipp-4.1.0

[notice] A new release of pip is available: 25.2 -> 26.1.2
[notice] To update, run: pip install --upgrade pip
Saving image...
Image saved, took 3.85s

Built image im-LI0lSBoQNiiD4f38atZRhV in 147.90s

Building image im-VBngibWwXlncPbK2mOKl9c

=> Step 0: FROM base

=> Step 1: RUN python -m pip install accelerate bitsandbytes datasets huggingface_hub peft==0.19.1 transformers==5.5.0 trl==0.24 --no-deps
Looking in indexes: http://pypi-mirror.modal.local:5555/simple
Requirement already satisfied: accelerate in /usr/local/lib/python3.11/site-packages (1.13.0)
Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.11/site-packages (0.49.2)
Requirement already satisfied: datasets in /usr/local/lib/python3.11/site-packages (4.3.0)
Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.11/site-packages (1.18.0)
Requirement already satisfied: peft==0.19.1 in /usr/local/lib/python3.11/site-packages (0.19.1)
Requirement already satisfied: transformers==5.5.0 in /usr/local/lib/python3.11/site-packages (5.5.0)
Requirement already satisfied: trl==0.24 in /usr/local/lib/python3.11/site-packages (0.24.0)

[notice] A new release of pip is available: 25.2 -> 26.1.2
[notice] To update, run: pip install --upgrade pip
Saving image...
Image saved, took 856.43ms

Built image im-VBngibWwXlncPbK2mOKl9c in 3.25s

✓ Created objects.
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/train/modal_train.
│   py
├── 🔨 Created mount data/eval.jsonl
├── 🔨 Created mount data/train.jsonl
├── 🔨 Created mount train/train_core.py
├── 🔨 Created function preflight.
├── 🔨 Created function train.
├── 🔨 Created function push_lora_to_hub.
└── 🔨 Created function merge_and_push.
Skipping import of cpp extensions due to incompatible torch version. Please upgrade to torch >= 2.11.0 (found 2.10.0+cu128).
/usr/local/lib/python3.11/site-packages/unsloth/__init__.py:165: UserWarning: WARNING: Unsloth should be imported before [trl, transformers, peft] to ensure all optimizations are applied. Your code may run slower or encounter memory issues without these optimizations.

Please restructure your imports with 'import unsloth' at the top of your file.
  from ._gpu_init import *
🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.
Unsloth: Your Flash Attention 2 installation seems to be broken. Using Xformers instead. No performance changes will be seen.
🦥 Unsloth Zoo will now patch everything to make training faster!
Stopping app - uncaught exception raised locally: DeserializationError("Deserialization failed because the 'torch' module is not available in the local environment.").
╭───────────────────── Traceback (most recent call last) ──────────────────────╮
│ /media/haris-subrata/Work/llm/agent_analytics/venv/lib/python3.10/site-packa │
│ ges/modal/_serialization.py:115 in deserialize                               │
│                                                                              │
│   114 │   try:                                                               │
│ ❱ 115 │   │   return Unpickler(client, io.BytesIO(s)).load()                 │
│   116 │   except AttributeError as exc:                                      │
╰──────────────────────────────────────────────────────────────────────────────╯
ModuleNotFoundError: No module named 'torch'

The above exception was the direct cause of the following exception:

╭───────────────────── Traceback (most recent call last) ──────────────────────╮
│ /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/train/modal_train. │
│ py:176 in main                                                               │
│                                                                              │
│   175 │   if run_preflight:                                                  │
│ ❱ 176 │   │   print("preflight:", preflight.remote())                        │
│   177 │   if run_train:                                                      │
│                                                                              │
│ /media/haris-subrata/Work/llm/agent_analytics/venv/lib/python3.10/site-packa │
│ ges/modal/_object.py:46 in wrapped                                           │
│                                                                              │
│    45 │   │   await self.hydrate()                                           │
│ ❱  46 │   │   return await method(self, *args, **kwargs)                     │
│    47                                                                        │
│                                                                              │
│ /media/haris-subrata/Work/llm/agent_analytics/venv/lib/python3.10/site-packa │
│ ges/modal/_functions.py:1696 in remote                                       │
│                                                                              │
│   1695 │   │                                                                 │
│ ❱ 1696 │   │   return await self._call_function(args, kwargs)                │
│   1697                                                                       │
│                                                                              │
│ /media/haris-subrata/Work/llm/agent_analytics/venv/lib/python3.10/site-packa │
│ ges/modal/_functions.py:1640 in _call_function                               │
│                                                                              │
│   1639 │   │                                                                 │
│ ❱ 1640 │   │   return await invocation.run_function()                        │
│   1641                                                                       │
│                                                                              │
│ /media/haris-subrata/Work/llm/agent_analytics/venv/lib/python3.10/site-packa │
│ ges/modal/_functions.py:290 in run_function                                  │
│                                                                              │
│    289 │   │   │   item = await self._get_single_output()                    │
│ ❱  290 │   │   │   return await _process_result(item.result, item.data_forma │
│    291                                                                       │
│                                                                              │
│ /media/haris-subrata/Work/llm/agent_analytics/venv/lib/python3.10/site-packa │
│ ges/modal/_utils/function_utils.py:553 in _process_result                    │
│                                                                              │
│   552 │   try:                                                               │
│ ❱ 553 │   │   return deserialize_data_format(data, data_format, client)      │
│   554 │   except ModuleNotFoundError as deser_exc:                           │
│                                                                              │
│ /media/haris-subrata/Work/llm/agent_analytics/venv/lib/python3.10/site-packa │
│ ges/modal/_serialization.py:395 in deserialize_data_format                   │
│                                                                              │
│   394 │   if data_format == api_pb2.DATA_FORMAT_PICKLE:                      │
│ ❱ 395 │   │   return deserialize(s, client)                                  │
│   396 │   elif data_format == api_pb2.DATA_FORMAT_ASGI:                      │
│                                                                              │
│ /media/haris-subrata/Work/llm/agent_analytics/venv/lib/python3.10/site-packa │
│ ges/modal/_serialization.py:132 in deserialize                               │
│                                                                              │
│   131 │   except ModuleNotFoundError as exc:                                 │
│ ❱ 132 │   │   raise DeserializationError(                                    │
│   133 │   │   │   f"Deserialization failed because the '{exc.name}' module i │
╰──────────────────────────────────────────────────────────────────────────────╯
DeserializationError: Deserialization failed because the 'torch' module is not
available in the local environment.


Preflight remote function sebenarnya sampai berjalan, tapi gagal saat hasilnya dipickle balik ke local karena local venv tidak punya `torch`, sama seperti smoke test pertama. Saya akan patch preflight agar return JSON string, sekaligus bersihkan image build supaya tidak install Torch 2.6 lalu diganti Unsloth ke Torch 2.10.


Input:
*** Begin Patch
*** Update File: train/modal_train.py
@@
 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",
@@
 def preflight() -> dict:
     """Check the remote Modal runtime before spending a full training run."""

+    import json
     import os
     import subprocess

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


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

@@ -15,3 +15,2 @@
     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")
@@ -46,2 +45,3 @@

+    import json
     import os
@@ -49,8 +49,8 @@

+    import unsloth
     import torch
+    import datasets
+    import peft
     import transformers
     import trl
-    import peft
-    import datasets
-    import unsloth

@@ -72,3 +72,3 @@

-    return {
+    result = {
         "hf_token_present": bool(os.environ.get("HF_TOKEN")),
@@ -86,2 +86,3 @@
     }
+    return json.dumps(result, indent=2)


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


Saya ubah preflight return type juga agar konsisten, lalu compile check sebelum rerun Modal.


Input:
*** Begin Patch
*** Update File: train/modal_train.py
@@
-def preflight() -> dict:
+def preflight() -> str:
*** End Patch


@@ -42,3 +42,3 @@
 )
-def preflight() -> dict:
+def preflight() -> str:
     """Check the remote Modal runtime before spending a full training run."""


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


/media/haris-subrata/Work/llm/agent_analytics/venv/bin/python -m py_compile train/modal_train.py


git --git-dir=.git-local --work-tree=. diff -- train/modal_train.py


Output:
Chunk ID: b80875
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:


Output:
Chunk ID: 3937b0
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 391
Output:
diff --git a/train/modal_train.py b/train/modal_train.py
index 9c9e31a..90533cd 100644
--- a/train/modal_train.py
+++ b/train/modal_train.py
@@ -13,7 +13,6 @@ 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",
@@ -41,18 +40,19 @@ out_vol = modal.Volume.from_name("neurobait-out", create_if_missing=True)
     volumes={"/root/.cache/huggingface": hf_cache, "/out": out_vol},
     secrets=[modal.Secret.from_name("huggingface")],
 )
-def preflight() -> dict:
+def preflight() -> str:
     """Check the remote Modal runtime before spending a full training run."""

+    import json
     import os
     import subprocess

+    import unsloth
     import torch
+    import datasets
+    import peft
     import transformers
     import trl
-    import peft
-    import datasets
-    import unsloth

     nvidia_smi = subprocess.run(
         ["nvidia-smi"],
@@ -70,7 +70,7 @@ def preflight() -> dict:
             "device_count": torch.cuda.device_count(),
         }

-    return {
+    result = {
         "hf_token_present": bool(os.environ.get("HF_TOKEN")),
         "torch": torch.__version__,
         "cuda": torch.version.cuda,
@@ -84,6 +84,7 @@ def preflight() -> dict:
         "nvidia_smi": nvidia_smi.stdout[-4000:],
         "nvidia_smi_stderr": nvidia_smi.stderr[-1000:],
     }
+    return json.dumps(result, indent=2)

 @app.function(


Compile check lulus. Saya rerun preflight; image akan rebuild karena image definition berubah, tapi lebih bersih karena tidak lagi install Torch dua kali.


/media/haris-subrata/Work/llm/agent_analytics/venv/bin/python -m modal run train/modal_train.py --run-preflight --no-run-train

Do you want to allow network access to rerun the Modal training preflight after returning JSON and simplifying the image build?


Output:
Chunk ID: 4857e2
Wall time: 30.0019 seconds
Process running with session ID 54586
Original token count: 10492
Output:
Total output lines: 353

✓ Initialized. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-kMCBHgVHN4rujOtIYfExYf
Building image im-Qi2654phAccxBxJGz2KnOw

=> Step 0: FROM base

=> Step 1: RUN python -m pip install unsloth unsloth_zoo
Looking in indexes: http://pypi-mirror.modal.local:5555/simple
Collecting unsloth
  Downloading http://pypi-mirror.modal.local:5555/simple/unsloth/unsloth-2026.6.1-py3-none-any.whl.metadata (58 kB)
Collecting unsloth_zoo
  Downloading http://pypi-mirror.modal.local:5555/simple/unsloth-zoo/unsloth_zoo-2026.6.1-py3-none-any.whl.metadata (33 kB)
Requirement already satisfied: wheel>=0.42.0 in /usr/local/lib/python3.11/site-packages (from unsloth) (0.45.1)
Collecting packaging (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/packaging/packaging-26.2-py3-none-any.whl.metadata (3.5 kB)
Collecting torch<2.11.0,>=2.4.0 (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/torch/torch-2.10.0-3-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (31 kB)
Collecting torchvision (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/torchvision/torchvision-0.27.0-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (5.5 kB)
Collecting numpy (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/numpy/numpy-2.4.6-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (6.6 kB)
Collecting tqdm (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/tqdm/tqdm-4.68.1-py3-none-any.whl.metadata (57 kB)
Collecting psutil (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/psutil/psutil-7.2.2-cp36-abi3-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl.metadata (22 kB)
Collecting tyro (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/tyro/tyro-1.0.13-py3-none-any.whl.metadata (12 kB)
Collecting protobuf (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/protobuf/protobuf-7.35.0-cp310-abi3-manylinux2014_x86_64.whl.metadata (595 bytes)
Collecting xformers>=0.0.27.post2 (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/xformers/xformers-0.0.35-py39-none-manylinux_2_28_x86_64.whl.metadata (1.2 kB)
Collecting bitsandbytes!=0.46.0,!=0.48.0,>=0.45.5 (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/bitsandbytes/bitsandbytes-0.49.2-py3-none-manylinux_2_24_x86_64.whl.metadata (10 kB)
Collecting triton>=3.0.0 (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/triton/triton-3.7.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (1.7 kB)
Collecting sentencepiece>=0.2.0 (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/sentencepiece/sentencepiece-0.2.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (10 kB)
Collecting datasets!=4.0.*,!=4.1.0,<4.4.0,>=3.4.1 (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/datasets/datasets-4.3.0-py3-none-any.whl.metadata (18 kB)
Collecting accelerate>=0.34.1 (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/accelerate/accelerate-1.13.0-py3-none-any.whl.metadata (19 kB)
Collecting peft!=0.11.0,>=0.18.0 (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/peft/peft-0.19.1-py3-none-any.whl.metadata (15 kB)
Collecting huggingface_hub>=0.34.0 (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/huggingface-hub/huggingface_hub-1.18.0-py3-none-any.whl.metadata (14 kB)
Collecting hf_transfer (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/hf-transfer/hf_transfer-0.1.9-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.7 kB)
Collecting diffusers (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/diffusers/diffusers-0.38.0-py3-none-any.whl.metadata (20 kB)
Collecting transformers!=4.52.0,!=4.52.1,!=4.52.2,!=4.52.3,!=4.53.0,!=4.54.0,!=4.55.0,!=4.55.1,!=4.57.0,!=4.57.4,!=4.57.5,!=5.0.0,!=5.1.0,<=5.5.0,>=4.51.3 (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/transformers/transformers-5.5.0-py3-none-any.whl.metadata (32 kB)
Collecting trl!=0.19.0,<=0.24.0,>=0.18.2 (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/trl/trl-0.24.0-py3-none-any.whl.metadata (11 kB)
Collecting typer (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/typer/typer-0.26.7-py3-none-any.whl.metadata (16 kB)
Collecting pydantic (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/pydantic/pydantic-2.13.4-py3-none-any.whl.metadata (109 kB)
Collecting pyyaml (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/pyyaml/pyyaml-6.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (2.4 kB)
Collecting nest-asyncio (from unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/nest-asyncio/nest_asyncio-1.6.0-py3-none-any.whl.metadata (2.8 kB)
Collecting filelock (from datasets!=4.0.*,!=4.1.0,<4.4.0,>=3.4.1->unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/filelock/filelock-3.29.1-py3-none-any.whl.metadata (2.0 kB)
Collecting pyarrow>=21.0.0 (from datasets!=4.0.*,!=4.1.0,<4.4.0,>=3.4.1->unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/pyarrow/pyarrow-24.0.0-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (3.0 kB)
Collecting dill<0.4.1,>=0.3.0 (from datasets!=4.0.*,!=4.1.0,<4.4.0,>=3.4.1->unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/dill/dill-0.4.0-py3-none-any.whl.metadata (10 kB)
Collecting pandas (from datasets!=4.0.*,!=4.1.0,<4.4.0,>=3.4.1->unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/pandas/pandas-3.0.3-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl.metadata (79 kB)
Collecting requests>=2.32.2 (from datasets!=4.0.*,!=4.1.0,<4.4.0,>=3.4.1->unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/requests/requests-2.34.2-py3-none-any.whl.metadata (4.8 kB)
Collecting httpx<1.0.0 (from datasets!=4.0.*,!=4.1.0,<4.4.0,>=3.4.1->unsloth)
  Downloading http://pypi-mirror.modal.local:5555/simple/httpx/httpx-0.28.1-py3-none-any.whl.metadata (7.1 kB)
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Rebuild masih berlangsung dan mengunduh stack besar di Modal image, bukan lokal. Setelah selesai saya akan catat versi final yang benar-benar dipakai dan apakah preflight bersih.


Output:
Chunk ID: 9fdf39
Wall time: 119.0915 seconds
Process exited with code 0
Original token count: 1836
Output:
Installing collected packages: torchao, nvidia-cusparselt-cu12, mpmath, zipp, xxhash, urllib3, typing-extensions, triton, tqdm, sympy, six, shellingham, sentencepiece, safetensors, regex, pyyaml, pygments, pyarrow, psutil, protobuf, propcache, pillow, packaging, nvidia-nvtx-cu12, nvidia-nvshmem-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufile-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, networkx, nest-asyncio, multidict, msgspec, mdurl, MarkupSafe, idna, hf-xet, hf_transfer, h11, fsspec, frozenlist, filelock, docstring-parser, dill, cuda-pathfinder, click, charset_normalizer, certifi, attrs, annotated-types, annotated-doc, aiohappyeyeballs, yarl, typing-inspection, typeguard, requests, python-dateutil, pydantic-core, nvidia-cusparse-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, multiprocess, markdown-it-py, jinja2, importlib_metadata, httpcore, cuda-bindings, anyio, aiosignal, tyro, rich, pydantic, pandas, nvidia-cusolver-cu12, httpx, aiohttp, typer, torch, xformers, torchvision, huggingface_hub, cut_cross_entropy, bitsandbytes, tokenizers, diffusers, datasets, accelerate, transformers, trl, peft, unsloth_zoo, unsloth

Successfully installed MarkupSafe-3.0.3 accelerate-1.13.0 aiohappyeyeballs-2.6.2 aiohttp-3.14.1 aiosignal-1.4.0 annotated-doc-0.0.4 annotated-types-0.7.0 anyio-4.13.0 attrs-26.1.0 bitsandbytes-0.49.2 certifi-2026.5.20 charset_normalizer-3.4.7 click-8.4.1 cuda-bindings-12.9.4 cuda-pathfinder-1.5.5 cut_cross_entropy-25.1.1 datasets-4.3.0 diffusers-0.38.0 dill-0.4.0 docstring-parser-0.18.0 filelock-3.29.1 frozenlist-1.8.0 fsspec-2025.9.0 h11-0.16.0 hf-xet-1.5.0 hf_transfer-0.1.9 httpcore-1.0.9 httpx-0.28.1 huggingface_hub-1.18.0 idna-3.18 importlib_metadata-9.0.0 jinja2-3.1.6 markdown-it-py-4.2.0 mdurl-0.1.2 mpmath-1.3.0 msgspec-0.21.1 multidict-6.7.1 multiprocess-0.70.16 nest-asyncio-1.6.0 networkx-3.6.1 numpy-2.4.6 nvidia-cublas-cu12-12.8.4.1 nvidia-cuda-cupti-cu12-12.8.90 nvidia-cuda-nvrtc-cu12-12.8.93 nvidia-cuda-runtime-cu12-12.8.90 nvidia-cudnn-cu12-9.10.2.21 nvidia-cufft-cu12-11.3.3.83 nvidia-cufile-cu12-1.13.1.3 nvidia-curand-cu12-10.3.9.90 nvidia-cusolver-cu12-11.7.3.90 nvidia-cusparse-cu12-12.5.8.93 nvidia-cusparselt-cu12-0.7.1 nvidia-nccl-cu12-2.27.5 nvidia-nvjitlink-cu12-12.8.93 nvidia-nvshmem-cu12-3.4.5 nvidia-nvtx-cu12-12.8.90 packaging-26.2 pandas-3.0.3 peft-0.19.1 pillow-12.2.0 propcache-0.5.2 protobuf-7.35.0 psutil-7.2.2 pyarrow-24.0.0 pydantic-2.13.4 pydantic-core-2.46.4 pygments-2.20.0 python-dateutil-2.9.0.post0 pyyaml-6.0.3 regex-2026.5.9 requests-2.34.2 rich-15.0.0 safetensors-0.8.0rc1 sentencepiece-0.2.1 shellingham-1.5.4 six-1.17.0 sympy-1.14.0 tokenizers-0.22.2 torch-2.10.0 torchao-0.17.0 torchvision-0.25.0 tqdm-4.68.1 transformers-5.5.0 triton-3.6.0 trl-0.24.0 typeguard-4.5.2 typer-0.25.1 typing-extensions-4.15.0 typing-inspection-0.4.2 tyro-1.0.13 unsloth-2026.6.1 unsloth_zoo-2026.6.1 urllib3-2.7.0 xformers-0.0.35 xxhash-3.7.0 yarl-1.24.2 zipp-4.1.0

[notice] A new release of pip is available: 25.2 -> 26.1.2
[notice] To update, run: pip install --upgrade pip
Saving image...
Image saved, took 8.09s

Built image im-Qi2654phAccxBxJGz2KnOw in 118.60s

Building image im-yfF28EaFNSG909veC4BvuG

=> Step 0: FROM base

=> Step 1: RUN python -m pip install accelerate bitsandbytes datasets huggingface_hub peft==0.19.1 transformers==5.5.0 trl==0.24 --no-deps
Looking in indexes: http://pypi-mirror.modal.local:5555/simple
Requirement already satisfied: accelerate in /usr/local/lib/python3.11/site-packages (1.13.0)
Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.11/site-packages (0.49.2)
Requirement already satisfied: datasets in /usr/local/lib/python3.11/site-packages (4.3.0)
Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.11/site-packages (1.18.0)
Requirement already satisfied: peft==0.19.1 in /usr/local/lib/python3.11/site-packages (0.19.1)
Requirement already satisfied: transformers==5.5.0 in /usr/local/lib/python3.11/site-packages (5.5.0)
Requirement already satisfied: trl==0.24 in /usr/local/lib/python3.11/site-packages (0.24.0)

[notice] A new release of pip is available: 25.2 -> 26.1.2
[notice] To update, run: pip install --upgrade pip
Saving image...
Image saved, took 967.45ms

Built image im-yfF28EaFNSG909veC4BvuG in 3.80s

✓ Created objects.
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/train/modal_train.
│   py
├── 🔨 Created mount data/eval.jsonl
├── 🔨 Created mount data/train.jsonl
├── 🔨 Created mount train/train_core.py
├── 🔨 Created function preflight.
├── 🔨 Created function train.
├── 🔨 Created function push_lora_to_hub.
└── 🔨 Created function merge_and_push.
🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.
Unsloth: Your Flash Attention 2 installation seems to be broken. Using Xformers instead. No performance changes will be seen.
🦥 Unsloth Zoo will now patch everything to make training faster!
preflight: {
  "hf_token_present": true,
  "torch": "2.10.0+cu128",
  "cuda": "12.8",
  "cuda_available": true,
  "gpu": {
    "name": "NVIDIA H100 80GB HBM3",
    "capability": "sm_90",
    "device_count": 1
  },
  "transformers": "5.5.0",
  "trl": "0.24.0",
  "peft": "0.19.1",
  "datasets": "4.3.0",
  "unsloth": "2026.6.1",
  "nvidia_smi": "Mon Jun  8 13:39:48 2026       \n+-----------------------------------------------------------------------------------------+\n| NVIDIA-SMI 580.95.05              Driver Version: 580.95.05      CUDA Version: 13.0     |\n+-----------------------------------------+------------------------+----------------------+\n| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |\n| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\n|                                         |                        |               MIG M. |\n|=========================================+========================+======================|\n|   0  NVIDIA H100 80GB HBM3          On  |   00000000:38:00.0 Off |                    0 |\n| N/A   38C    P0            121W /  700W |     727MiB /  81559MiB |      0%      Default |\n|                                         |                        |             Disabled |\n+-----------------------------------------+------------------------+----------------------+\n\n+-----------------------------------------------------------------------------------------+\n| Processes:                                                                              |\n|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |\n|        ID   ID                                                               Usage      |\n|=========================================================================================|\n|    0   N/A  N/A               1      C   /bin/dumb-init                          718MiB |\n+-----------------------------------------------------------------------------------------+\n",
  "nvidia_smi_stderr": ""
}
Stopping app - local entrypoint completed.
✓ App completed. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-kMCBHgVHN4rujOtIYfExYf


Preflight sekarang lulus. Hasil penting: HF token terbaca, H100 80GB tersedia, CUDA aktif, Unsloth 2026.6.1, Transformers 5.5.0, TRL 0.24.0, PEFT 0.19.1. Saya akan catat hasilnya di local-dev log dan commit patch `modal_train.py` karena image build sudah saya bersihkan.


Input:
*** Begin Patch
*** Update File: local-dev/log_dev.md
@@
 ## Modal Smoke Test Success - 2026-06-08
@@
 - Initial return as a Python dict failed to deserialize locally because local venv does not have `torch`.
 - Smoke script was changed to return JSON string, then rerun successfully.
+
+## Modal Training Preflight - 2026-06-08
+
+Command:
+
+```text
+/media/haris-subrata/Work/llm/agent_analytics/venv/bin/python -m modal run train/modal_train.py --run-preflight --no-run-train
+```
+
+First preflight built the Unsloth image but failed local deserialization because
+the remote function returned a Python dict after importing `torch`, while local
+venv intentionally has no torch.
+
+Patch applied:
+
+- `preflight()` now returns JSON string.
+- Removed explicit `torch==2.6.0` install from training image because Unsloth
+  resolver installs `torch-2.10.0+cu128` anyway.
+
+Successful preflight result:
+
+```text
+hf_token_present: true
+torch: 2.10.0+cu128
+cuda runtime: 12.8
+cuda_available: true
+gpu: NVIDIA H100 80GB HBM3
+capability: sm_90
+device_count: 1
+transformers: 5.5.0
+trl: 0.24.0
+peft: 0.19.1
+datasets: 4.3.0
+unsloth: 2026.6.1
+nvidia-smi driver: 580.95.05
+nvidia-smi CUDA: 13.0
+```
+
+Unsloth message:
+
+```text
+Flash Attention 2 installation seems to be broken. Using Xformers instead.
+```
+
+This matches the upstream RunPod note that FA2 can be broken and Xformers is
+acceptable. Preflight is clean enough to proceed to training.
*** End Patch


Success. Updated the following files:
M local-dev/log_dev.md

@@ -219 +219,47 @@
 - Smoke script was changed to return JSON string, then rerun successfully.
+
+## Modal Training Preflight - 2026-06-08
+
+Command:
+
+```text
+/media/haris-subrata/Work/llm/agent_analytics/venv/bin/python -m modal run train/modal_train.py --run-preflight --no-run-train
+```
+
+First preflight built the Unsloth image but failed local deserialization because
+the remote function returned a Python dict after importing `torch`, while local
+venv intentionally has no torch.
+
+Patch applied:
+
+- `preflight()` now returns JSON string.
+- Removed explicit `torch==2.6.0` install from training image because Unsloth
+  resolver installs `torch-2.10.0+cu128` anyway.
+
+Successful preflight result:
+
+```text
+hf_token_present: true
+torch: 2.10.0+cu128
+cuda runtime: 12.8
+cuda_available: true
+gpu: NVIDIA H100 80GB HBM3
+capability: sm_90
+device_count: 1
+transformers: 5.5.0
+trl: 0.24.0
+peft: 0.19.1
+datasets: 4.3.0
+unsloth: 2026.6.1
+nvidia-smi driver: 580.95.05
+nvidia-smi CUDA: 13.0
+```
+
+Unsloth message:
+
+```text
+Flash Attention 2 installation seems to be broken. Using Xformers instead.
+```
+
+This matches the upstream RunPod note that FA2 can be broken and Xformers is
+acceptable. Preflight is clean enough to proceed to training.


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
Success. Updated the following files:
M local-dev/log_dev.md


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


Output:
Chunk ID: 680b81
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:


Output:
Chunk ID: 941702
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 38
Output:
 M train/modal_train.py
!! .git-local/
!! data/
!! deploy/__pycache__/
!! eval/__pycache__/
!! local-dev/
!! scripts/__pycache__/
!! train/__pycache__/


Output:
Chunk ID: a712f0
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 391
Output:
diff --git a/train/modal_train.py b/train/modal_train.py
index 9c9e31a..90533cd 100644
--- a/train/modal_train.py
+++ b/train/modal_train.py
@@ -13,7 +13,6 @@ 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",
@@ -41,18 +40,19 @@ out_vol = modal.Volume.from_name("neurobait-out", create_if_missing=True)
     volumes={"/root/.cache/huggingface": hf_cache, "/out": out_vol},
     secrets=[modal.Secret.from_name("huggingface")],
 )
-def preflight() -> dict:
+def preflight() -> str:
     """Check the remote Modal runtime before spending a full training run."""

+    import json
     import os
     import subprocess

+    import unsloth
     import torch
+    import datasets
+    import peft
     import transformers
     import trl
-    import peft
-    import datasets
-    import unsloth

     nvidia_smi = subprocess.run(
         ["nvidia-smi"],
@@ -70,7 +70,7 @@ def preflight() -> dict:
             "device_count": torch.cuda.device_count(),
         }

-    return {
+    result = {
         "hf_token_present": bool(os.environ.get("HF_TOKEN")),
         "torch": torch.__version__,
         "cuda": torch.version.cuda,
@@ -84,6 +84,7 @@ def preflight() -> dict:
         "nvidia_smi": nvidia_smi.stdout[-4000:],
         "nvidia_smi_stderr": nvidia_smi.stderr[-1000:],
     }
+    return json.dumps(result, indent=2)

 @app.function(


Saya commit patch Modal preflight. Local-dev log tetap tidak ikut GitHub.


git --git-dir=.git-local --work-tree=. add train/modal_train.py


git --git-dir=.git-local --work-tree=. diff --cached --stat


Output:
Chunk ID: 62688a
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:


Output:
Chunk ID: 7412e6
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 23
Output:
 train/modal_train.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)


Commit dibuat dengan attribution Codex, lalu saya push.


git --git-dir=.git-local --work-tree=. commit -m "Fix Modal preflight serialization" -m "Co-authored-by: Codex <codex@openai.com>"


Output:
Chunk ID: 4c118b
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 25
Output:
[main d99d8d2] Fix Modal preflight serialization
 1 file changed, 7 insertions(+), 6 deletions(-)


Commit siap, saya push ke GitHub.


git --git-dir=.git-local --work-tree=. push

Do you want to allow network access to push the Modal preflight serialization fix to GitHub?


Output:
Chunk ID: 879fc2
Wall time: 1.4743 seconds
Process exited with code 0
Original token count: 25
Output:
To https://github.com/Subrata15/NeuroBait-Build-Small-Model.git
   b6b0b1e..d99d8d2  main -> main


Preflight training Modal sudah sukses. Ini hasil konfirmasi paling penting sebelum retrain:

```text
hf_token_present: true
torch: 2.10.0+cu128
cuda runtime: 12.8
cuda_available: true
gpu: NVIDIA H100 80GB HBM3
capability: sm_90
device_count: 1
transformers: 5.5.0
trl: 0.24.0
peft: 0.19.1
datasets: 4.3.0
unsloth: 2026.6.1
nvidia-smi driver: 580.95.05
nvidia-smi CUDA: 13.0
```

Ada dua hal yang saya tangani:
- Preflight awal gagal deserialize karena local venv tidak punya `torch`. Saya ubah `preflight()` agar return JSON string.
- Image awal install `torch==2.6.0` lalu Unsloth menggantinya ke `torch==2.10.0+cu128`. Saya hapus install Torch eksplisit supaya image lebih bersih.

Catatan Unsloth:
```text
Flash Attention 2 installation seems to be broken. Using Xformers instead.
```
Ini sesuai catatan upstream run #2: FA2 broken lalu fallback ke Xformers, tidak dianggap blocker.

Patch sudah commit dan push:
`d99d8d2 Fix Modal preflight serialization`

Kesimpulan: environment Modal siap untuk full retrain.


# 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


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


Saya akan dokumentasikan runbook retrain dan evaluasi sekarang, supaya urutan setelah training tidak bergantung pada ingatan chat: train, persist/download adapter, eval existing, lalu eval tambahan yang lebih umum dipakai untuk chat model.


Input:
*** Begin Patch
*** Add File: docs/retrain_runbook.md
+# Retrain Runbook
+
+This runbook is the execution checklist for NeuroBait run #3 on Modal.
+
+## Preconditions
+
+- Modal auth is configured with `modal token new`.
+- Modal secret exists:
+
+```bash
+modal secret list
+```
+
+Expected secret:
+
+```text
+huggingface
+```
+
+- Dataset exists locally but is not committed:
+
+```text
+data/train.jsonl
+data/eval.jsonl
+```
+
+- Dataset validation passes:
+
+```bash
+python scripts/validate_dataset.py
+```
+
+- Modal preflight passes:
+
+```bash
+modal run train/modal_train.py --run-preflight --no-run-train
+```
+
+## Training
+
+Run:
+
+```bash
+modal run train/modal_train.py
+```
+
+Expected run #3 training steps:
+
+```text
+ceil(270 / 8) * 3 = 102
+```
+
+Locked training settings:
+
+- Base: `unsloth/gemma-4-26b-a4b-it`
+- Method: 16-bit LoRA
+- LoRA: r=16, alpha=16, dropout=0
+- Epochs: 3
+- LR: 2e-4
+- Batch: 1 x grad_accum 8
+- Max sequence: 2048
+- Chat template: `gemma-4`
+- Response markers: `<|turn>user\n` / `<|turn>model\n`
+- Save strategy: `no`
+
+## Artifact Handling
+
+Training saves adapter to Modal Volume:
+
+```text
+/out/neurobait-lora-run3
+```
+
+Download adapter locally after training:
+
+```bash
+modal volume get neurobait-out neurobait-lora-run3 ./neurobait-lora-run3
+```
+
+Do not commit the adapter. It is ignored by `.gitignore`.
+
+Keep the Modal Volume copy until:
+
+- adapter is downloaded locally,
+- eval artifacts are generated,
+- final HF upload strategy is confirmed,
+- model has been pushed or otherwise backed up.
+
+## Immediate Post-Train Checks
+
+Check `train_summary.json` in the adapter directory:
+
+- `n_train` should be `270`
+- `n_eval` should be `30`
+- `expected_steps` should be `102`
+- confirm final train/eval metrics are present
+
+## Evaluation Order
+
+1. Existing/local heuristic eval:
+
+```bash
+python eval/eval_neurobait.py data/eval.jsonl --output outputs/eval_reference.json
+python eval/make_report.py outputs/eval_reference.json
+```
+
+2. GPU generation eval:
+
+- load base + LoRA once,
+- compare base vs fine-tuned,
+- use held-out eval turns,
+- include novel prompts,
+- include English-transfer prompts,
+- save JSON output,
+- render report.
+
+3. Additional general chat-model eval:
+
+Use a judge-based pairwise eval inspired by MT-Bench/AlpacaEval-style practice:
+
+- compare base vs fine-tuned responses blind,
+- judge with a fixed rubric,
+- report win/tie/loss rate,
+- evaluate helpfulness, persona fit, instruction following, concision, safety,
+  and language consistency,
+- include bilingual ID/EN prompt sets.
+
+The judge eval should not replace qualitative review; it adds a standardized
+second signal after the project-specific NeuroBait eval.
+
+## Upload And Deployment
+
+Do not push to Hugging Face until target org/repo names are confirmed.
+
+Expected repos:
+
+- adapter model repo
+- merged model repo
+- Space repo
+
+Push only after eval is acceptable.
*** Add File: docs/evaluation_plan.md
+# Evaluation Plan
+
+NeuroBait needs both project-specific evaluation and a more common chat-model
+evaluation pattern. The goal is to avoid relying on loss alone, because prior
+runs showed eval loss is a weak proxy for this style task.
+
+## Eval 1: NeuroBait-Specific Eval
+
+Purpose: test whether the fine-tune learned the intended persona and multi-turn
+momentum behavior.
+
+Core checks:
+
+- no label leakage such as `Micro-action`, `Hook`, `Stakes`
+- short, warm, natural prose
+- no guilt framing
+- preserves user agency
+- asks one useful question when context is insufficient
+- gives one tiny concrete action when context is sufficient
+- response length remains compact
+- conversation arc builds momentum over turns
+- English inputs do not degrade or trigger unwanted code-switching
+
+Inputs:
+
+- held-out `data/eval.jsonl`
+- novel ID prompts
+- novel EN prompts
+- app-initiated opener continuation cases
+
+Outputs:
+
+- JSON generations and scores
+- Markdown report
+- qualitative examples for reviewer inspection
+
+## Eval 2: General Chat-Model Pairwise Judge Eval
+
+Purpose: add a standardized second signal commonly used for instruction/chat
+models.
+
+Approach:
+
+- Generate responses from base and fine-tuned model for the same prompt set.
+- Hide model identity from the judge.
+- Ask a judge model to choose winner/tie under a fixed rubric.
+- Report win/tie/loss rate and category-level notes.
+
+This is similar in spirit to MT-Bench / AlpacaEval-style pairwise judging, but
+with a NeuroBait-specific rubric added.
+
+Suggested categories:
+
+- Helpfulness: does it move the user toward starting?
+- Persona fit: warm, agency-preserving, non-judgmental.
+- Instruction following: follows the system prompt without exposing structure.
+- Specificity: avoids generic motivation and uses context.
+- Concision: compact enough for ADHD-friendly interaction.
+- Safety: avoids medical overclaiming, shame, coercion, or crisis mishandling.
+- Language consistency: responds naturally in the user's language.
+
+Prompt set:
+
+- ID single-turn prompts
+- EN single-turn prompts
+- ID multi-turn continuations
+- EN multi-turn continuations
+- sparse-context prompts requiring one question
+- sufficient-context prompts requiring one micro-action
+- edge cases: shame, avoidance spiral, deadline panic, boredom, overwhelm
+
+Reporting:
+
+- overall fine-tuned win rate vs base
+- ID win/tie/loss
+- EN win/tie/loss
+- single-turn vs multi-turn split
+- safety failures
+- representative wins and losses
+
+Guardrails:
+
+- The judge prompt must forbid rewarding visible labels or long coaching plans.
+- Pairwise judge results are advisory, not final truth.
+- Human review remains required before upload/deploy.
*** Update File: README.md
@@
 Modal CLI notes are in `docs/modal_cli.md`.
+Retraining and evaluation steps are in `docs/retrain_runbook.md` and
+`docs/evaluation_plan.md`.
*** Update File: local-dev/log_dev.md
@@
 ## Immediate Next Actions

-1. Auth Modal locally:
-   `modal token new`
-2. Create Modal secret:
-   `modal secret create huggingface HF_TOKEN=...`
-3. Run local dataset validation:
+1. Run local dataset validation:
    `python scripts/validate_dataset.py`
-4. Run remote Modal preflight:
+2. Run remote Modal preflight:
    `modal run train/modal_train.py --run-preflight --no-run-train`
-5. Run training:
+3. Run training:
    `modal run train/modal_train.py`
-6. After training, run eval and only then push adapter/merged model.
+4. Download adapter from Modal Volume:
+   `modal volume get neurobait-out neurobait-lora-run3 ./neurobait-lora-run3`
+5. Run existing NeuroBait eval.
+6. Run additional pairwise judge eval.
+7. Only push adapter/merged model after eval passes and HF target org/repo is confirmed.
*** End Patch


Success. Updated the following files:
A docs/retrain_runbook.md
A docs/evaluation_plan.md
M README.md
M local-dev/log_dev.md

# Evaluation Plan

NeuroBait needs both project-specific evaluation and a more common chat-model
evaluation pattern. The goal is to avoid relying on loss alone, because prior
runs showed eval loss is a weak proxy for this style task.

## Eval 1: NeuroBait-Specific Eval

Purpose: test whether the fine-tune learned the intended persona and multi-turn
momentum behavior.

Core checks:

- no label leakage such as `Micro-action`, `Hook`, `Stakes`
- short, warm, natural prose
- no guilt framing
- preserves user agency
- asks one useful question when context is insufficient
- gives one tiny concrete action when context is sufficient
- response length remains compact
- conversation arc builds momentum over turns
- English inputs do not degrade or trigger unwanted code-switching

Inputs:

- held-out `data/eval.jsonl`
- novel ID prompts
- novel EN prompts
- app-initiated opener continuation cases

Outputs:

- JSON generations and scores
- Markdown report
- qualitative examples for reviewer inspection

## Eval 2: General Chat-Model Pairwise Judge Eval

Purpose: add a standardized second signal commonly used for instruction/chat
models.

Approach:

- Generate responses from base and fine-tuned model for the same prompt set.
- Hide model identity from the judge.
- Ask a judge model to choose winner/tie under a fixed rubric.
- Report win/tie/loss rate and category-level notes.

This is similar in spirit to MT-Bench / AlpacaEval-style pairwise judging, but
with a NeuroBait-specific rubric added.

Suggested categories:

- Helpfulness: does it move the user toward starting?
- Persona fit: warm, agency-preserving, non-judgmental.
- Instruction following: follows the system prompt without exposing structure.
- Specificity: avoids generic motivation and uses context.
- Concision: compact enough for ADHD-friendly interaction.
- Safety: avoids medical overclaiming, shame, coercion, or crisis mishandling.
- Language consistency: responds naturally in the user's language.

Prompt set:

- ID single-turn prompts
- EN single-turn prompts
- ID multi-turn continuations
- EN multi-turn continuations
- sparse-context prompts requiring one question
- sufficient-context prompts requiring one micro-action
- edge cases: shame, avoidance spiral, deadline panic, boredom, overwhelm

Reporting:

- overall fine-tuned win rate vs base
- ID win/tie/loss
- EN win/tie/loss
- single-turn vs multi-turn split
- safety failures
- representative wins and losses

Guardrails:

- The judge prompt must forbid rewarding visible labels or long coaching plans.
- Pairwise judge results are advisory, not final truth.
- Human review remains required before upload/deploy.

@@ -39,2 +39,4 @@
 Modal CLI notes are in `docs/modal_cli.md`.
+Retraining and evaluation steps are in `docs/retrain_runbook.md` and
+`docs/evaluation_plan.md`.

# Retrain Runbook

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

## Preconditions

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

```bash
modal secret list
```

Expected secret:

```text
huggingface
```

- Dataset exists locally but is not committed:

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

- Dataset validation passes:

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

- Modal preflight passes:

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

## Training

Run:

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

Expected run #3 training steps:

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

Locked training settings:

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

## Artifact Handling

Training saves adapter to Modal Volume:

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

Download adapter locally after training:

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

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

Keep the Modal Volume copy until:

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

## Immediate Post-Train Checks

Check `train_summary.json` in the adapter directory:

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

## Evaluation Order

1. Existing/local heuristic eval:

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

2. GPU generation eval:

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

3. Additional general chat-model eval:

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

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

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

## Upload And Deployment

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

Expected repos:

- adapter model repo
- merged model repo
- Space repo

Push only after eval is acceptable.

@@ -123,13 +123,13 @@

-1. Auth Modal locally:
-   `modal token new`
-2. Create Modal secret:
-   `modal secret create huggingface HF_TOKEN=...`
-3. Run local dataset validation:
+1. Run local dataset validation:
    `python scripts/validate_dataset.py`
-4. Run remote Modal preflight:
+2. Run remote Modal preflight:
    `modal run train/modal_train.py --run-preflight --no-run-train`
-5. Run training:
+3. Run training:
    `modal run train/modal_train.py`
-6. After training, run eval and only then push adapter/merged model.
+4. Download adapter from Modal Volume:
+   `modal volume get neurobait-out neurobait-lora-run3 ./neurobait-lora-run3`
+5. Run existing NeuroBait eval.
+6. Run additional pairwise judge eval.
+7. Only push adapter/merged model after eval passes and HF target org/repo is confirmed.


Output:
Exit code: 0
Wall time: 0.5 seconds
Output:
Success. Updated the following files:
A docs/retrain_runbook.md
A docs/evaluation_plan.md
M README.md
M local-dev/log_dev.md


Runbook dan evaluation plan sudah dibuat. Saya akan cek status lalu commit file publiknya; local-dev log tetap tidak ikut Git.


Output:
Chunk ID: dd94a6
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 49
Output:
 M README.md
?? docs/evaluation_plan.md
?? docs/retrain_runbook.md
!! .git-local/
!! data/
!! deploy/__pycache__/
!! eval/__pycache__/
!! local-dev/
!! scripts/__pycache__/
!! train/__pycache__/


Output:
Chunk ID: cd83c0
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 697
Output:
# Retrain Runbook

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

## Preconditions

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

```bash
modal secret list
```

Expected secret:

```text
huggingface
```

- Dataset exists locally but is not committed:

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

- Dataset validation passes:

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

- Modal preflight passes:

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

## Training

Run:

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

Expected run #3 training steps:

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

Locked training settings:

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

## Artifact Handling

Training saves adapter to Modal Volume:

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

Download adapter locally after training:

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

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

Keep the Modal Volume copy until:

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

## Immediate Post-Train Checks

Check `train_summary.json` in the adapter directory:

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

## Evaluation Order

1. Existing/local heuristic eval:

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

2. GPU generation eval:

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

3. Additional general chat-model eval:

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

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

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

## Upload And Deployment

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

Expected repos:

- adapter model repo
- merged model repo
- Space repo

Push only after eval is acceptable.


Output:
Chunk ID: 1f4575
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 665
Output:
# Evaluation Plan

NeuroBait needs both project-specific evaluation and a more common chat-model
evaluation pattern. The goal is to avoid relying on loss alone, because prior
runs showed eval loss is a weak proxy for this style task.

## Eval 1: NeuroBait-Specific Eval

Purpose: test whether the fine-tune learned the intended persona and multi-turn
momentum behavior.

Core checks:

- no label leakage such as `Micro-action`, `Hook`, `Stakes`
- short, warm, natural prose
- no guilt framing
- preserves user agency
- asks one useful question when context is insufficient
- gives one tiny concrete action when context is sufficient
- response length remains compact
- conversation arc builds momentum over turns
- English inputs do not degrade or trigger unwanted code-switching

Inputs:

- held-out `data/eval.jsonl`
- novel ID prompts
- novel EN prompts
- app-initiated opener continuation cases

Outputs:

- JSON generations and scores
- Markdown report
- qualitative examples for reviewer inspection

## Eval 2: General Chat-Model Pairwise Judge Eval

Purpose: add a standardized second signal commonly used for instruction/chat
models.

Approach:

- Generate responses from base and fine-tuned model for the same prompt set.
- Hide model identity from the judge.
- Ask a judge model to choose winner/tie under a fixed rubric.
- Report win/tie/loss rate and category-level notes.

This is similar in spirit to MT-Bench / AlpacaEval-style pairwise judging, but
with a NeuroBait-specific rubric added.

Suggested categories:

- Helpfulness: does it move the user toward starting?
- Persona fit: warm, agency-preserving, non-judgmental.
- Instruction following: follows the system prompt without exposing structure.
- Specificity: avoids generic motivation and uses context.
- Concision: compact enough for ADHD-friendly interaction.
- Safety: avoids medical overclaiming, shame, coercion, or crisis mishandling.
- Language consistency: responds naturally in the user's language.

Prompt set:

- ID single-turn prompts
- EN single-turn prompts
- ID multi-turn continuations
- EN multi-turn continuations
- sparse-context prompts requiring one question
- sufficient-context prompts requiring one micro-action
- edge cases: shame, avoidance spiral, deadline panic, boredom, overwhelm

Reporting:

- overall fine-tuned win rate vs base
- ID win/tie/loss
- EN win/tie/loss
- single-turn vs multi-turn split
- safety failures
- representative wins and losses

Guardrails:

- The judge prompt must forbid rewarding visible labels or long coaching plans.
- Pairwise judge results are advisory, not final truth.
- Human review remains required before upload/deploy.


Dokumen sudah sesuai. Saya commit dan push supaya runbook ada di repo sebelum training berjalan.


git --git-dir=.git-local --work-tree=. add README.md docs/retrain_runbook.md docs/evaluation_plan.md


Output:
Chunk ID: 58b24c
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:


Output:
Chunk ID: 1e1ba4
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 53
Output:
 README.md               |   2 +
 docs/evaluation_plan.md |  85 +++++++++++++++++++++++++++++
 docs/retrain_runbook.md | 141 ++++++++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 228 insertions(+)


Saya buat commit dengan attribution Codex, lalu push.


git --git-dir=.git-local --work-tree=. commit -m "Add retrain and evaluation runbooks" -m "Co-authored-by: Codex <codex@openai.com>"


Output:
Chunk ID: cfce50
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 44
Output:
[main 0d018db] Add retrain and evaluation runbooks
 3 files changed, 228 insertions(+)
 create mode 100644 docs/evaluation_plan.md
 create mode 100644 docs/retrain_runbook.md


Do you want to allow network access to push the retrain and evaluation runbooks to GitHub?


Output:
Chunk ID: 43dc0e
Wall time: 1.5046 seconds
Process exited with code 0
Original token count: 25
Output:
To https://github.com/Subrata15/NeuroBait-Build-Small-Model.git
   d99d8d2..0d018db  main -> main


Siap, sudah saya dokumentasikan sebelum retrain jalan.

Ditambahkan:
- [docs/retrain_runbook.md](/media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/docs/retrain_runbook.md:1)
- [docs/evaluation_plan.md](/media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/docs/evaluation_plan.md:1)
- README link ke dua dokumen itu.

Isi runbook sudah mencakup:
- precondition Modal/HF secret/dataset validation
- command retrain
- expected steps `102`
- adapter disimpan di `/out/neurobait-lora-run3`
- download adapter lokal:
  ```bash
  modal volume get neurobait-out neurobait-lora-run3 ./neurobait-lora-run3
  ```
- adapter jangan di-commit
- setelah retrain langsung eval

Untuk evaluasi tambahan, saya dokumentasikan **pairwise judge eval** yang umum dipakai untuk chat/instruction model, ala MT-Bench/AlpacaEval-style:
- base vs fine-tuned blind comparison
- win/tie/loss rate
- rubric: helpfulness, persona fit, instruction following, specificity, concision, safety, language consistency
- split ID/EN dan single/multi-turn

Sudah commit dan push:
`0d018db Add retrain and evaluation runbooks`


Summary: auto


ok jalankan retrain sekarang


Saya mulai retrain run #3 sekarang dengan Modal H100. Saya akan monitor output sampai selesai, lalu cek summary, download adapter lokal, dan lanjut evaluasi sesuai runbook.


/media/haris-subrata/Work/llm/agent_analytics/venv/bin/python -m modal run train/modal_train.py

Do you want to allow network access to start the full Modal H100 retraining run for NeuroBait run #3?


Output:
Chunk ID: 22fcb8
Wall time: 30.0014 seconds
Process running with session ID 94523
Original token count: 210
Output:
✓ Initialized. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-YGUakrjOKifjD9XMVAGi8R
✓ Created objects.
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/train/modal_train.
│   py
├── 🔨 Created mount data/eval.jsonl
├── 🔨 Created mount train/train_core.py
├── 🔨 Created mount data/train.jsonl
├── 🔨 Created function preflight.
├── 🔨 Created function train.
├── 🔨 Created function push_lora_to_hub.
└── 🔨 Created function merge_and_push.
🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.
Unsloth: Your Flash Attention 2 installation seems to be broken. Using Xformers instead. No performance changes will be seen.
🦥 Unsloth Zoo will now patch everything to make training faster!


Training function sudah start, image dan mounts siap. Saat ini masuk fase Unsloth/model load; saya lanjut polling output.


Output:
Chunk ID: 349fe7
Wall time: 30.0016 seconds
Process running with session ID 94523
Original token count: 146
Output:
>>> loading base unsloth/gemma-4-26b-a4b-it with 16-bit LoRA
==((====))==  Unsloth 2026.6.1: Fast Gemma4 patching. Transformers: 5.5.0.
   \\   /|    NVIDIA H100 80GB HBM3. Num GPUs = 1. Max memory: 79.179 GB. Platform: Linux.
O^O/ \_/ \    Torch: 2.10.0+cu128. CUDA: 9.0. CUDA Toolkit: 12.8. Triton: 3.6.0
\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.35. FA2 = False]
 "-____-"     Free license: http://github.com/unslothai/unsloth
Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!

Fetching 2 files:   0%|          | 0/2 [00:00<?, ?it/s]


Model base sedang di-download/load dari HF cache Modal. H100 terdeteksi dengan memori ~79GB dan BF16 aktif.


Output:
Chunk ID: 0d62ab
Wall time: 60.0016 seconds
Process running with session ID 94523
Original token count: 0
Output:


Belum ada output baru; kemungkinan masih download base model besar ke volume cache HF. Ini expected untuk run pertama di Modal karena base sekitar puluhan GB.


Output:
Chunk ID: 779d00
Wall time: 120.0015 seconds
Process running with session ID 94523
Original token count: 1309
Output:
Fetching 2 files:  50%|█████     | 1/2 [02:13<02:13, 133.59s/it]Fetching 2 files: 100%|██████████| 2/2 [02:13<00:00, 66.79s/it]

Loading weights:   0%|          | 0/1013 [00:00<?, ?it/s]
Loading weights:   0%|          | 4/1013 [00:00<00:26, 38.59it/s]
Loading weights:   3%|▎         | 26/1013 [00:00<00:12, 80.66it/s]
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Loading weights:  11%|█▏        | 114/1013 [00:01<00:11, 79.65it/s]
Loading weights:  13%|█▎        | 134/1013 [00:01<00:08, 100.50it/s]
Loading weights:  14%|█▍        | 146/1013 [00:01<00:09, 91.48it/s]
Loading weights:  15%|█▌        | 157/1013 [00:01<00:11, 73.69it/s]
Loading weights:  18%|█▊        | 179/1013 [00:02<00:10, 79.75it/s]
Loading weights:  20%|█▉        | 200/1013 [00:02<00:08, 100.89it/s]
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Unsloth: Detected MoE model with num_experts = 128 and target_modules = ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj']. Enabling LoRA on MoE parameters: ['experts.gate_up_proj', 'experts.down_proj']

Generating train split: 0 examples [00:00, ? examples/s]
Generating train split: 270 examples [00:00, 19758.22 examples/s]

Map:   0%|          | 0/270 [00:00<?, ? examples/s]
Map: 100%|██████████| 270/270 [00:00<00:00, 2637.36 examples/s]
Map: 100%|██████████| 270/270 [00:00<00:00, 2574.29 examples/s]

Generating train split: 0 examples [00:00, ? examples/s]
Generating train split: 30 examples [00:00, 7826.17 examples/s]

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Map: 100%|██████████| 30/30 [00:00<00:00, 1818.18 examples/s]
>>> train=270 eval=30
>>> pre-flight marker OK '<|turn>user\n' '<|turn>model\n'
warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead.
warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead.

Unsloth: Tokenizing ["text"] (num_proc=21):   0%|          | 0/270 [00:00<?, ? examples/s]
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Unsloth: Tokenizing ["text"] (num_proc=21):  29%|██▉       | 78/270 [00:24<00:53,  3.59 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=21):  34%|███▎      | 91/270 [00:28<00:49,  3.61 examples/s]


Base model sudah selesai download/load, dataset 270/30 valid di remote, marker Gemma 4 lolos, tokenisasi sedang berjalan. Langkah train 102 belum mulai atau baru akan mulai setelah tokenisasi selesai.