arbyazra123/sumobot_ml

Sumobot AI Assistant Models

This repository contains three Jupyter notebooks for training and exporting AI models for Sumobot gameplay:

• train_ml.ipynb: A multi-class classification model (ML) using TensorFlow
• train_slm.ipynb: A sequence-level model (SLM) using a GPT-style transformer
• train_llm.ipynb: A large language model (LLM) using LoRA fine-tuning

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Get Started

Clone including the datasets

Note: This will fetch all dataset/*.csv sumobot logs

1. Run git lfs install
2. Run git clone https://huggingface.co/datasets/arbyazra123/sumobot_ml

Clone without datasets

Note: By running the .ipynb files, it will scan local dataset folder. If it's empty, will fetch from online

1. Run GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/arbyazra123/sumobot_ml

Running Online Notebooks

1. Upload ipynb file that you want to run, for example train_llm.ipynb
2. Upload dataset_helper.py to the notebook, for example in google colab, drag and drop dataset_helper.py on colab's Files
3. train_llm.ipynb requires to install trl. Therefore, install the package by running !pip install trl
4. train_ml.ipynb requires to install tensorflow. Therefore, install the package by running !pip install tensorflow
5. If online notebook still raising ModuleNotFound, upload requirements.txt in online notebooks, then run !pip install -r requirements.txt
6. It's done, you dont have to follow next steps.

Running the Offline Notebooks

Installing Requirements

Install all dependencies using pip with Command Prompt / Powershell (Windows) or Terminal (Macos/Linux):

pip install -r requirements.txt

This installs all required packages (PyTorch, TensorFlow, Hugging Face, ONNX, etc.) and works on Windows, macOS, and Linux.

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Prerequisites

• Jupyter Notebook installed
• Python 3.10+
• Dataset will be fetched from HugginFace

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1. Run train_ml.ipynb (Multi-Label Classification)

1. Open Jupyter:

   jupyter notebook
   

2. Navigate to train_ml.ipynb
3. Run all cells

This trains a Keras model and exports it as ml.onnx

Output: ml.onnx and action_labels.json

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2. Run train_slm.ipynb (Sequence Model)

1. Open Jupyter
2. Navigate to train_slm.ipynb
3. Run all cells

This trains a GPT-style SLM and exports as slm.onnx

Output: slm.onnx and slm_tokenizer.json

⚠️ Note: This model works best with short, structured text inputs (e.g., bot positions, angles). You can adjust max_dataset to limit training data.

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3. Run train_llm.ipynb (LLM with LoRA)

1. Open Jupyter
2. Navigate to train_llm.ipynb
3. Run all cells

This fine-tunes a Qwen-0.5B model using LoRA and exports the adapter

Output: adapters/qwen2.5_0.5b_lora/ (contains model + tokenizer)

Requires ~4-8GB VRAM for full training. For low-end systems, reduce max_dataset or num_train_epoch.

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Adjusting Configurations

Modify key settings in each notebook:

In train_ml.ipynb:

• Change features to include/exclude bot or enemy stats
• Adjust batch_size, epochs, or learning_rate for performance
• Set max_dataset to limit training (e.g., max_dataset=1000)

In train_slm.ipynb:

• Adjust block_size, batch_size, n_layers, lr
• Set max_dataset to control training data size
• Change prompt in generate() to test different inputs

In train_llm.ipynb:

• Set max_dataset to limit training (e.g., max_dataset=10000)
• Adjust batches_per_device, gradient_accumulation, learning_rate, or num_train_epoch
• Modify prompt in inference section to test different behaviors

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Notes for All Platforms

Platform	Notes
Windows	Use pip install in terminal; Jupyter works via jupyter notebook or jupyter lab
macOS	Use pip or pip3; Jupyter works via jupyter notebook or jupyter lab
Linux (Ubuntu/Debian)	Use pip install and jupyter notebook

To rerun: Restart Jupyter kernel and re-run from the top.

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Known Issues & Tips

• train_llm.ipynb: may fail on CPU due to lack of VRAM — use device_map="auto" or limit max_dataset
• train_ml.ipynb: Ensure cleaned_log.csv for has columns: Name, Duration, and the listed features
• Test outputs by changing the prompt or input data

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Example Prompt Test (for all models)

For train_slm.ipynb and train_llm.ipynb, you can test with:

BotPos=[2.23,2.25], BotRot=228, EnemyPos=[2.87,0.39], EnemyRot=87, AngleToEnemy=-29.68, AngleToEnemyScore=0.87, DistanceToEnemyScore=0.79, NearBorderArenaScore=0.42, FacingToArena=0.65. Suggested Action:

The models will generate a response based on the provided game state.

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Summary

Model	Use Case	Exported To
ML	Action classification	ml.onnx
SLM	Sequence prediction	slm.onnx
LLM	Natural language reasoning	adapters/qwen2.5_0.5b_lora/
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