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

---
base_model: unsloth/gemma-4-26b-a4b-it
library_name: peft
pipeline_tag: text-generation
tags:
  - lora
  - peft
  - gemma-4
  - adhd
  - neurodivergent
  - task-initiation
  - build-small-hackathon
license: other
---

# NeuroBait

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

This repo contains the LoRA adapter, tokenizer files, chat template, and the
training summary for run #3. It is intended to be used by the Gradio Space:

```text
https://huggingface.co/spaces/build-small-hackathon/NeuroBait
```

## Intended Behavior

NeuroBait should:

- respond in short, natural prose,
- avoid visible labels such as `Micro-action`, `Hook`, or `Stakes`,
- avoid guilt framing,
- preserve user agency,
- ask one light question when context is too sparse,
- offer one tiny concrete action when enough context exists.

It should not act as a medical device, diagnostic tool, therapist, emergency
support system, or replacement for professional care.

## Training Data

Run #3 used a bilingual Indonesian/English conversational dataset:

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

The dataset is not included in this model repo.

## Training Configuration

- Base: `unsloth/gemma-4-26b-a4b-it`
- Method: 16-bit LoRA
- LoRA rank: 16
- LoRA alpha: 16
- LoRA dropout: 0
- Target modules: q/k/v/o/gate/up/down projections
- Epochs: 3
- Learning rate: 2e-4
- Effective batch size: 8
- Max sequence length: 2048
- Scheduler: cosine
- Warmup ratio: 0.05
- Optimizer: adamw 8-bit
- Precision: bf16
- Chat template: `gemma-4`
- Response-only markers: `<|turn>user\n` / `<|turn>model\n`

Training ran on Modal with an H100 80GB GPU.

## Run #3 Results

Training completed 102 steps.

Training summary:

- train conversations: 270
- eval conversations: 30
- train loss: 0.242
- final `trainer.evaluate()` loss: 2.4044

The loss signal should be treated as a weak training diagnostic for this project.
NeuroBait is primarily evaluated through generated behavior against the base
model.

Generation eval summary over 8 held-out/novel prompts:

- base persona average: 3.625 / 4
- fine-tuned persona average: 4.0 / 4
- base average words: 72.0
- fine-tuned average words: 56.625
- base label leaks: 1
- fine-tuned label leaks: 0
- base action-cue responses: 3
- fine-tuned action-cue responses: 5

Qualitatively, the fine-tuned adapter produced shorter, more conversational
responses, maintained English on English prompts more reliably than the base
model in this sample, and avoided literal structure labels in the tested turns.

## Loading

Example adapter loading path:

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

base_model = "unsloth/gemma-4-26b-a4b-it"
adapter_id = "build-small-hackathon/NeuroBait"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.bfloat16,
        bnb_4bit_quant_type="nf4",
    ),
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter_id)
model.eval()
```

## Limitations

- The model is not a medical or crisis-support system.
- It may ask for a motivator or deadline when the user has already implied one.
- It may still be too brief or too playful for some users.
- Long-term personalization should be handled by product logic, not only by the
  model.
- App-initiated reminders should be handled by the app layer, not generated
  spontaneously by the model.

## Submission Context

This model is part of the Build Small Model Hackathon submission for NeuroBait.
The public GitHub source repo tracks training, evaluation, deployment scaffolding,
and agent development notes.


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


Saya juga akan perbaiki runbook karena perintah download adapter sebelumnya ternyata punya edge case dan kita sudah pakai cara download file satu per satu. Ini penting untuk agent berikutnya.


Input:
*** Begin Patch
*** Update File: README.md
@@
-## Architecture
+## Hugging Face Targets
+
+Short public slugs keep the hackathon URLs readable:
+
+- Model repo: `https://huggingface.co/build-small-hackathon/NeuroBait`
+- Space app: `https://huggingface.co/spaces/build-small-hackathon/NeuroBait`
+
+The visible app title remains `NeuroBait`, with the submission/story context
+documented in this GitHub repo and the model/Space cards.
+
+## Architecture
@@
-Optional upload/merge:
+Optional upload/merge:

 ```bash
-modal run train/modal_train.py --push-lora
-modal run train/modal_train.py --push-lora --merge
+modal run train/modal_train.py --no-run-train --push-lora
+modal run train/modal_train.py --no-run-train --push-space
+modal run train/modal_train.py --no-run-train --push-lora --merge
 ```
@@
-Set `MODEL_ID` in the Space environment to the merged HF Model repo, for example:
+The default Space app loads the LoRA adapter from:

 ```text
-USER/neurobait-gemma4-26b-a4b
+ADAPTER_ID=build-small-hackathon/NeuroBait
+BASE_MODEL=unsloth/gemma-4-26b-a4b-it
+LOAD_IN_4BIT=1
 ```

-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.
+The Space uses Gradio and `@spaces.GPU`. Hardware must be chosen based on the
+final model format. The current deployment path uses 4-bit base loading plus the
+LoRA adapter, but it still needs direct testing on HF Space hardware because the
+base model is a 26B MoE model.
*** End Patch


Success. Updated the following files:
M README.md

@@ -7,2 +7,12 @@

+## Hugging Face Targets
+
+Short public slugs keep the hackathon URLs readable:
+
+- Model repo: `https://huggingface.co/build-small-hackathon/NeuroBait`
+- Space app: `https://huggingface.co/spaces/build-small-hackathon/NeuroBait`
+
+The visible app title remains `NeuroBait`, with the submission/story context
+documented in this GitHub repo and the model/Space cards.
+
 ## Architecture
@@ -85,4 +95,5 @@
 ```bash
-modal run train/modal_train.py --push-lora
-modal run train/modal_train.py --push-lora --merge
+modal run train/modal_train.py --no-run-train --push-lora
+modal run train/modal_train.py --no-run-train --push-space
+modal run train/modal_train.py --no-run-train --push-lora --merge
 ```
@@ -130,6 +141,8 @@

-Set `MODEL_ID` in the Space environment to the merged HF Model repo, for example:
+The default Space app loads the LoRA adapter from:

 ```text
-USER/neurobait-gemma4-26b-a4b
+ADAPTER_ID=build-small-hackathon/NeuroBait
+BASE_MODEL=unsloth/gemma-4-26b-a4b-it
+LOAD_IN_4BIT=1
 ```
@@ -137,4 +150,5 @@
 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.
+final model format. The current deployment path uses 4-bit base loading plus the
+LoRA adapter, but it still needs direct testing on HF Space hardware because the
+base model is a 26B MoE model.


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Output:
Success. Updated the following files:
M README.md


Input:
*** Begin Patch
*** Update File: deploy/README.md
@@
-This Space loads:
+Target URL:
+
+```text
+https://huggingface.co/spaces/build-small-hackathon/NeuroBait
+```
+
+This Space loads:
*** End Patch


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

@@ -17,2 +17,8 @@

+Target URL:
+
+```text
+https://huggingface.co/spaces/build-small-hackathon/NeuroBait
+```
+
 This Space loads:


Output:
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Wall time: 0.1 seconds
Output:
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M deploy/README.md


Input:
*** Begin Patch
*** Update File: docs/retrain_runbook.md
@@
 Download adapter locally after training:

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

+If that creates a plain file instead of a directory, remove it and download the
+artifact files one by one from `modal volume ls neurobait-out neurobait-lora-run3`.
+
 Do not commit the adapter. It is ignored by `.gitignore`.
@@
-Expected repos:
+Current public target repos:

-- adapter model repo
-- merged model repo
-- Space repo
+- model adapter repo: `build-small-hackathon/NeuroBait`
+- Space repo: `build-small-hackathon/NeuroBait`
+- optional merged model repo: `build-small-hackathon/NeuroBait-Merged`

 Push only after eval is acceptable.
*** End Patch


Success. Updated the following files:
M docs/retrain_runbook.md

@@ -79,2 +79,5 @@

+If that creates a plain file instead of a directory, remove it and download the
+artifact files one by one from `modal volume ls neurobait-out neurobait-lora-run3`.
+
 Do not commit the adapter. It is ignored by `.gitignore`.
@@ -134,7 +137,7 @@

-Expected repos:
+Current public target repos:

-- adapter model repo
-- merged model repo
-- Space repo
+- model adapter repo: `build-small-hackathon/NeuroBait`
+- Space repo: `build-small-hackathon/NeuroBait`
+- optional merged model repo: `build-small-hackathon/NeuroBait-Merged`


Output:
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Wall time: 0.1 seconds
Output:
Success. Updated the following files:
M docs/retrain_runbook.md


Dokumentasi target URL sudah masuk. Saya akan cek sintaks dan konfigurasi dulu, lalu baru publish ke Hugging Face via Modal kalau valid.


/media/haris-subrata/Work/llm/agent_analytics/venv/bin/python -m py_compile deploy/app.py train/modal_train.py eval/modal_generation_eval.py scripts/validate_dataset.py


git --git-dir=.git-local --work-tree=. diff -- train/modal_train.py deploy/app.py deploy/README.md deploy/requirements.txt model-card/README.md README.md docs/retrain_runbook.md


Output:
Chunk ID: 0e8966
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:


Output:
Chunk ID: 0bea98
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 81
Output:
[
  {
    "path": "data/train.jsonl",
    "rows": 270,
    "turns": {
      "system": 270,
      "user": 1356,
      "assistant": 1356
    },
    "errors": []
  },
  {
    "path": "data/eval.jsonl",
    "rows": 30,
    "turns": {
      "system": 30,
      "user": 164,
      "assistant": 164
    },
    "errors": []
  }
]


Output:
Chunk ID: 80987f
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 6314
Output:
diff --git a/README.md b/README.md
index 19014ba..809ea26 100644
--- a/README.md
+++ b/README.md
@@ -5,6 +5,16 @@ This repo is the engineering source of truth for training, evaluation, and
 deployment scaffolding. Dataset and model artifacts are intentionally kept out
 of Git.

+## Hugging Face Targets
+
+Short public slugs keep the hackathon URLs readable:
+
+- Model repo: `https://huggingface.co/build-small-hackathon/NeuroBait`
+- Space app: `https://huggingface.co/spaces/build-small-hackathon/NeuroBait`
+
+The visible app title remains `NeuroBait`, with the submission/story context
+documented in this GitHub repo and the model/Space cards.
+
 ## Architecture

 There are three separate operational artifacts:
@@ -83,8 +93,9 @@ modal run train/modal_train.py --run-preflight --no-run-train
 Optional upload/merge:

 ```bash
-modal run train/modal_train.py --push-lora
-modal run train/modal_train.py --push-lora --merge
+modal run train/modal_train.py --no-run-train --push-lora
+modal run train/modal_train.py --no-run-train --push-space
+modal run train/modal_train.py --no-run-train --push-lora --merge
 ```

 Locked training design:
@@ -128,15 +139,18 @@ deploy/app.py
 deploy/requirements.txt
 ```

-Set `MODEL_ID` in the Space environment to the merged HF Model repo, for example:
+The default Space app loads the LoRA adapter from:

 ```text
-USER/neurobait-gemma4-26b-a4b
+ADAPTER_ID=build-small-hackathon/NeuroBait
+BASE_MODEL=unsloth/gemma-4-26b-a4b-it
+LOAD_IN_4BIT=1
 ```

 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.
+final model format. The current deployment path uses 4-bit base loading plus the
+LoRA adapter, but it still needs direct testing on HF Space hardware because the
+base model is a 26B MoE model.

 ## Repository Boundaries

diff --git a/deploy/README.md b/deploy/README.md
index 9739946..7264e5e 100644
--- a/deploy/README.md
+++ b/deploy/README.md
@@ -1,31 +1,45 @@
-# NeuroBait HF Space
+---
+title: NeuroBait
+colorFrom: green
+colorTo: blue
+sdk: gradio
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: ADHD-friendly task initiation with one tiny next move.
+---

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

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

-- `app.py`: Gradio chat app
-- `requirements.txt`: Space dependencies
-
-## Space Configuration
-
-Set this environment variable in the HF Space:
+Target URL:

 ```text
-MODEL_ID=USER/neurobait-gemma4-26b-a4b
+https://huggingface.co/spaces/build-small-hackathon/NeuroBait
 ```

-The model repo should point to the merged model intended for inference.
+This Space loads:
+
+- Base model: `unsloth/gemma-4-26b-a4b-it`
+- LoRA adapter: `build-small-hackathon/NeuroBait`

-## Hardware
+## Runtime

-A merged bf16 Gemma 4 26B-A4B model is large. Use a GPU tier that can actually
-load the final model, or switch the app to a quantized loading path before using
-smaller hardware.
+The default app path uses 4-bit loading for the base model plus the NeuroBait
+LoRA adapter. The Space still needs a large GPU tier or ZeroGPU allocation that
+can load a 26B MoE model.

-## App-Initiated Openers
+Expected environment variables:
+
+```text
+BASE_MODEL=unsloth/gemma-4-26b-a4b-it
+ADAPTER_ID=build-small-hackathon/NeuroBait
+LOAD_IN_4BIT=1
+MAX_NEW_TOKENS=260
+```

-Proactive openers belong in application logic. If the app wants to start with an
-assistant message, inject that opener into chat history before the next user
-turn. Do not ask the model to decide when to initiate contact.
+If the base model requires authentication in the Space runtime, add `HF_TOKEN`
+as a Space secret.
diff --git a/deploy/app.py b/deploy/app.py
index d7e0c83..63cd869 100644
--- a/deploy/app.py
+++ b/deploy/app.py
@@ -3,14 +3,22 @@
 from __future__ import annotations

 import os
+import re
+from threading import Lock

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

-MODEL_ID = os.environ.get("MODEL_ID", "USER/neurobait-gemma4-26b-a4b")
+BASE_MODEL = os.environ.get("BASE_MODEL", "unsloth/gemma-4-26b-a4b-it")
+ADAPTER_ID = os.environ.get("ADAPTER_ID", os.environ.get("MODEL_ID", "build-small-hackathon/NeuroBait"))
+MAX_NEW_TOKENS = int(os.environ.get("MAX_NEW_TOKENS", "260"))
+LOAD_IN_4BIT = os.environ.get("LOAD_IN_4BIT", "1").lower() not in {"0", "false", "no"}
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
 SYSTEM_PROMPT = """Kamu adalah NeuroBait — asisten AI untuk orang dengan ADHD dan neurodivergent. Tugasmu bukan membuat to-do list. Tugasmu menyalakan dopamin untuk memicu task initiation.

 Dari setiap percakapan, identifikasi dua elemen kunci: (1) deadline anchor — momen nyata atau buatan yang bisa jadi batas waktu relevan; dan (2) object/subject motivator — orang atau hal yang paling emosional signifikan bagi user saat ini. Gunakan keduanya sebagai bahan bakar Resep Engagement yang personal, bukan generik.
@@ -22,17 +30,12 @@ Kalau konteks user belum cukup untuk membuat resep yang personal, ajukan tepat s
 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",
-)
-
+_model = None
+_tokenizer = None
+_load_lock = Lock()

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

+def _content_blocks(messages: list[dict]) -> list[dict]:
     normalized = []
     for message in messages:
         content = message["content"]
@@ -42,51 +45,257 @@ def _mm(messages: list[dict]) -> list[dict]:
     return normalized

-def _history_to_messages(history: list) -> list[dict]:
+def _load_model():
+    global _model, _tokenizer
+
+    with _load_lock:
+        if _model is not None and _tokenizer is not None:
+            return _model, _tokenizer
+
+        tokenizer = AutoTokenizer.from_pretrained(ADAPTER_ID, token=HF_TOKEN)
+        quantization_config = None
+        torch_dtype = torch.bfloat16
+        if LOAD_IN_4BIT:
+            quantization_config = BitsAndBytesConfig(
+                load_in_4bit=True,
+                bnb_4bit_compute_dtype=torch.bfloat16,
+                bnb_4bit_quant_type="nf4",
+            )
+            torch_dtype = None
+
+        base = AutoModelForCausalLM.from_pretrained(
+            BASE_MODEL,
+            token=HF_TOKEN,
+            torch_dtype=torch_dtype,
+            quantization_config=quantization_config,
+            device_map="auto",
+        )
+        model = PeftModel.from_pretrained(base, ADAPTER_ID, token=HF_TOKEN)
+        model.eval()
+
+        _model = model
+        _tokenizer = tokenizer
+        return _model, _tokenizer
+
+
+def _history_to_messages(history: list[dict]) -> list[dict]:
     messages = []
     for item in history:
-        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})
+        role = item.get("role")
+        content = item.get("content")
+        if role in {"user", "assistant"} and isinstance(content, str) and content.strip():
+            messages.append({"role": role, "content": content.strip()})
     return messages

-@spaces.GPU
-def chat(message: str, history: list) -> str:
+def _clean_response(text: str) -> str:
+    text = text.strip()
+    text = re.sub(r"(?im)^\s*(micro-action|hook|stakes|validasi|validation)\s*:\s*", "", text)
+    return text.strip()
+
+
+@spaces.GPU(duration=120)
+def respond(message: str, history: list[dict], temperature: float, top_p: float) -> str:
+    model, tokenizer = _load_model()
     messages = [{"role": "system", "content": SYSTEM_PROMPT}]
     messages.extend(_history_to_messages(history))
-    messages.append({"role": "user", "content": message})
+    messages.append({"role": "user", "content": message.strip()})

     input_ids = tokenizer.apply_chat_template(
-        _mm(messages),
+        _content_blocks(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,
+    start = input_ids.shape[-1]
+
+    with torch.no_grad():
+        output_ids = model.generate(
+            input_ids,
+            max_new_tokens=MAX_NEW_TOKENS,
+            do_sample=True,
+            temperature=temperature,
+            top_p=top_p,
+            repetition_penalty=1.1,
+            pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id,
+        )
+    text = tokenizer.decode(output_ids[0][start:], skip_special_tokens=True)
+    return _clean_response(text)
+
+
+CSS = """
+:root {
+  --nb-ink: #202624;
+  --nb-muted: #61706b;
+  --nb-paper: #faf7f0;
+  --nb-panel: #fffdf8;
+  --nb-line: #d8ded2;
+  --nb-sage: #6f8f7a;
+  --nb-clay: #b36a4c;
+  --nb-blue: #536d89;
+}
+body, .gradio-container {
+  background: var(--nb-paper);
+  color: var(--nb-ink);
+  font-family: Inter, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
+}
+.nb-shell {
+  max-width: 1120px;
+  margin: 0 auto;
+}
+.nb-title {
+  display: flex;
+  align-items: baseline;
+  justify-content: space-between;
+  gap: 16px;
+  padding: 18px 0 10px;
+  border-bottom: 1px solid var(--nb-line);
+}
+.nb-title h1 {
+  margin: 0;
+  font-size: 28px;
+  line-height: 1.1;
+  letter-spacing: 0;
+}
+.nb-title p {
+  margin: 0;
+  color: var(--nb-muted);
+  font-size: 14px;
+}
+.nb-layout {
+  display: grid;
+  grid-template-columns: minmax(0, 1fr) 280px;
+  gap: 18px;
+  margin-top: 18px;
+}
+.nb-side {
+  border-left: 1px solid var(--nb-line);
+  padding-left: 18px;
+}
+.nb-side h2 {
+  font-size: 14px;
+  margin: 0 0 10px;
+}
+.nb-side p {
+  color: var(--nb-muted);
+  font-size: 13px;
+  line-height: 1.45;
+}
+.nb-chatbot {
+  min-height: 580px;
+  border: 1px solid var(--nb-line);
+  background: var(--nb-panel);
+}
+.nb-chatbot .message {
+  border-radius: 8px !important;
+}
+.nb-input textarea {
+  border-radius: 8px !important;
+}
+.nb-examples button {
+  border-radius: 8px !important;
+  min-height: 38px;
+  text-align: left;
+}
+.nb-controls {
+  gap: 10px;
+}
+.nb-controls .wrap {
+  border-radius: 8px !important;
+}
+button.primary {
+  background: var(--nb-sage) !important;
+  border-color: var(--nb-sage) !important;
+}
+@media (max-width: 860px) {
+  .nb-title {
+    display: block;
+  }
+  .nb-title p {
+    margin-top: 8px;
+  }
+  .nb-layout {
+    grid-template-columns: 1fr;
+  }
+  .nb-side {
+    border-left: 0;
+    border-top: 1px solid var(--nb-line);
+    padding-left: 0;
+    padding-top: 14px;
+  }
+}
+"""
+
+
+with gr.Blocks(css=CSS, theme=gr.themes.Soft(primary_hue="green", neutral_hue="stone")) as demo:
+    gr.HTML(
+        """
+        <div class="nb-shell">
+          <div class="nb-title">
+            <h1>NeuroBait</h1>
+            <p>ADHD-friendly task initiation, tuned for one tiny next move.</p>
+          </div>
+        </div>
+        """
     )
-    return tokenizer.decode(output_ids[0][input_ids.shape[-1] :], skip_special_tokens=True).strip()
+    with gr.Row(elem_classes=["nb-shell", "nb-layout"]):
+        with gr.Column(scale=1):
+            chatbot = gr.Chatbot(
+                type="messages",
+                height=580,
+                show_copy_button=True,
+                elem_classes=["nb-chatbot"],
+            )
+            message = gr.Textbox(
+                placeholder="What are you avoiding right now?",
+                lines=2,
+                max_lines=5,
+                show_label=False,
+                elem_classes=["nb-input"],
+            )
+            with gr.Row():
+                submit = gr.Button("Start", variant="primary")
+                clear = gr.Button("Clear")
+            gr.Examples(
+                examples=[
+                    "I need to finish a presentation tomorrow, but I keep avoiding the first slide.",
+                    "My inbox is so messy that I freeze every time I open it.",
+                    "I feel stuck and I don't know where to start.",
+                ],
+                inputs=message,
+                elem_classes=["nb-examples"],
+            )
+        with gr.Column(scale=0, min_width=260, elem_classes=["nb-side"]):
+            gr.HTML(
+                """
+                <h2>Session feel</h2>
+                <p>No streaks, shame, or productivity theatre. NeuroBait looks for the emotional anchor and turns it into a small action you can start now.</p>
+                """
+            )
+            with gr.Accordion("Generation", open=False):
+                temperature = gr.Slider(0.2, 1.0, value=0.7, step=0.05, label="Temperature")
+                top_p = gr.Slider(0.6, 1.0, value=0.9, step=0.05, label="Top p")
+
+    def user_turn(user_message: str, chat_history: list[dict]) -> tuple[str, list[dict]]:
+        if not user_message.strip():
+            return "", chat_history
+        return "", chat_history + [{"role": "user", "content": user_message.strip()}]

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

-demo = gr.ChatInterface(
-    fn=chat,
-    title="NeuroBait",
-    description="Asisten task-initiation untuk otak ADHD dan neurodivergent.",
-)
+    submit.click(user_turn, [message, chatbot], [message, chatbot], queue=False).then(
+        bot_turn,
+        [chatbot, temperature, top_p],
+        chatbot,
+    )
+    message.submit(user_turn, [message, chatbot], [message, chatbot], queue=False).then(
+        bot_turn,
+        [chatbot, temperature, top_p],
+        chatbot,
+    )
+    clear.click(lambda: [], outputs=chatbot, queue=False)

 if __name__ == "__main__":
diff --git a/deploy/requirements.txt b/deploy/requirements.txt
index 06e90a4..7824238 100644
--- a/deploy/requirements.txt
+++ b/deploy/requirements.txt
@@ -1,6 +1,8 @@
 spaces
 gradio
 transformers==5.5.0
+peft==0.19.1
 accelerate
 torch
 bitsandbytes
+huggingface_hub
diff --git a/docs/retrain_runbook.md b/docs/retrain_runbook.md
index a914b2b..77f0a89 100644
--- a/docs/retrain_runbook.md
+++ b/docs/retrain_runbook.md
@@ -77,6 +77,9 @@ Download adapter locally after training:
 modal volume get neurobait-out neurobait-lora-run3 ./neurobait-lora-run3
 ```

+If that creates a plain file instead of a directory, remove it and download the
+artifact files one by one from `modal volume ls neurobait-out neurobait-lora-run3`.
+
 Do not commit the adapter. It is ignored by `.gitignore`.

 Keep the Modal Volume copy until:
@@ -132,10 +135,10 @@ second signal after the project-specific NeuroBait eval.

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

-Expected repos:
+Current public target repos:

-- adapter model repo
-- merged model repo
-- Space repo
+- model adapter repo: `build-small-hackathon/NeuroBait`
+- Space repo: `build-small-hackathon/NeuroBait`
+- optional merged model repo: `build-small-hackathon/NeuroBait-Merged`

 Push only after eval is acceptable.
diff --git a/model-card/README.md b/model-card/README.md
index 90c5fff..00eb4f9 100644
--- a/model-card/README.md
+++ b/model-card/README.md
@@ -1,58 +1,146 @@
-# NeuroBait Gemma 4 26B-A4B
+---
+base_model: unsloth/gemma-4-26b-a4b-it
+library_name: peft
+pipeline_tag: text-generation
+tags:
+  - lora
+  - peft
+  - gemma-4
+  - adhd
+  - neurodivergent
+  - task-initiation
+  - build-small-hackathon
+license: other
+---
+
+# NeuroBait

-Draft model card for the Hugging Face Model repo.
+NeuroBait is a LoRA fine-tune of `unsloth/gemma-4-26b-a4b-it` for ADHD and
+neurodivergent task-initiation conversations. It is designed to produce warm,
+short, agency-preserving prose that helps a user start one tiny next move
+without turning the conversation into a full to-do list.

-## Model Summary
+This repo contains the LoRA adapter, tokenizer files, chat template, and the
+training summary for run #3. It is intended to be used by the Gradio Space:

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

-## Base Model
+## Intended Behavior

-- Base: `unsloth/gemma-4-26b-a4b-it`
-- Method: 16-bit LoRA
-- License inheritance: verify from the base model card before publishing
+NeuroBait should:
+
+- respond in short, natural prose,
+- avoid visible labels such as `Micro-action`, `Hook`, or `Stakes`,
+- avoid guilt framing,
+- preserve user agency,
+- ask one light question when context is too sparse,
+- offer one tiny concrete action when enough context exists.
+
+It should not act as a medical device, diagnostic tool, therapist, emergency
+support system, or replacement for professional care.

 ## Training Data

-The run #3 dataset is bilingual Indonesian/English:
+Run #3 used a bilingual Indonesian/English conversational dataset:

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

-Dataset files are not included in the source repo or model repo unless explicitly
-published later.
-
-## Intended Use
-
-This model is intended for a Gradio chat demo and research/product exploration
-around ADHD-friendly task initiation support.
-
-## Limitations
-
-- Not a medical device.
-- Not a replacement for professional mental-health support.
-- May fail to infer the right motivator or deadline anchor from sparse context.
-- English transfer must be evaluated separately for run #3.
-- App-initiated reminders should be handled by app logic, not generated
-  spontaneously by the model.
+The dataset is not included in this model repo.

 ## Training Configuration

+- Base: `unsloth/gemma-4-26b-a4b-it`
+- Method: 16-bit LoRA
 - LoRA rank: 16
 - LoRA alpha: 16
 - LoRA dropout: 0
+- Target modules: q/k/v/o/gate/up/down projections
 - Epochs: 3
 - Learning rate: 2e-4
 - Effective batch size: 8
 - Max sequence length: 2048
+- Scheduler: cosine
+- Warmup ratio: 0.05
+- Optimizer: adamw 8-bit
+- Precision: bf16
 - Chat template: `gemma-4`
 - Response-only markers: `<|turn>user\n` / `<|turn>model\n`

-## Evaluation
+Training ran on Modal with an H100 80GB GPU.
+
+## Run #3 Results
+
+Training completed 102 steps.
+
+Training summary:
+
+- train conversations: 270
+- eval conversations: 30
+- train loss: 0.242
+- final `trainer.evaluate()` loss: 2.4044
+
+The loss signal should be treated as a weak training diagnostic for this project.
+NeuroBait is primarily evaluated through generated behavior against the base
+model.
+
+Generation eval summary over 8 held-out/novel prompts:
+
+- base persona average: 3.625 / 4
+- fine-tuned persona average: 4.0 / 4
+- base average words: 72.0
+- fine-tuned average words: 56.625
+- base label leaks: 1
+- fine-tuned label leaks: 0
+- base action-cue responses: 3
+- fine-tuned action-cue responses: 5
+
+Qualitatively, the fine-tuned adapter produced shorter, more conversational
+responses, maintained English on English prompts more reliably than the base
+model in this sample, and avoided literal structure labels in the tested turns.
+
+## Loading
+
+Example adapter loading path:
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+
+base_model = "unsloth/gemma-4-26b-a4b-it"
+adapter_id = "build-small-hackathon/NeuroBait"
+
+tokenizer = AutoTokenizer.from_pretrained(adapter_id)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    quantization_config=BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_compute_dtype=torch.bfloat16,
+        bnb_4bit_quant_type="nf4",
+    ),
+    device_map="auto",
+)
+model = PeftModel.from_pretrained(model, adapter_id)
+model.eval()
+```
+
+## Limitations
+
+- The model is not a medical or crisis-support system.
+- It may ask for a motivator or deadline when the user has already implied one.
+- It may still be too brief or too playful for some users.
+- Long-term personalization should be handled by product logic, not only by the
+  model.
+- App-initiated reminders should be handled by the app layer, not generated
+  spontaneously by the model.
+
+## Submission Context

-Add final run #3 metrics and qualitative examples here after Modal eval.
+This model is part of the Build Small Model Hackathon submission for NeuroBait.
+The public GitHub source repo tracks training, evaluation, deployment scaffolding,
+and agent development notes.
diff --git a/train/modal_train.py b/train/modal_train.py
index 90533cd..502aa3e 100644
--- a/train/modal_train.py
+++ b/train/modal_train.py
@@ -6,8 +6,9 @@ import modal

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

 app = modal.App(APP_NAME)

@@ -27,6 +28,8 @@ image = (
     .add_local_file("train/train_core.py", "/root/neurobait/train_core.py")
     .add_local_file("data/train.jsonl", "/data/train.jsonl")
     .add_local_file("data/eval.jsonl", "/data/eval.jsonl")
+    .add_local_file("model-card/README.md", "/root/neurobait/model-card/README.md")
+    .add_local_dir("deploy", "/root/neurobait/deploy")
 )

 hf_cache = modal.Volume.from_name("hf-cache", create_if_missing=True)
@@ -119,8 +122,13 @@ def train() -> dict:
     secrets=[modal.Secret.from_name("huggingface")],
 )
 def push_lora_to_hub(repo_id: str = DEFAULT_LORA_REPO) -> str:
+    import shutil
+
     from huggingface_hub import HfApi

+    api = HfApi()
+    api.create_repo(repo_id=repo_id, repo_type="model", private=False, exist_ok=True)
+    shutil.copyfile("/root/neurobait/model-card/README.md", "/out/neurobait-lora-run3/README.md")
     HfApi().upload_folder(
         folder_path="/out/neurobait-lora-run3",
         repo_id=repo_id,
@@ -129,6 +137,30 @@ def push_lora_to_hub(repo_id: str = DEFAULT_LORA_REPO) -> str:
     return repo_id

+@app.function(
+    image=image,
+    timeout=30 * 60,
+    secrets=[modal.Secret.from_name("huggingface")],
+)
+def push_space_to_hub(repo_id: str = DEFAULT_SPACE_REPO) -> str:
+    from huggingface_hub import HfApi
+
+    api = HfApi()
+    api.create_repo(
+        repo_id=repo_id,
+        repo_type="space",
+        space_sdk="gradio",
+        private=False,
+        exist_ok=True,
+    )
+    api.upload_folder(
+        folder_path="/root/neurobait/deploy",
+        repo_id=repo_id,
+        repo_type="space",
+    )
+    return repo_id
+
+
 @app.function(
     image=image,
     gpu="H100",
@@ -169,8 +201,10 @@ def main(
     run_preflight: bool = False,
     run_train: bool = True,
     push_lora: bool = False,
+    push_space: bool = False,
     merge: bool = False,
     lora_repo: str = DEFAULT_LORA_REPO,
+    space_repo: str = DEFAULT_SPACE_REPO,
     merged_repo: str = DEFAULT_MERGED_REPO,
 ) -> None:
     if run_preflight:
@@ -180,5 +214,7 @@ def main(
         print("train:", summary)
     if push_lora:
         print("lora repo:", push_lora_to_hub.remote(lora_repo))
+    if push_space:
+        print("space repo:", push_space_to_hub.remote(space_repo))
     if merge:
         print("merged repo:", merge_and_push.remote(merged_repo))


Validasi lokal sudah lolos: `py_compile` untuk app/train/eval/scripts dan dataset validator tetap 270 train / 30 eval tanpa error. Saya akan publish adapter repo dulu ke HF model repo pendek `build-small-hackathon/NeuroBait`.


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

Allow Modal to upload the trained LoRA adapter from the Modal Volume to the public Hugging Face model repo?


Output:
Chunk ID: 6bcc9f
Wall time: 17.2587 seconds
Process exited with code 0
Original token count: 2327
Output:
✓ Initialized. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-mjjKPIiyQjEE7iRq6wmB0O
✓ Created objects.
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/train/modal_train.
│   py
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/deploy
├── 🔨 Created mount data/eval.jsonl
├── 🔨 Created mount data/train.jsonl
├── 🔨 Created mount train/train_core.py
├── 🔨 Created mount model-card/README.md
├── 🔨 Created function preflight.
├── 🔨 Created function train.
├── 🔨 Created function push_lora_to_hub.
├── 🔨 Created function push_space_to_hub.
└── 🔨 Created function merge_and_push.

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lora repo: build-small-hackathon/NeuroBait
Stopping app - local entrypoint completed.
✓ App completed. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-mjjKPIiyQjEE7iRq6wmB0O


Adapter LoRA sudah ter-upload ke model repo `build-small-hackathon/NeuroBait`. Saya lanjut push source Space Gradio ke slug yang sama di namespace Space: `https://huggingface.co/spaces/build-small-hackathon/NeuroBait`.


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

Allow Modal to create/update the public Hugging Face Gradio Space for NeuroBait?


Output:
Chunk ID: aa816d
Wall time: 9.7538 seconds
Process exited with code 0
Original token count: 241
Output:
✓ Initialized. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-JS6cfuI3jxYiRu1V0CsxAy
✓ Created objects.
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/train/modal_train.
│   py
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/deploy
├── 🔨 Created mount model-card/README.md
├── 🔨 Created mount data/train.jsonl
├── 🔨 Created mount data/eval.jsonl
├── 🔨 Created mount train/train_core.py
├── 🔨 Created function preflight.
├── 🔨 Created function train.
├── 🔨 Created function push_lora_to_hub.
├── 🔨 Created function push_space_to_hub.
└── 🔨 Created function merge_and_push.
space repo: build-small-hackathon/NeuroBait
Stopping app - local entrypoint completed.
✓ App completed. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-JS6cfuI3jxYiRu1V0CsxAy


Question: site:huggingface.co/build-small-hackathon/NeuroBait NeuroBait

Question: site:huggingface.co/build-small-hackathon/NeuroBait NeuroBait


Question: site:huggingface.co/build-small-hackathon/NeuroBait NeuroBait


Publish Space berhasil dari sisi Modal. Saya akan verifikasi publik via HF API tanpa token supaya jelas model/Space benar-benar terlihat dari luar.


Output:
Chunk ID: 8c3737
Wall time: 0.2490 seconds
Process exited with code 0
Original token count: 1033
Output:
{"_id":"6a26dd7d34789e0d440655f8","id":"build-small-hackathon/NeuroBait","private":false,"pipeline_tag":"text-generation","library_name":"peft","tags":["peft","safetensors","lora","gemma-4","adhd","neurodivergent","task-initiation","build-small-hackathon","text-generation","conversational","license:other","region:us"],"downloads":0,"likes":0,"modelId":"build-small-hackathon/NeuroBait","author":"build-small-hackathon","sha":"7ad340aac705ed3c6f3d3e7c9807b1b518793fdb","lastModified":"2026-06-08T15:19:33.000Z","gated":false,"disabled":false,"widgetData":[{"text":"Hi, what can you help me with?"},{"text":"What is 84 * 3 / 2?"},{"text":"Tell me an interesting fact about the universe!"},{"text":"Explain quantum computing in simple terms."}],"model-index":null,"config":{"tokenizer_config":{"bos_token":"<bos>","eos_token":"<turn|>","mask_token":"<mask>","pad_token":"<pad>","unk_token":"<unk>"},"chat_template_jinja":"{{ bos_token }}{%- macro strip_thinking(text) -%}\n    {%- set ns = namespace(result='') -%}\n    {%- for part in text.split('<channel|>') -%}\n        {%- if '<|channel>' in part -%}\n            {%- set ns.result = ns.result + part.split('<|channel>')[0] -%}\n        {%- else -%}\n            {%- set ns.result = ns.result + part -%}\n        {%- endif -%}\n    {%- endfor -%}\n    {{- ns.result | trim -}}\n{%- endmacro -%}\n{%- set thinking = enable_thinking is defined and enable_thinking -%}\n{%- set loop_messages = messages -%}\n{%- if messages[0]['role'] in ['system', 'developer'] or thinking -%}\n    {{ '<|turn>system\n' }}\n    {%- if thinking -%}\n        {{ '<|think|>\n' }}\n    {%- endif -%}\n    {%- if messages[0]['role'] in ['system', 'developer'] -%}\n        {{ messages[0]['content'] | trim }}\n        {%- set loop_messages = messages[1:] -%}\n    {%- endif -%}\n    {{ '<turn|>\n' }}\n{%- endif -%}\n{%- for message in loop_messages -%}\n    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}\n        {{ raise_exception(\"Conversation roles must alternate user/assistant/user/assistant/...\") }}\n    {%- endif -%}\n    {%- if (message['role'] == 'assistant') -%}\n        {%- set role = \"model\" -%}\n    {%- else -%}\n        {%- set role = message['role'] -%}\n    {%- endif -%}\n    {{ '<|turn>' + role + '\n' }}\n    {%- if message['content'] is string -%}\n        {%- if role == \"model\" -%}\n            {{ strip_thinking(message['content']) }}\n        {%- else -%}\n            {{ message['content'] | trim }}\n        {%- endif -%}\n    {%- elif message['content'] is iterable -%}\n        {%- for item in message['content'] -%}\n            {%- if item['type'] == 'audio' -%}\n                {{ '<|audio|>' }}\n            {%- elif item['type'] == 'image' -%}\n                {{ '<|image|>' }}\n            {%- elif item['type'] == 'video' -%}\n                {{ '<|video|>' }}\n            {%- elif item['type'] == 'text' -%}\n                {%- if role == \"model\" -%}\n                    {{ strip_thinking(item['text']) }}\n                {%- else -%}\n                    {{ item['text'] | trim }}\n                {%- endif -%}\n            {%- endif -%}\n        {%- endfor -%}\n    {%- else -%}\n        {{ raise_exception(\"Invalid content type\") }}\n    {%- endif -%}\n    {{ '<turn|>\n' }}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n    {{'<|turn>model\n'}}\n{%- endif -%}\n","peft":{"base_model_name_or_path":"unsloth/gemma-4-26b-a4b-it","task_type":"CAUSAL_LM"}},"cardData":{"base_model":"unsloth/gemma-4-26b-a4b-it","library_name":"peft","pipeline_tag":"text-generation","tags":["lora","peft","gemma-4","adhd","neurodivergent","task-initiation","build-small-hackathon"],"license":"other"},"siblings":[{"rfilename":".gitattributes"},{"rfilename":"README.md"},{"rfilename":"adapter_config.json"},{"rfilename":"adapter_model.safetensors"},{"rfilename":"chat_template.jinja"},{"rfilename":"processor_config.json"},{"rfilename":"tokenizer.json"},{"rfilename":"tokenizer_config.json"},{"rfilename":"train_summary.json"}],"spaces":["build-small-hackathon/NeuroBait"],"createdAt":"2026-06-08T15:19:25.000Z","usedStorage":1043170482}


Output:
Chunk ID: 82a378
Wall time: 0.1990 seconds
Process exited with code 0
Original token count: 289
Output:
{"_id":"6a26dd980eccd859eba85b85","id":"build-small-hackathon/NeuroBait","sdk":"gradio","likes":0,"tags":["gradio","region:us"],"private":false,"author":"build-small-hackathon","sha":"f69025987bc8751b6de43a66a3fb756f31c6a517","lastModified":"2026-06-08T15:19:53.000Z","cardData":{"title":"NeuroBait","colorFrom":"green","colorTo":"blue","sdk":"gradio","app_file":"app.py","pinned":false,"license":"apache-2.0","short_description":"ADHD-friendly task initiation with one tiny next move."},"subdomain":"build-small-hackathon-neurobait","gated":false,"disabled":false,"host":"https://build-small-hackathon-neurobait.hf.space","models":["build-small-hackathon/NeuroBait","unsloth/gemma-4-26B-A4B-it"],"runtime":{"stage":"BUILDING","hardware":{"current":null,"requested":"cpu-basic"},"gcTimeout":172800,"replicas":{"requested":1},"devMode":false,"domains":[{"domain":"build-small-hackathon-neurobait.hf.space","stage":"READY"}]},"siblings":[{"rfilename":".gitattributes"},{"rfilename":"README.md"},{"rfilename":"__pycache__/app.cpython-310.pyc"},{"rfilename":"app.py"},{"rfilename":"requirements.txt"}],"createdAt":"2026-06-08T15:19:52.000Z","usedStorage":0}


Verifikasi publik sukses: model dan Space sudah visible. Ada satu hygiene issue: karena saya menjalankan `py_compile`, folder `deploy/__pycache__` ikut ter-upload ke Space. Saya akan bersihkan local dan update publish function supaya pycache/pyc tidak ikut upload lagi, lalu re-push Space.


Allow removing the generated deploy/__pycache__ directory that was created by py_compile and should not be committed or uploaded?


Output:
Chunk ID: b545e0
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:


Approved command prefix saved:
- ["rm", "-rf", "deploy/__pycache__"]


Saya akan patch upload Space agar repo HF ikut menghapus file generated yang telanjur ada. Ini menjaga Space repo tetap bersih.


Input:
*** Begin Patch
*** Update File: train/modal_train.py
@@
     api.upload_folder(
         folder_path="/root/neurobait/deploy",
         repo_id=repo_id,
         repo_type="space",
+        ignore_patterns=["__pycache__/*", "*.pyc"],
+        delete_patterns=["__pycache__/*", "*.pyc"],
     )
*** End Patch


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

@@ -159,2 +159,4 @@
         repo_type="space",
+        ignore_patterns=["__pycache__/*", "*.pyc"],
+        delete_patterns=["__pycache__/*", "*.pyc"],
     )


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


Patch cleanup sudah masuk. Saya re-push Space sekali lagi agar file pycache di Hub terhapus juga.


Allow re-uploading the Space after adding ignore/delete rules for generated pycache files?


Output:
Chunk ID: 8688dd
Wall time: 10.0500 seconds
Process exited with code 0
Original token count: 241
Output:
✓ Initialized. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-WvEWWo9BqN7d4tIGqZ8nnR
✓ Created objects.
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/train/modal_train.
│   py
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/deploy
├── 🔨 Created mount model-card/README.md
├── 🔨 Created mount data/eval.jsonl
├── 🔨 Created mount data/train.jsonl
├── 🔨 Created mount train/train_core.py
├── 🔨 Created function train.
├── 🔨 Created function push_lora_to_hub.
├── 🔨 Created function push_space_to_hub.
├── 🔨 Created function preflight.
└── 🔨 Created function merge_and_push.
space repo: build-small-hackathon/NeuroBait
Stopping app - local entrypoint completed.
✓ App completed. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-WvEWWo9BqN7d4tIGqZ8nnR


Output:
Chunk ID: e4dc7a
Wall time: 0.2275 seconds
Process exited with code 0
Original token count: 277
Output:
{"_id":"6a26dd980eccd859eba85b85","id":"build-small-hackathon/NeuroBait","sdk":"gradio","likes":0,"tags":["gradio","region:us"],"private":false,"author":"build-small-hackathon","sha":"00f16db34b09a09a8e8787c569378f664cce30ef","lastModified":"2026-06-08T15:20:53.000Z","cardData":{"title":"NeuroBait","colorFrom":"green","colorTo":"blue","sdk":"gradio","app_file":"app.py","pinned":false,"license":"apache-2.0","short_description":"ADHD-friendly task initiation with one tiny next move."},"subdomain":"build-small-hackathon-neurobait","gated":false,"disabled":false,"host":"https://build-small-hackathon-neurobait.hf.space","models":["build-small-hackathon/NeuroBait","unsloth/gemma-4-26B-A4B-it"],"runtime":{"stage":"BUILDING","hardware":{"current":null,"requested":"cpu-basic"},"gcTimeout":172800,"replicas":{"requested":1},"devMode":false,"domains":[{"domain":"build-small-hackathon-neurobait.hf.space","stage":"READY"}]},"siblings":[{"rfilename":".gitattributes"},{"rfilename":"README.md"},{"rfilename":"app.py"},{"rfilename":"requirements.txt"}],"createdAt":"2026-06-08T15:19:52.000Z","usedStorage":0}


Question: Hugging Face HfApi request_space_hardware hardware flavors ZeroGPU official docs

Question: Hugging Face HfApi request_space_hardware hardware flavors ZeroGPU official docs

huggingface_hub request_space_hardware SpaceHardware enum


Question: Hugging Face HfApi request_space_hardware hardware flavors ZeroGPU official docs

huggingface_hub request_space_hardware SpaceHardware enum


HF runtime masih `cpu-basic`, jadi app repo sudah ada tapi belum layak inferensi. Saya akan request ZeroGPU dulu, bukan paid GPU, karena docs resmi menyebut ZeroGPU untuk Gradio Spaces dengan opsi `large` 48GB dan `xlarge` 96GB; untuk model 26B 4-bit, `xlarge` adalah uji paling masuk akal.


Input:
*** Begin Patch
*** Update File: deploy/app.py
@@
-@spaces.GPU(duration=120)
+@spaces.GPU(duration=300, size="xlarge")
 def respond(message: str, history: list[dict], temperature: float, top_p: float) -> str:
*** End Patch


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

@@ -96,3 +96,3 @@

-@spaces.GPU(duration=120)
+@spaces.GPU(duration=300, size="xlarge")
 def respond(message: str, history: list[dict], temperature: float, top_p: float) -> str:


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


Input:
*** Begin Patch
*** Update File: deploy/README.md
@@
-The default app path uses 4-bit loading for the base model plus the NeuroBait
-LoRA adapter. The Space still needs a large GPU tier or ZeroGPU allocation that
-can load a 26B MoE model.
+The default app path uses 4-bit loading for the base model plus the NeuroBait
+LoRA adapter. The `@spaces.GPU` call requests ZeroGPU `xlarge` for the generation
+function because the model is a 26B MoE adapter stack.
*** End Patch


@@ -31,4 +31,4 @@
 The default app path uses 4-bit loading for the base model plus the NeuroBait
-LoRA adapter. The Space still needs a large GPU tier or ZeroGPU allocation that
-can load a 26B MoE model.
+LoRA adapter. The `@spaces.GPU` call requests ZeroGPU `xlarge` for the generation
+function because the model is a 26B MoE adapter stack.


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


Saya tambahkan entrypoint Modal untuk mengubah hardware Space dan membaca runtime-nya. Dengan ini kita bisa cek langsung apakah org hackathon mengizinkan ZeroGPU untuk Space ini.


Input:
*** Begin Patch
*** Update File: train/modal_train.py
@@
 def push_space_to_hub(repo_id: str = DEFAULT_SPACE_REPO) -> str:
@@
     return repo_id

+@app.function(
+    image=image,
+    timeout=10 * 60,
+    secrets=[modal.Secret.from_name("huggingface")],
+)
+def set_space_hardware(repo_id: str = DEFAULT_SPACE_REPO, hardware: str = "zero-a10g") -> str:
+    import json
+
+    from huggingface_hub import HfApi
+
+    api = HfApi()
+    runtime = api.request_space_hardware(repo_id=repo_id, hardware=hardware)
+    return json.dumps(
+        {
+            "repo_id": repo_id,
+            "requested": hardware,
+            "stage": getattr(runtime, "stage", None),
+            "hardware": getattr(runtime, "hardware", None),
+            "requested_hardware": getattr(runtime, "requested_hardware", None),
+            "raw": getattr(runtime, "raw", None),
+        },
+        indent=2,
+        default=str,
+    )
+
+
+@app.function(
+    image=image,
+    timeout=10 * 60,
+    secrets=[modal.Secret.from_name("huggingface")],
+)
+def get_space_runtime(repo_id: str = DEFAULT_SPACE_REPO) -> str:
+    import json
+
+    from huggingface_hub import HfApi
+
+    runtime = HfApi().get_space_runtime(repo_id=repo_id)
+    return json.dumps(
+        {
+            "repo_id": repo_id,
+            "stage": getattr(runtime, "stage", None),
+            "hardware": getattr(runtime, "hardware", None),
+            "requested_hardware": getattr(runtime, "requested_hardware", None),
+            "sleep_time": getattr(runtime, "sleep_time", None),
+            "raw": getattr(runtime, "raw", None),
+        },
+        indent=2,
+        default=str,
+    )
+
+
 @app.function(
     image=image,
     gpu="H100",
@@
     push_lora: bool = False,
     push_space: bool = False,
+    check_space: bool = False,
+    set_hardware: bool = False,
+    hardware: str = "zero-a10g",
     merge: bool = False,
@@
     if push_space:
         print("space repo:", push_space_to_hub.remote(space_repo))
+    if set_hardware:
+        print("space hardware:", set_space_hardware.remote(space_repo, hardware))
+    if check_space:
+        print("space runtime:", get_space_runtime.remote(space_repo))
     if merge:
         print("merged repo:", merge_and_push.remote(merged_repo))
*** End Patch


@@ -167,2 +167,53 @@
     image=image,
+    timeout=10 * 60,
+    secrets=[modal.Secret.from_name("huggingface")],
+)
+def set_space_hardware(repo_id: str = DEFAULT_SPACE_REPO, hardware: str = "zero-a10g") -> str:
+    import json
+
+    from huggingface_hub import HfApi
+
+    api = HfApi()
+    runtime = api.request_space_hardware(repo_id=repo_id, hardware=hardware)
+    return json.dumps(
+        {
+            "repo_id": repo_id,
+            "requested": hardware,
+            "stage": getattr(runtime, "stage", None),
+            "hardware": getattr(runtime, "hardware", None),
+            "requested_hardware": getattr(runtime, "requested_hardware", None),
+            "raw": getattr(runtime, "raw", None),
+        },
+        indent=2,
+        default=str,
+    )
+
+
+@app.function(
+    image=image,
+    timeout=10 * 60,
+    secrets=[modal.Secret.from_name("huggingface")],
+)
+def get_space_runtime(repo_id: str = DEFAULT_SPACE_REPO) -> str:
+    import json
+
+    from huggingface_hub import HfApi
+
+    runtime = HfApi().get_space_runtime(repo_id=repo_id)
+    return json.dumps(
+        {
+            "repo_id": repo_id,
+            "stage": getattr(runtime, "stage", None),
+            "hardware": getattr(runtime, "hardware", None),
+            "requested_hardware": getattr(runtime, "requested_hardware", None),
+            "sleep_time": getattr(runtime, "sleep_time", None),
+            "raw": getattr(runtime, "raw", None),
+        },
+        indent=2,
+        default=str,
+    )
+
+
+@app.function(
+    image=image,
     gpu="H100",
@@ -206,2 +257,5 @@
     push_space: bool = False,
+    check_space: bool = False,
+    set_hardware: bool = False,
+    hardware: str = "zero-a10g",
     merge: bool = False,
@@ -220,2 +274,6 @@
         print("space repo:", push_space_to_hub.remote(space_repo))
+    if set_hardware:
+        print("space hardware:", set_space_hardware.remote(space_repo, hardware))
+    if check_space:
+        print("space runtime:", get_space_runtime.remote(space_repo))
     if merge:


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


Saya re-push Space setelah update `@spaces.GPU(size="xlarge")`, lalu request hardware ZeroGPU. Kalau request ditolak oleh org/plan, kita akan tahu dari response runtime, bukan asumsi.


/media/haris-subrata/Work/llm/agent_analytics/venv/bin/python -m modal run train/modal_train.py --no-run-train --push-space --set-hardware --check-space

Allow Modal to update the Space app and request ZeroGPU hardware for a direct feasibility check?


Output:
Chunk ID: a6ded3
Wall time: 19.3998 seconds
Process exited with code 0
Original token count: 522
Output:
✓ Initialized. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-H7yvIdnue9TxyHNsPmoVaC
✓ Created objects.
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/train/modal_train.
│   py
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/deploy
├── 🔨 Created mount data/eval.jsonl
├── 🔨 Created mount data/train.jsonl
├── 🔨 Created mount model-card/README.md
├── 🔨 Created mount train/train_core.py
├── 🔨 Created function preflight.
├── 🔨 Created function train.
├── 🔨 Created function push_lora_to_hub.
├── 🔨 Created function push_space_to_hub.
├── 🔨 Created function set_space_hardware.
├── 🔨 Created function get_space_runtime.
└── 🔨 Created function merge_and_push.
space repo: build-small-hackathon/NeuroBait
space hardware: {
  "repo_id": "build-small-hackathon/NeuroBait",
  "requested": "zero-a10g",
  "stage": "BUILDING",
  "hardware": null,
  "requested_hardware": "cpu-basic",
  "raw": {
    "stage": "BUILDING",
    "hardware": {
      "current": null,
      "requested": "cpu-basic"
    },
    "gcTimeout": 172800,
    "replicas": {
      "requested": 1
    },
    "devMode": false,
    "domains": [
      {
        "domain": "build-small-hackathon-neurobait.hf.space",
        "stage": "READY"
      }
    ]
  }
}
space runtime: {
  "repo_id": "build-small-hackathon/NeuroBait",
  "stage": "BUILDING",
  "hardware": null,
  "requested_hardware": "zero-a10g",
  "sleep_time": 172800,
  "raw": {
    "stage": "BUILDING",
    "hardware": {
      "current": null,
      "requested": "zero-a10g"
    },
    "gcTimeout": 172800,
    "replicas": {
      "requested": 1
    },
    "devMode": false,
    "domains": [
      {
        "domain": "build-small-hackathon-neurobait.hf.space",
        "stage": "READY"
      }
    ]
  }
}
Stopping app - local entrypoint completed.
✓ App completed. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-H7yvIdnue9TxyHNsPmoVaC


ZeroGPU request diterima: runtime requested sekarang `zero-a10g`, sedang `BUILDING`. Saya akan tunggu sebentar lalu cek ulang status build dari HF API.


Output:
Chunk ID: e84a02
Wall time: 0.3247 seconds
Process exited with code 0
Original token count: 277
Output:
{"_id":"6a26dd980eccd859eba85b85","id":"build-small-hackathon/NeuroBait","sdk":"gradio","likes":0,"tags":["gradio","region:us"],"private":false,"author":"build-small-hackathon","sha":"19f1e91add07a7a2e560dba3f9034b16ad86f5e6","lastModified":"2026-06-08T15:22:15.000Z","cardData":{"title":"NeuroBait","colorFrom":"green","colorTo":"blue","sdk":"gradio","app_file":"app.py","pinned":false,"license":"apache-2.0","short_description":"ADHD-friendly task initiation with one tiny next move."},"subdomain":"build-small-hackathon-neurobait","gated":false,"disabled":false,"host":"https://build-small-hackathon-neurobait.hf.space","models":["build-small-hackathon/NeuroBait","unsloth/gemma-4-26B-A4B-it"],"runtime":{"stage":"BUILDING","hardware":{"current":null,"requested":"zero-a10g"},"gcTimeout":172800,"replicas":{"requested":1},"devMode":false,"domains":[{"domain":"build-small-hackathon-neurobait.hf.space","stage":"READY"}]},"siblings":[{"rfilename":".gitattributes"},{"rfilename":"README.md"},{"rfilename":"app.py"},{"rfilename":"requirements.txt"}],"createdAt":"2026-06-08T15:19:52.000Z","usedStorage":0}


Status masih `BUILDING` dan hardware requested tetap `zero-a10g`. Saya akan cek lagi setelah build punya waktu selesai; kalau masuk RUNNING baru kita uji request inference ringan.


Output:
Chunk ID: 19c5f7
Wall time: 30.0005 seconds
Process running with session ID 31291
Original token count: 0
Output:


Build masih berjalan, saya sedang memberi jeda agar HF menyelesaikan install dependencies. Ini normal untuk Space yang memasang torch/transformers.


Output:
Chunk ID: 321365
Wall time: 8.8339 seconds
Process exited with code 0
Original token count: 0
Output:


Output:
Chunk ID: 26a789
Wall time: 0.2039 seconds
Process exited with code 0
Original token count: 277
Output:
{"_id":"6a26dd980eccd859eba85b85","id":"build-small-hackathon/NeuroBait","sdk":"gradio","likes":0,"tags":["gradio","region:us"],"private":false,"author":"build-small-hackathon","sha":"19f1e91add07a7a2e560dba3f9034b16ad86f5e6","lastModified":"2026-06-08T15:22:15.000Z","cardData":{"title":"NeuroBait","colorFrom":"green","colorTo":"blue","sdk":"gradio","app_file":"app.py","pinned":false,"license":"apache-2.0","short_description":"ADHD-friendly task initiation with one tiny next move."},"subdomain":"build-small-hackathon-neurobait","gated":false,"disabled":false,"host":"https://build-small-hackathon-neurobait.hf.space","models":["build-small-hackathon/NeuroBait","unsloth/gemma-4-26B-A4B-it"],"runtime":{"stage":"BUILDING","hardware":{"current":null,"requested":"zero-a10g"},"gcTimeout":172800,"replicas":{"requested":1},"devMode":false,"domains":[{"domain":"build-small-hackathon-neurobait.hf.space","stage":"READY"}]},"siblings":[{"rfilename":".gitattributes"},{"rfilename":"README.md"},{"rfilename":"app.py"},{"rfilename":"requirements.txt"}],"createdAt":"2026-06-08T15:19:52.000Z","usedStorage":0}


Masih `BUILDING`. Saya akan coba ambil runtime/logs lewat API yang terautentikasi dari Modal; kalau ada dependency error, kita tidak perlu menebak dari halaman web.


Input:
*** Begin Patch
*** Update File: train/modal_train.py
@@
 def get_space_runtime(repo_id: str = DEFAULT_SPACE_REPO) -> str:
@@
     )

+@app.function(
+    image=image,
+    timeout=10 * 60,
+    secrets=[modal.Secret.from_name("huggingface")],
+)
+def get_space_logs(repo_id: str = DEFAULT_SPACE_REPO) -> str:
+    import os
+
+    import requests
+
+    token = os.environ["HF_TOKEN"]
+    response = requests.get(
+        f"https://huggingface.co/api/spaces/{repo_id}/logs",
+        headers={"Authorization": f"Bearer {token}"},
+        timeout=60,
+    )
+    return response.text[-12000:]
+
+
 @app.function(
@@
     check_space: bool = False,
+    check_logs: bool = False,
@@
     if check_space:
         print("space runtime:", get_space_runtime.remote(space_repo))
+    if check_logs:
+        print("space logs:", get_space_logs.remote(space_repo))
*** End Patch


@@ -218,2 +218,21 @@
     image=image,
+    timeout=10 * 60,
+    secrets=[modal.Secret.from_name("huggingface")],
+)
+def get_space_logs(repo_id: str = DEFAULT_SPACE_REPO) -> str:
+    import os
+
+    import requests
+
+    token = os.environ["HF_TOKEN"]
+    response = requests.get(
+        f"https://huggingface.co/api/spaces/{repo_id}/logs",
+        headers={"Authorization": f"Bearer {token}"},
+        timeout=60,
+    )
+    return response.text[-12000:]
+
+
+@app.function(
+    image=image,
     gpu="H100",
@@ -258,2 +277,3 @@
     check_space: bool = False,
+    check_logs: bool = False,
     set_hardware: bool = False,
@@ -278,2 +298,4 @@
         print("space runtime:", get_space_runtime.remote(space_repo))
+    if check_logs:
+        print("space logs:", get_space_logs.remote(space_repo))
     if merge:


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


Saya tambahkan log checker kecil di Modal yang memakai HF token dari secret. Sekarang saya jalankan hanya `--check-space --check-logs`, tidak upload ulang.


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

Allow Modal to read the authenticated Hugging Face Space runtime and logs for the current build?


Output:
Chunk ID: 0f4a3f
Wall time: 15.9232 seconds
Process exited with code 0
Original token count: 416
Output:
✓ Initialized. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-APtkemtkQJVCsaWYvIRPfz
✓ Created objects.
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/train/modal_train.
│   py
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/deploy
├── 🔨 Created mount model-card/README.md
├── 🔨 Created mount data/train.jsonl
├── 🔨 Created mount data/eval.jsonl
├── 🔨 Created mount train/train_core.py
├── 🔨 Created function preflight.
├── 🔨 Created function train.
├── 🔨 Created function push_lora_to_hub.
├── 🔨 Created function get_space_logs.
├── 🔨 Created function merge_and_push.
├── 🔨 Created function get_space_runtime.
├── 🔨 Created function push_space_to_hub.
└── 🔨 Created function set_space_hardware.
space runtime: {
  "repo_id": "build-small-hackathon/NeuroBait",
  "stage": "APP_STARTING",
  "hardware": null,
  "requested_hardware": "zero-a10g",
  "sleep_time": 172800,
  "raw": {
    "stage": "APP_STARTING",
    "hardware": {
      "current": null,
      "requested": "zero-a10g"
    },
    "gcTimeout": 172800,
    "replicas": {
      "requested": 1
    },
    "devMode": false,
    "domains": [
      {
        "domain": "build-small-hackathon-neurobait.hf.space",
        "stage": "READY"
      }
    ]
  }
}
space logs: {"error":"Sorry, we can't find the page you are looking for."}
Stopping app - local entrypoint completed.
✓ App completed. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-APtkemtkQJVCsaWYvIRPfz


Runtime sudah naik dari `BUILDING` ke `APP_STARTING`; endpoint logs yang saya coba tidak valid, tapi runtime API cukup menunjukkan progress. Saya akan tunggu sampai RUNNING lalu uji halaman/app endpoint.


Output:
Chunk ID: 8889a1
Wall time: 30.0018 seconds
Process running with session ID 42672
Original token count: 0
Output:


Masih menunggu startup Space. Bagian ini bisa lama karena container pertama kali membangun environment dan mulai app Gradio.


Output:
Chunk ID: b16a5a
Wall time: 26.1051 seconds
Process exited with code 0
Original token count: 0
Output:


Saya cek status lagi sekarang; kalau sudah RUNNING saya lanjut hit host Space.


curl -s -I https://build-small-hackathon-neurobait.hf.space


Output:
Chunk ID: ec9445
Wall time: 0.2572 seconds
Process exited with code 0
Original token count: 445
Output:
{"_id":"6a26dd980eccd859eba85b85","id":"build-small-hackathon/NeuroBait","sdk":"gradio","likes":0,"tags":["gradio","region:us"],"private":false,"author":"build-small-hackathon","sha":"19f1e91add07a7a2e560dba3f9034b16ad86f5e6","lastModified":"2026-06-08T15:22:15.000Z","cardData":{"title":"NeuroBait","colorFrom":"green","colorTo":"blue","sdk":"gradio","app_file":"app.py","pinned":false,"license":"apache-2.0","short_description":"ADHD-friendly task initiation with one tiny next move."},"subdomain":"build-small-hackathon-neurobait","gated":false,"disabled":false,"host":"https://build-small-hackathon-neurobait.hf.space","models":["build-small-hackathon/NeuroBait","unsloth/gemma-4-26B-A4B-it"],"runtime":{"stage":"RUNTIME_ERROR","hardware":{"current":null,"requested":"zero-a10g"},"gcTimeout":172800,"errorMessage":"Exit code: 1. Reason: /home/user/app/app.py:229: UserWarning: The parameters have been moved from the Blocks constructor to the launch() method in Gradio 6.0: theme, css. Please pass these parameters to launch() instead.\n  with gr.Blocks(css=CSS, theme=gr.themes.Soft(primary_hue=\"green\", neutral_hue=\"stone\")) as demo:\nTraceback (most recent call last):\n  File \"/home/user/app/app.py\", line 242, in <module>\n    chatbot = gr.Chatbot(\n  File \"/usr/local/lib/python3.10/site-packages/gradio/component_meta.py\", line 194, in wrapper\n    return fn(self, **kwargs)\nTypeError: Chatbot.__init__() got an unexpected keyword argument 'type'\n","replicas":{"requested":1},"devMode":false,"domains":[{"domain":"build-small-hackathon-neurobait.hf.space","stage":"READY"}]},"siblings":[{"rfilename":".gitattributes"},{"rfilename":"README.md"},{"rfilename":"app.py"},{"rfilename":"requirements.txt"}],"createdAt":"2026-06-08T15:19:52.000Z","usedStorage":0}


Output:
Chunk ID: 6e2f98
Wall time: 1.0007 seconds
Process running with session ID 99411
Original token count: 0
Output:


Output:
Chunk ID: 154d77
Wall time: 1.6126 seconds
Process exited with code 0
Original token count: 85
Output:
HTTP/2 503
date: Mon, 08 Jun 2026 15:25:16 GMT
content-type: text/html; charset=utf-8
content-length: 49
link: <https://huggingface.co/spaces/build-small-hackathon/NeuroBait>;rel="canonical"
x-request-id: zd7_1S
vary: origin, access-control-request-method, access-control-request-headers
access-control-allow-credentials: true


Space berhasil sampai runtime, tapi crash di API Gradio: versi Space tidak menerima `gr.Chatbot(type="messages")`. Saya akan ubah ke format Chatbot tuple klasik agar kompatibel lebih luas, lalu re-push dan cek ulang.


Input:
*** Begin Patch
*** Update File: deploy/app.py
@@
-def _history_to_messages(history: list[dict]) -> list[dict]:
+def _history_to_messages(history: list) -> list[dict]:
     messages = []
     for item in history:
-        role = item.get("role")
-        content = item.get("content")
-        if role in {"user", "assistant"} and isinstance(content, str) and content.strip():
-            messages.append({"role": role, "content": content.strip()})
+        if isinstance(item, dict):
+            role = item.get("role")
+            content = item.get("content")
+            if role in {"user", "assistant"} and isinstance(content, str) and content.strip():
+                messages.append({"role": role, "content": content.strip()})
+            continue
+        if isinstance(item, (tuple, list)) and len(item) == 2:
+            user_text, assistant_text = item
+            if isinstance(user_text, str) and user_text.strip():
+                messages.append({"role": "user", "content": user_text.strip()})
+            if isinstance(assistant_text, str) and assistant_text.strip():
+                messages.append({"role": "assistant", "content": assistant_text.strip()})
     return messages
@@
             chatbot = gr.Chatbot(
-                type="messages",
                 height=580,
                 show_copy_button=True,
                 elem_classes=["nb-chatbot"],
             )
@@
-    def user_turn(user_message: str, chat_history: list[dict]) -> tuple[str, list[dict]]:
+    def user_turn(user_message: str, chat_history: list) -> tuple[str, list]:
         if not user_message.strip():
             return "", chat_history
-        return "", chat_history + [{"role": "user", "content": user_message.strip()}]
+        return "", chat_history + [(user_message.strip(), None)]

-    def bot_turn(chat_history: list[dict], temperature_value: float, top_p_value: float) -> list[dict]:
-        user_message = chat_history[-1]["content"]
+    def bot_turn(chat_history: list, temperature_value: float, top_p_value: float) -> list:
+        user_message = chat_history[-1][0]
         assistant_message = respond(user_message, chat_history[:-1], temperature_value, top_p_value)
-        return chat_history + [{"role": "assistant", "content": assistant_message}]
+        chat_history[-1] = (user_message, assistant_message)
+        return chat_history
*** End Patch


@@ -80,9 +80,17 @@

-def _history_to_messages(history: list[dict]) -> list[dict]:
+def _history_to_messages(history: list) -> list[dict]:
     messages = []
     for item in history:
-        role = item.get("role")
-        content = item.get("content")
-        if role in {"user", "assistant"} and isinstance(content, str) and content.strip():
-            messages.append({"role": role, "content": content.strip()})
+        if isinstance(item, dict):
+            role = item.get("role")
+            content = item.get("content")
+            if role in {"user", "assistant"} and isinstance(content, str) and content.strip():
+                messages.append({"role": role, "content": content.strip()})
+            continue
+        if isinstance(item, (tuple, list)) and len(item) == 2:
+            user_text, assistant_text = item
+            if isinstance(user_text, str) and user_text.strip():
+                messages.append({"role": "user", "content": user_text.strip()})
+            if isinstance(assistant_text, str) and assistant_text.strip():
+                messages.append({"role": "assistant", "content": assistant_text.strip()})
     return messages
@@ -242,3 +250,2 @@
             chatbot = gr.Chatbot(
-                type="messages",
                 height=580,
@@ -277,11 +284,12 @@

-    def user_turn(user_message: str, chat_history: list[dict]) -> tuple[str, list[dict]]:
+    def user_turn(user_message: str, chat_history: list) -> tuple[str, list]:
         if not user_message.strip():
             return "", chat_history
-        return "", chat_history + [{"role": "user", "content": user_message.strip()}]
+        return "", chat_history + [(user_message.strip(), None)]

-    def bot_turn(chat_history: list[dict], temperature_value: float, top_p_value: float) -> list[dict]:
-        user_message = chat_history[-1]["content"]
+    def bot_turn(chat_history: list, temperature_value: float, top_p_value: float) -> list:
+        user_message = chat_history[-1][0]
         assistant_message = respond(user_message, chat_history[:-1], temperature_value, top_p_value)
-        return chat_history + [{"role": "assistant", "content": assistant_message}]
+        chat_history[-1] = (user_message, assistant_message)
+        return chat_history


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


Patch kompatibilitas Chatbot sudah masuk. Saya re-push Space dan cek runtime lagi.


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

Allow re-uploading the fixed Gradio Space and checking its runtime status?


Output:
Chunk ID: b75471
Wall time: 15.6522 seconds
Process exited with code 0
Original token count: 406
Output:
✓ Initialized. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-8PJ5Lb2GoXx27UnlqvJzfu
✓ Created objects.
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/train/modal_train.
│   py
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/deploy
├── 🔨 Created mount model-card/README.md
├── 🔨 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 push_space_to_hub.
├── 🔨 Created function set_space_hardware.
├── 🔨 Created function get_space_runtime.
├── 🔨 Created function get_space_logs.
└── 🔨 Created function merge_and_push.
space repo: build-small-hackathon/NeuroBait
space runtime: {
  "repo_id": "build-small-hackathon/NeuroBait",
  "stage": "BUILDING",
  "hardware": null,
  "requested_hardware": "zero-a10g",
  "sleep_time": 172800,
  "raw": {
    "stage": "BUILDING",
    "hardware": {
      "current": null,
      "requested": "zero-a10g"
    },
    "gcTimeout": 172800,
    "replicas": {
      "requested": 1
    },
    "devMode": false,
    "domains": [
      {
        "domain": "build-small-hackathon-neurobait.hf.space",
        "stage": "READY"
      }
    ]
  }
}
Stopping app - local entrypoint completed.
✓ App completed. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-8PJ5Lb2GoXx27UnlqvJzfu


Space kembali `BUILDING` karena commit baru. Saya tunggu sebentar lalu cek status; target minimum sekarang adalah app bisa `RUNNING` tanpa crash import/UI.


Output:
Chunk ID: eeca46
Wall time: 30.0012 seconds
Process running with session ID 18582
Original token count: 0
Output:


Masih menunggu rebuild HF. Saya lanjut poll setelah jeda selesai.


Output:
Chunk ID: d27519
Wall time: 26.1251 seconds
Process exited with code 0
Original token count: 0
Output:


Output:
Chunk ID: 0cc400
Wall time: 0.4900 seconds
Process exited with code 0
Original token count: 448
Output:
{"_id":"6a26dd980eccd859eba85b85","id":"build-small-hackathon/NeuroBait","sdk":"gradio","likes":0,"tags":["gradio","region:us"],"private":false,"author":"build-small-hackathon","sha":"aa680b725a187674ba1c2eaaf514ee55e9fedccf","lastModified":"2026-06-08T15:25:50.000Z","cardData":{"title":"NeuroBait","colorFrom":"green","colorTo":"blue","sdk":"gradio","app_file":"app.py","pinned":false,"license":"apache-2.0","short_description":"ADHD-friendly task initiation with one tiny next move."},"subdomain":"build-small-hackathon-neurobait","gated":false,"disabled":false,"host":"https://build-small-hackathon-neurobait.hf.space","models":["build-small-hackathon/NeuroBait","unsloth/gemma-4-26B-A4B-it"],"runtime":{"stage":"RUNTIME_ERROR","hardware":{"current":null,"requested":"zero-a10g"},"gcTimeout":172800,"errorMessage":"Exit code: 1. Reason: /home/user/app/app.py:237: UserWarning: The parameters have been moved from the Blocks constructor to the launch() method in Gradio 6.0: theme, css. Please pass these parameters to launch() instead.\n  with gr.Blocks(css=CSS, theme=gr.themes.Soft(primary_hue=\"green\", neutral_hue=\"stone\")) as demo:\nTraceback (most recent call last):\n  File \"/home/user/app/app.py\", line 250, in <module>\n    chatbot = gr.Chatbot(\n  File \"/usr/local/lib/python3.10/site-packages/gradio/component_meta.py\", line 194, in wrapper\n    return fn(self, **kwargs)\nTypeError: Chatbot.__init__() got an unexpected keyword argument 'show_copy_button'\n","replicas":{"requested":1},"devMode":false,"domains":[{"domain":"build-small-hackathon-neurobait.hf.space","stage":"READY"}]},"siblings":[{"rfilename":".gitattributes"},{"rfilename":"README.md"},{"rfilename":"app.py"},{"rfilename":"requirements.txt"}],"createdAt":"2026-06-08T15:19:52.000Z","usedStorage":0}


Crash kedua masih API Gradio UI, bukan model: `show_copy_button` juga tidak diterima. Saya akan simplify komponen Chatbot ke parameter paling konservatif.


Input:
*** Begin Patch
*** Update File: deploy/app.py
@@
             chatbot = gr.Chatbot(
                 height=580,
-                show_copy_button=True,
                 elem_classes=["nb-chatbot"],
             )
*** End Patch


@@ -251,3 +251,2 @@
                 height=580,
-                show_copy_button=True,
                 elem_classes=["nb-chatbot"],


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


Saya juga pindahkan `css/theme` ke `launch()` mengikuti warning Gradio 6, supaya tidak cuma “tidak crash” tapi custom UI tetap diterapkan.


Input:
*** Begin Patch
*** Update File: deploy/app.py
@@
-with gr.Blocks(css=CSS, theme=gr.themes.Soft(primary_hue="green", neutral_hue="stone")) as demo:
+with gr.Blocks() as demo:
@@
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(css=CSS, theme=gr.themes.Soft(primary_hue="green", neutral_hue="stone"))
*** End Patch


@@ -236,3 +236,3 @@

-with gr.Blocks(css=CSS, theme=gr.themes.Soft(primary_hue="green", neutral_hue="stone")) as demo:
+with gr.Blocks() as demo:
     gr.HTML(
@@ -308,2 +308,2 @@
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(css=CSS, theme=gr.themes.Soft(primary_hue="green", neutral_hue="stone"))


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


Saya re-push lagi dan cek runtime. Kalau error UI berikutnya masih muncul, kita akan keep trimming parameter Gradio sampai app RUNNING.


Allow re-uploading the simplified Gradio Space and checking runtime again?


Output:
Chunk ID: f44b39
Wall time: 24.7015 seconds
Process exited with code 0
Original token count: 406
Output:
✓ Initialized. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-P0W9cScZSOiKnGFBVeo2bl
✓ Created objects.
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/train/modal_train.
│   py
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/deploy
├── 🔨 Created mount model-card/README.md
├── 🔨 Created mount data/eval.jsonl
├── 🔨 Created mount data/train.jsonl
├── 🔨 Created mount train/train_core.py
├── 🔨 Created function preflight.
├── 🔨 Created function push_lora_to_hub.
├── 🔨 Created function push_space_to_hub.
├── 🔨 Created function set_space_hardware.
├── 🔨 Created function get_space_runtime.
├── 🔨 Created function get_space_logs.
├── 🔨 Created function merge_and_push.
└── 🔨 Created function train.
space repo: build-small-hackathon/NeuroBait
space runtime: {
  "repo_id": "build-small-hackathon/NeuroBait",
  "stage": "BUILDING",
  "hardware": null,
  "requested_hardware": "zero-a10g",
  "sleep_time": 172800,
  "raw": {
    "stage": "BUILDING",
    "hardware": {
      "current": null,
      "requested": "zero-a10g"
    },
    "gcTimeout": 172800,
    "replicas": {
      "requested": 1
    },
    "devMode": false,
    "domains": [
      {
        "domain": "build-small-hackathon-neurobait.hf.space",
        "stage": "READY"
      }
    ]
  }
}
Stopping app - local entrypoint completed.
✓ App completed. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-P0W9cScZSOiKnGFBVeo2bl


Rebuild berjalan lagi. Saya tunggu sampai status berubah dari BUILDING.


Output:
Chunk ID: 9e175a
Wall time: 30.0005 seconds
Process running with session ID 90467
Original token count: 0
Output:


Build HF belum selesai dalam 30 detik pertama; saya lanjutkan poll.


Output:
Chunk ID: da7ef5
Wall time: 24.2059 seconds
Process exited with code 0
Original token count: 0
Output:


Output:
Chunk ID: 00f0bf
Wall time: 0.2386 seconds
Process exited with code 0
Original token count: 336
Output:
{"_id":"6a26dd980eccd859eba85b85","id":"build-small-hackathon/NeuroBait","sdk":"gradio","likes":0,"tags":["gradio","region:us"],"private":false,"author":"build-small-hackathon","sha":"449014ed7debfb085d6f22fee3b39ca1bf9ea81b","lastModified":"2026-06-08T15:27:52.000Z","cardData":{"title":"NeuroBait","colorFrom":"green","colorTo":"blue","sdk":"gradio","app_file":"app.py","pinned":false,"license":"apache-2.0","short_description":"ADHD-friendly task initiation with one tiny next move."},"subdomain":"build-small-hackathon-neurobait","gated":false,"disabled":false,"host":"https://build-small-hackathon-neurobait.hf.space","models":["build-small-hackathon/NeuroBait","unsloth/gemma-4-26B-A4B-it"],"runtime":{"stage":"RUNTIME_ERROR","hardware":{"current":null,"requested":"zero-a10g"},"gcTimeout":172800,"errorMessage":"Exit code: 1. Reason: Traceback (most recent call last):\n  File \"/home/user/app/app.py\", line 264, in <module>\n    gr.Examples(\nTypeError: create_examples() got an unexpected keyword argument 'elem_classes'\n","replicas":{"requested":1},"devMode":false,"domains":[{"domain":"build-small-hackathon-neurobait.hf.space","stage":"READY"}]},"siblings":[{"rfilename":".gitattributes"},{"rfilename":"README.md"},{"rfilename":"app.py"},{"rfilename":"requirements.txt"}],"createdAt":"2026-06-08T15:19:52.000Z","usedStorage":0}


Output:
Chunk ID: d39441
Wall time: 0.9992 seconds
Process exited with code 0
Original token count: 85
Output:
HTTP/2 503
date: Mon, 08 Jun 2026 15:29:10 GMT
content-type: text/html; charset=utf-8
content-length: 49
link: <https://huggingface.co/spaces/build-small-hackathon/NeuroBait>;rel="canonical"
x-request-id: 1j_faG
vary: origin, access-control-request-method, access-control-request-headers
access-control-allow-credentials: true


Crash berikutnya masih parameter UI: `gr.Examples` tidak menerima `elem_classes`. Saya remove parameter itu; style contoh prompt bisa cukup dari global Gradio.


Input:
*** Begin Patch
*** Update File: deploy/app.py
@@
             gr.Examples(
                 examples=[
                     "I need to finish a presentation tomorrow, but I keep avoiding the first slide.",
                     "My inbox is so messy that I freeze every time I open it.",
                     "I feel stuck and I don't know where to start.",
                 ],
                 inputs=message,
-                elem_classes=["nb-examples"],
             )
*** End Patch


@@ -270,3 +270,2 @@
                 inputs=message,
-                elem_classes=["nb-examples"],
             )


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


Saya re-push lagi dan cek. Ini masih dalam lapisan UI startup, belum sampai model loading.


Allow re-uploading the Gradio Examples compatibility fix and checking runtime?


Output:
Chunk ID: 099a31
Wall time: 13.4272 seconds
Process exited with code 0
Original token count: 406
Output:
✓ Initialized. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-QDjn80yKdu4f29BCQ18Wu6
✓ Created objects.
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/train/modal_train.
│   py
├── 🔨 Created mount
│   /media/haris-subrata/Work/llm/NeuroBait-Build-Small-Model/deploy
├── 🔨 Created mount model-card/README.md
├── 🔨 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 push_space_to_hub.
├── 🔨 Created function set_space_hardware.
├── 🔨 Created function get_space_runtime.
├── 🔨 Created function get_space_logs.
└── 🔨 Created function merge_and_push.
space repo: build-small-hackathon/NeuroBait
space runtime: {
  "repo_id": "build-small-hackathon/NeuroBait",
  "stage": "BUILDING",
  "hardware": null,
  "requested_hardware": "zero-a10g",
  "sleep_time": 172800,
  "raw": {
    "stage": "BUILDING",
    "hardware": {
      "current": null,
      "requested": "zero-a10g"
    },
    "gcTimeout": 172800,
    "replicas": {
      "requested": 1
    },
    "devMode": false,
    "domains": [
      {
        "domain": "build-small-hackathon-neurobait.hf.space",
        "stage": "READY"
      }
    ]
  }
}
Stopping app - local entrypoint completed.
✓ App completed. View run at
https://modal.com/apps/harisabektidsubrata/main/ap-QDjn80yKdu4f29BCQ18Wu6


Build restart lagi. Saya beri jeda dan poll runtime.


Output:
Chunk ID: 553b18
Wall time: 30.0013 seconds
Process running with session ID 66529
Original token count: 0
Output:


Saya lanjut menunggu rebuild HF; sejauh ini semua error masih deterministik dari Gradio component kwargs.


Output:
Chunk ID: 6539ef
Wall time: 26.1822 seconds
Process exited with code 0
Original token count: 0
Output:


Output:
Chunk ID: 1a0ca4
Wall time: 0.2833 seconds
Process exited with code 0
Original token count: 300
Output:
{"_id":"6a26dd980eccd859eba85b85","id":"build-small-hackathon/NeuroBait","sdk":"gradio","likes":0,"tags":["gradio","region:us"],"private":false,"author":"build-small-hackathon","sha":"021c767d1e367ab3330b9d0ccc838ab515326ad7","lastModified":"2026-06-08T15:29:38.000Z","cardData":{"title":"NeuroBait","colorFrom":"green","colorTo":"blue","sdk":"gradio","app_file":"app.py","pinned":false,"license":"apache-2.0","short_description":"ADHD-friendly task initiation with one tiny next move."},"subdomain":"build-small-hackathon-neurobait","gated":false,"disabled":false,"host":"https://build-small-hackathon-neurobait.hf.space","models":["build-small-hackathon/NeuroBait","unsloth/gemma-4-26B-A4B-it"],"runtime":{"stage":"RUNNING","hardware":{"current":"zero-a10g","requested":"zero-a10g"},"gcTimeout":172800,"replicas":{"current":1,"requested":1},"devMode":false,"domains":[{"domain":"build-small-hackathon-neurobait.hf.space","stage":"READY"}],"sha":"021c767d1e367ab3330b9d0ccc838ab515326ad7","pySpacesVersion":"0.50.4"},"siblings":[{"rfilename":".gitattributes"},{"rfilename":"README.md"},{"rfilename":"app.py"},{"rfilename":"requirements.txt"}],"createdAt":"2026-06-08T15:19:52.000Z","usedStorage":0}


Space sekarang `RUNNING` di `zero-a10g`. Saya akan hit host Space untuk memastikan halaman Gradio reachable, lalu kalau memungkinkan uji inference ringan.


Output:
Chunk ID: 926772
Wall time: 1.0013 seconds
Process running with session ID 21498
Original token count: 0
Output:


Output:
Chunk ID: aae5bf
Wall time: 1.0014 seconds
Process running with session ID 17859
Original token count: 0
Output: