Category	Examples	Description
Household Chores	147	Cleaning, laundry, organization, maintenance
Shopping/Errands	90	Grocery shopping, returns, pickups, banking
Cooking/Meals	80	Meal prep, baking, special occasions
School Projects	103	Research papers, presentations, studying
Work Tasks	112	Reports, meetings, reviews, planning
Personal Projects	81	Learning, websites, home renovation
Health/Fitness	63	Exercise routines, meal prep, wellness
Events/Travel	68	Parties, weddings, vacations, moves
Maintenance	58	Car, home, appliance, tech repairs

Total: 804 examples (723 training, 81 evaluation)

Usage

Input Format

Decompose this task into subtasks: [YOUR TASK HERE]

Expected Output Format

The model outputs JSON with subtasks containing:

title: Brief, actionable subtask description
estimateMinutes: Conservative time estimate in minutes
order: Sequential order (1, 2, 3, ...)

json

[
  {"title": "Choose party date and time", "estimateMinutes": 15, "order": 1},
  {"title": "Create guest list", "estimateMinutes": 20, "order": 2},
  {"title": "Book venue or prepare home", "estimateMinutes": 45, "order": 3},
  {"title": "Order cake and plan menu", "estimateMinutes": 30, "order": 4},
  {"title": "Send invitations", "estimateMinutes": 20, "order": 5},
  {"title": "Shop for decorations", "estimateMinutes": 60, "order": 6}
]

With llama.cpp

bash

./llama-cli -m taskapp-gemma3-270m-Q8_0.gguf \
    -p "Decompose this task into subtasks: Clean the garage" \
    --temp 0.7 \
    --top-p 0.9

With llama_sdk (Flutter/Dart)

dart

final llama = Llama(LlamaController(
  modelPath: 'path/to/taskapp-gemma3-270m-Q8_0.gguf',
  nCtx: 2048,
  nBatch: 512,
  greedy: false,
));

final messages = [
  UserLlamaMessage('Decompose this task into subtasks: Plan birthday party'),
];

final stream = llama.prompt(messages);
await for (final response in stream) {
  print(response.text);
}

With Python (transformers)

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("johkamb/task-decomposer-270m")
tokenizer = AutoTokenizer.from_pretrained("johkamb/task-decomposer-270m")

prompt = "Decompose this task into subtasks: Organize garage"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))

Example Outputs

Example 1: Plan Birthday Party

json

[
  {"title": "Choose date and send save-the-dates", "estimateMinutes": 15, "order": 1},
  {"title": "Create guest list", "estimateMinutes": 20, "order": 2},
  {"title": "Book venue or prepare home", "estimateMinutes": 45, "order": 3},
  {"title": "Order cake and plan menu", "estimateMinutes": 30, "order": 4},
  {"title": "Send formal invitations", "estimateMinutes": 20, "order": 5},
  {"title": "Shop for decorations and party supplies", "estimateMinutes": 60, "order": 6}
]

Example 2: Write Research Paper

json

[
  {"title": "Choose topic and create thesis", "estimateMinutes": 45, "order": 1},
  {"title": "Research and gather sources", "estimateMinutes": 120, "order": 2},
  {"title": "Create detailed outline", "estimateMinutes": 30, "order": 3},
  {"title": "Write first draft", "estimateMinutes": 180, "order": 4},
  {"title": "Revise and edit", "estimateMinutes": 90, "order": 5},
  {"title": "Format citations and bibliography", "estimateMinutes": 45, "order": 6}
]

Example 3: Clean Garage

json

[
  {"title": "Remove everything from garage", "estimateMinutes": 60, "order": 1},
  {"title": "Sweep and clean floor", "estimateMinutes": 20, "order": 2},
  {"title": "Sort items into keep/donate/trash", "estimateMinutes": 45, "order": 3},
  {"title": "Organize items on shelves", "estimateMinutes": 40, "order": 4},
  {"title": "Dispose of unwanted items", "estimateMinutes": 30, "order": 5}
]

Training Details

Hyperparameters

Base Model: unsloth/gemma-3-270m-it
Training Examples: 804 (no eval split for full data utilization)
Epochs: 3
Batch Size: 4 per device
Gradient Accumulation: 1 (effective batch size = 4)
Learning Rate: 5e-5
LR Scheduler: Linear
Warmup Steps: 10
Optimizer: AdamW 8-bit
Weight Decay: 0.001
LoRA Rank (r): 128
LoRA Alpha: 128
LoRA Dropout: 0
Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Max Sequence Length: 2048 tokens
Precision: FP32 (Gemma3 requirement)
Gradient Checkpointing: unsloth (memory efficient)

Training Environment

GPU: NVIDIA T4 (Google Colab)
Training Time: ~20-30 minutes
Framework: Unsloth + transformers + TRL (SFTConfig)
Quantization: FP32 during training, Q8_0 for deployment

Performance Metrics

Final Training Loss: < 0.5 (excellent), < 1.0 (good)
Trainable Parameters: ~3-5% of total model parameters (r=128)
Model Size:
- Original: ~540MB (FP32)
- Quantized (Q8_0): ~270MB
Quality: Excellent (Q8_0 has minimal quality loss vs FP32)
Inference Speed: Fast on mobile devices (optimized by llama.cpp)

Limitations

Language: Optimized for English only
Task Scope: Best for tasks completable within days/weeks
Domain: Optimized for everyday tasks (household, work, school, personal)
Time Estimates: Conservative estimates (designed to under-promise)
Specialized Tasks: May struggle with highly technical or niche domains
JSON Output: Occasionally may need retry for valid JSON formatting
Context Length: Limited to 2048 tokens (~1500 words)

Ethical Considerations

Bias: Training data focuses on common Western/American tasks; may not reflect all cultures
Time Estimates: Conservative estimates may not fit all working styles
Task Decomposition: Suggested subtasks are general; individual needs may vary
Not for: Medical advice, legal guidance, or critical decision-making

Acknowledgments

Base Model: Google Gemma Team
Fine-tuning: Unsloth AI (optimized training)
Quantization: llama.cpp team (GGUF format)
Training Platform: Google Colab