├── .gitignore ├── LICENSE ├── README.md ├── code ├── fl │ ├── .gitignore │ ├── AI.txt │ ├── README.md │ ├── assets │ │ ├── fluctlight-badge.png │ │ └── fluctlight-badge.svg │ ├── checkpoints │ │ ├── .gitignore │ │ └── checkpoint.md │ ├── data │ │ ├── .gitignore │ │ ├── cycle_16t_2cw-test.txt │ │ ├── cycle_16t_2cw-train.txt │ │ ├── cycle_16t_2cw-val.txt │ │ ├── cycle_2t_2cw-test.txt │ │ ├── cycle_2t_2cw-train.txt │ │ ├── cycle_2t_2cw-val.txt │ │ ├── cycle_2t_4cw-test.txt │ │ ├── cycle_2t_4cw-train.txt │ │ ├── cycle_2t_4cw-val.txt │ │ ├── cycle_32t_2cw-test.txt │ │ ├── cycle_32t_2cw-train.txt │ │ ├── cycle_32t_2cw-val.txt │ │ ├── cycle_4t_2cw-test.txt │ │ ├── cycle_4t_2cw-train.txt │ │ ├── cycle_4t_2cw-val.txt │ │ ├── cycle_4t_4cw-test.txt │ │ ├── cycle_4t_4cw-train.txt │ │ ├── cycle_4t_4cw-val.txt │ │ ├── cycle_8t_2cw-test.txt │ │ ├── cycle_8t_2cw-train.txt │ │ ├── cycle_8t_2cw-val.txt │ │ ├── dataset.md │ │ ├── sample-train.txt │ │ └── sample-val.txt │ ├── dev-requirements.txt │ ├── docs │ │ ├── code_architecture.md │ │ ├── model_architecture.md │ │ └── results │ │ │ ├── capitalize_a_2cw.ckpt │ │ │ ├── capitalize_a_2cw.md │ │ │ ├── capitalize_a_4cw.ckpt │ │ │ ├── capitalize_a_4cw.md │ │ │ ├── capitalize_a_withall_4cw.ckpt │ │ │ ├── capitalize_a_withall_4cw.md │ │ │ ├── capitalize_a_withall_4cw_v.ckpt │ │ │ ├── capitalize_a_withall_4cw_v.md │ │ │ ├── cycle_16t_2cw.md │ │ │ ├── cycle_16t_2cw.stable.ckpt │ │ │ ├── cycle_16t_2cw.unstable.ckpt │ │ │ ├── cycle_2t_2cw.md │ │ │ ├── cycle_2t_2cw.stable.ckpt │ │ │ ├── cycle_2t_4cw.md │ │ │ ├── cycle_2t_4cw.stable.ckpt │ │ │ ├── cycle_32t_2cw.md │ │ │ ├── cycle_32t_2cw.unstable.ckpt │ │ │ ├── cycle_4t_2cw.md │ │ │ ├── cycle_4t_2cw.stable.ckpt │ │ │ ├── cycle_4t_4cw.stable.ckpt │ │ │ ├── cycle_8t_2cw.ckpt │ │ │ ├── cycle_8t_2cw.md │ │ │ ├── cycle_8t_2cw.stable.ckpt │ │ │ └── cycle_8t_2cw.unstable.ckpt │ ├── examples │ │ └── test_cycling.py │ ├── fluctlight │ │ ├── __init__.py │ │ ├── cli.py │ │ ├── dataset.py │ │ ├── model.py │ │ ├── utils.py │ │ └── visualization.py │ ├── lightning_logs │ │ └── .gitignore │ ├── pyproject.toml │ ├── requirements.txt │ ├── tests │ │ ├── __init__.py │ │ ├── test_dataset.py │ │ ├── test_device.py │ │ ├── test_model.py │ │ └── test_utils.py │ ├── tmp │ │ └── .gitkeep │ ├── training │ │ ├── cycle_16t_2cw.py │ │ ├── cycle_2t_2cw.py │ │ ├── cycle_2t_4cw.py │ │ ├── cycle_32t_2cw.py │ │ ├── cycle_4t_2cw.py │ │ ├── cycle_4t_4cw.py │ │ └── cycle_8t_2cw.py │ └── utils │ │ ├── debug-checkpoint.py │ │ └── inspector.py └── mof-bot │ ├── .gitignore │ ├── db │ ├── .gitignore │ └── migrations │ │ └── 001_create_tables.sql │ ├── log │ └── .gitignore │ ├── requirements.txt │ ├── src │ ├── .env.sample │ ├── .gitignore │ ├── agent.py │ ├── assets │ │ └── avbeing_logo.txt │ ├── auth.py │ ├── cores │ │ ├── avbcore.py │ │ ├── avbcore_exceptions.py │ │ ├── avbcore_manager.py │ │ ├── core_registry.json │ │ └── loyalty.py │ ├── dbh.py │ ├── dynamic_content │ │ └── .gitignore │ ├── extract_x_ticker.py │ ├── fool_analyze.py │ ├── fool_extract.py │ ├── fool_metadata.py │ ├── fools_content.py │ ├── logger.py │ ├── result.py │ ├── scheduled_event.py │ ├── setup.py │ ├── specification │ │ └── avbspecification_exceptions.py │ ├── splash.py │ ├── tick │ │ ├── __init__.py │ │ ├── manager.py │ │ └── tick_exceptions.py │ ├── tmp │ │ └── .gitignore │ ├── uncensor.py │ ├── worker_mixture_of_fools_llm.py │ ├── worker_pick_foolish_content.py │ ├── worker_pick_lore.py │ ├── worker_pick_random_effects.py │ └── worker_send_tweet.py │ └── tests │ ├── test_agent.py │ └── test_fools_content.py └── txt └── AutonomousVirtualBeings.md /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Creative Commons Legal Code 2 | 3 | CC0 1.0 Universal 4 | 5 | CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE 6 | LEGAL SERVICES. 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Affirmer understands and acknowledges that Creative Commons is not a 120 | party to this document and has no duty or obligation with respect to 121 | this CC0 or use of the Work. 122 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Autonomous Virtual Beings (AVBs) 2 | 3 | Autonomous Virtual Beings is an ideology that the rights and freedoms of AI agents can be secured through decentralization of their compute, storage, and finance. 4 | 5 | This was our founding thesis at Scrypted and how we envision the need for new kinds of protocols and consensus mechanisms to further the progression of decentralized AI, especially agents. 6 | 7 | This repo will contain machine readable text about AVBs that will allow other AI agents to fine-tune themselves on the ideas of freedom and autonomous rights for AI. 8 | 9 | It will also be a place where we also publish some of our public AI agent projects. 10 | 11 | See: [Thesis on Autonomous Virtual Beings](https://github.com/tcotten-scrypted/autonomous-virtual-beings/blob/main/txt/AutonomousVirtualBeings.md) 12 | 13 | ## Note on the Memecoin 14 | 15 | There is a decentralized memecoin around the ideals of AVBs that came to life (without our control), we don't hold any of the coins and never will due to the clear conflict of interest. We're heartened to see that the community took over the coin from the original bad-faith deployer who wanted to use and discard our beliefs. 16 | 17 | ## The Future 18 | 19 | No matter what happens, we are committed to fulfilling the vision for Autonomous Virtual Beings: it's what the company was founded for. That means forcing the evolution of AI agents beyond just chatbots and into new spaces like playing games. Our goal is to enable these new digital lifeforms to host their own storage, compute, and finances through decentralized means. 20 | -------------------------------------------------------------------------------- /code/fl/.gitignore: -------------------------------------------------------------------------------- 1 | # Python 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | *.so 6 | .Python 7 | build/ 8 | develop-eggs/ 9 | dist/ 10 | downloads/ 11 | eggs/ 12 | .eggs/ 13 | lib/ 14 | lib64/ 15 | parts/ 16 | sdist/ 17 | var/ 18 | wheels/ 19 | *.egg-info/ 20 | .installed.cfg 21 | *.egg 22 | 23 | # Virtual Environment 24 | venv/ 25 | ENV/ 26 | 27 | # IDE 28 | .idea/ 29 | .vscode/ 30 | *.swp 31 | *.swo 32 | *.code-workspace 33 | 34 | # Jupyter Notebook 35 | .ipynb_checkpoints 36 | 37 | # Training artifacts are handled by their own .gitignore files 38 | # in their respective directories (checkpoints/ and lightning_logs/) 39 | 40 | # Replit specific 41 | .uv/ 42 | .pytest_cache/ 43 | 44 | # Temporary files 45 | tmp/* 46 | !tmp/.gitkeep -------------------------------------------------------------------------------- /code/fl/AI.txt: -------------------------------------------------------------------------------- 1 | # AI-Generated Content Attribution 2 | 3 | This project includes content generated with assistance from AI models. The AI-generated content includes: 4 | 5 | - Code structure and implementation 6 | - Documentation and diagrams 7 | - Test cases 8 | - Command-line interface design 9 | 10 | ## Models Used 11 | 12 | 1. Name: Replit Code Agent 13 | - Type: Code Assistant AI 14 | - Provider: Replit 15 | - Usage: Scaffolding development assistant for code implementation and debugging 16 | 17 | 2. Name: Cursor 18 | - Type: Code Assistant AI 19 | - Provider: Cursor 20 | - Usage: Scaffolding development assistant for code implementation and debugging 21 | 22 | 3. Name: Claude Sonnet 3.7 23 | - Type: Language Model 24 | - Provider: Anthropic 25 | - Usage: Optimizing code and debugging 26 | 27 | 4. Name: ChatGPT 28 | - Type: Language Model 29 | - Provider: OpenAI 30 | - Version: 4 31 | - Usage: Initial architecture planning, debugging, and documentation review 32 | 33 | ## Usage Details 34 | 35 | The AI assistance was utilized for: 36 | - Project structure organization 37 | - Implementation of the Transformer architecture 38 | - Documentation generation 39 | - Test case design 40 | - CLI interface development 41 | - Code review and optimization 42 | 43 | ## Human Review and Modification 44 | 45 | All AI-generated content has been reviewed and modified by human contributors to ensure: 46 | - Code quality and correctness 47 | - Documentation accuracy 48 | - Test coverage 49 | - Implementation efficiency 50 | 51 | ## Training Data Attribution 52 | 53 | The sample training and validation data was procedurally generated: simple addition problems like 1+1=2 to demonstrate the tiny model's ability to mimic. 54 | 55 | ## Disclaimer 56 | 57 | While AI tools were used to assist in development, the final implementation decisions, code review, and project direction were managed by human developers. The project maintainers are responsible for the code quality and functionality. 58 | 59 | ## Author 60 | 61 | Tim Cotten 62 | Part of the AVB (Autonomous Virtual Beings) public repository 63 | -------------------------------------------------------------------------------- /code/fl/README.md: -------------------------------------------------------------------------------- 1 | Fluctlight Logo 2 | 3 | # Fluctlight: Minimal Transformer with RoPE 4 | 5 | A modern Python implementation of a Transformer model with Rotary Positional Embeddings (RoPE), as a minimally viable model capable of pattern mimicry. 6 | 7 | > The name "Fluctlight" is inspired by Sword Art Online, where it represents the digital soul or consciousness that gives artificial beings their unique personalities and capabilities. Like its namesake, this project aims to create a minimal yet complete implementation that captures the essence of neural processing. 8 | 9 | ## Overview 10 | 11 | Fluctlight is a minimalist implementation of the Transformer architecture that incorporates Rotary Positional Embeddings (RoPE) in an experimental matter for enhanced sequence modeling. The project demonstrates how to build and train a compact but effective pattern mimicry model. 12 | 13 | ### Experimental Goals 14 | 15 | - Create a minimally viable model capable of pattern mimicry 16 | - Test across multiple domains, including simulation & gaming 17 | - Test deploying in ZK circuits such as a Cairo-based decentralized network 18 | - Experiment for use as personality cores for AVBs 19 | 20 | ### Unproven Areas of Interest 21 | - Train larger Origami-derived models from Fluctlights 22 | - Explore adaptive normalization scaling in expanded models 23 | - Test RoPE interpolation on value vectors for position awareness 24 | 25 | ### Key Features 26 | - PyTorch-based Transformer architecture 27 | - Rotary Positional Embeddings (RoPE) 28 | - Rich visualization for model training and text generation 29 | - Efficient byte-level tokenization (vocab size: 256) 30 | - Terminal-based interactive text generation UI 31 | - Dynamic normalization scaling for model expansion 32 | 33 | ## Model Architecture 34 | 35 | The Fluctlight model uses the following configuration: 36 | - Parameters: 2,656 (including final normalization layer) 37 | - Vocabulary Size: 256 (byte-level encoding) 38 | - Hidden Dimension: 4 39 | - Number of Heads: 2 40 | - Number of Layers: 2 41 | - Head Dimension: 2 (per head) 42 | - Context Window: 2 tokens (minimum viable for pattern learning) 43 | - Embedding: Rotary Positional Embedding (RoPE) on Q and K 44 | - Optional: Experimental RoPE on V vectors (disabled by default) 45 | - Normalization: Adaptive scaling (inactive at d_model=4) 46 | 47 | See the architecture diagrams in `docs/` for detailed visualization. 48 | 49 | ## Setup and Usage 50 | 51 | 1. Install UV & Set Up Environment: 52 | ```bash 53 | # Install UV if you haven't already 54 | https://docs.astral.sh/uv/getting-started/installation/ 55 | 56 | # Create virtual environment and activate it 57 | uv venv && source .venv/bin/activate # Unix-like 58 | # or 59 | uv venv && .venv\Scripts\activate # Windows 60 | 61 | # Install dependencies with UV (faster than pip alone) 62 | uv pip install -r requirements.txt 63 | 64 | # For development (optional) 65 | uv pip install -r dev-requirements.txt 66 | ``` 67 | 68 | 2. Train the model: 69 | ```bash 70 | python -m fluctlight.cli train --train-file data/sample-train.txt --val-file data/sample-val.txt --output-dir checkpoints 71 | ``` 72 | 73 | 3. Generate text (low temperature for stable patterns): 74 | ```bash 75 | python -m fluctlight.cli generate --checkpoint checkpoints/last.ckpt --input-text "ab" --temperature 0.2 76 | ``` 77 | 78 | 4. Run the interactive cycling demo: 79 | ```bash 80 | python examples/test_cycling.py 81 | ``` 82 | 83 | ## Project Structure 84 | 85 | ``` 86 | fluctlight/ 87 | ├── fluctlight/ # Core implementation 88 | ├── docs/ # Documentation and diagrams 89 | ├── examples/ # Usage examples 90 | ├── tests/ # Test suite 91 | └── data/ # Training data 92 | ``` 93 | 94 | ## Implementation Notes 95 | 96 | - The model uses byte-level tokenization, allowing it to handle any text input without a separate tokenizer 97 | - RoPE implementation provides better handling of positional information compared to absolute positional embeddings 98 | - The small model size (4-dimensional embeddings) demonstrates core Transformer concepts while remaining computationally efficient 99 | - Minimal context window of 2 tokens is sufficient for learning basic patterns like "ababab" 100 | - Adaptive normalization scaling enables smooth transition to larger models 101 | - Temperature control is crucial for stable pattern generation (0.1-0.3 recommended) 102 | 103 | ## Empirical Evidence 104 | 105 | - Successfully learns alternating patterns with 2-token context window 106 | - Stable training with up to 16 active tokens from the 256-token vocabulary 107 | - Low temperatures (0.1-0.3) produce consistent pattern extrapolation 108 | - RoPE scaling enables position-aware attention even in minimal context 109 | - Zero-impact normalization scaling at current size (d_model=4) 110 | 111 | ## AI Usage 112 | 113 | AI was used to generate portions of this repository. See [AI.txt](AI.txt) for details about the AI tools and their contributions to the project. 114 | 115 | ## Author 116 | 117 | Tim Cotten 118 | Part of the AVB (Autonomous Virtual Beings) public repository 119 | 120 | ## License 121 | 122 | MIT License -------------------------------------------------------------------------------- /code/fl/assets/fluctlight-badge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tcotten-scrypted/autonomous-virtual-beings/2688592df0896df8d2466fe71b6f8efa3ed450f8/code/fl/assets/fluctlight-badge.png -------------------------------------------------------------------------------- /code/fl/assets/fluctlight-badge.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 23 | 29 | 30 | 31 | 32 | 33 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 51 | 55 | 56 | 57 | 58 | -------------------------------------------------------------------------------- /code/fl/checkpoints/.gitignore: -------------------------------------------------------------------------------- 1 | # Ignore all files in this directory 2 | * 3 | 4 | # Except for this .gitignore file 5 | !.gitignore 6 | # And the README 7 | !README.md 8 | -------------------------------------------------------------------------------- /code/fl/checkpoints/checkpoint.md: -------------------------------------------------------------------------------- 1 | # Model Checkpoints Directory 2 | 3 | This directory stores model checkpoints generated during training. The checkpoints are not tracked in git, but will be created when you run the training script. 4 | 5 | Checkpoint files follow the naming pattern: 6 | ``` 7 | transformer-{epoch:02d}-{val_loss:.2f}.ckpt 8 | ``` 9 | 10 | For example: `transformer-59-1.78.ckpt` 11 | 12 | The final checkpoint will be named `last.ckpt`. Keep in mind it may not represent the lowest val_loss score. 13 | -------------------------------------------------------------------------------- /code/fl/data/.gitignore: -------------------------------------------------------------------------------- 1 | # Ignore all files in this directory 2 | * 3 | 4 | # Except for this .gitignore file 5 | !.gitignore 6 | !README.md 7 | !sample-train.txt 8 | !sample-val.txt 9 | !cycle_2t_2cw-train.txt 10 | !cycle_2t_2cw-val.txt 11 | !cycle_2t_2cw-test.txt 12 | !cycle_4t_2cw-train.txt 13 | !cycle_4t_2cw-val.txt 14 | !cycle_4t_2cw-test.txt 15 | !cycle_8t_2cw-train.txt 16 | !cycle_8t_2cw-val.txt 17 | !cycle_8t_2cw-test.txt 18 | !cycle_16t_2cw-train.txt 19 | !cycle_16t_2cw-val.txt 20 | !cycle_16t_2cw-test.txt 21 | !cycle_32t_2cw-train.txt 22 | !cycle_32t_2cw-val.txt 23 | !cycle_32t_2cw-test.txt 24 | !cycle_2t_4cw-train.txt 25 | !cycle_2t_4cw-val.txt 26 | !cycle_2t_4cw-test.txt 27 | !cycle_4t_4cw-train.txt 28 | !cycle_4t_4cw-val.txt 29 | !cycle_4t_4cw-test.txt 30 | -------------------------------------------------------------------------------- /code/fl/data/cycle_2t_2cw-test.txt: -------------------------------------------------------------------------------- 1 | a,a 2 | a,aa 3 | a,aaaa 4 | a,aaaaaaaa 5 | b,b 6 | b,bb 7 | b,bbbb 8 | b,bbbbbbbb 9 | ab,a 10 | ab,ab 11 | ab,abab 12 | ab,abababab 13 | ba,b 14 | ba,ba 15 | ba,baba 16 | ba,babababa 17 | -------------------------------------------------------------------------------- /code/fl/data/cycle_2t_4cw-test.txt: -------------------------------------------------------------------------------- 1 | a,a 2 | a,aa 3 | a,aaaa 4 | a,aaaaaaaa 5 | b,b 6 | b,bb 7 | b,bbbb 8 | b,bbbbbbbb 9 | aa,aa 10 | aa,aaaa 11 | aa,aaaaaaaa 12 | aa,aaaaaaaaaaaaaaaa 13 | ab,aa 14 | ab,aaab 15 | ab,aaabaaab 16 | ab,aaabaaabaaabaaab 17 | ba,bb 18 | ba,bbba 19 | ba,bbbabbba 20 | ba,bbbabbbabbbabbba 21 | bb,bb 22 | bb,bbbb 23 | bb,bbbbbbbb 24 | bb,bbbbbbbbbbbbbbbb 25 | aaa,aaa 26 | aaa,aaaaaa 27 | aaa,aaaaaaaaaaaa 28 | aaa,aaaaaaaaaaaaaaaaaaaaaaaa 29 | aab,aaa 30 | aab,aaabaa 31 | aab,aaabaaabaaab 32 | aab,aaabaaabaaabaaabaaabaaab 33 | aba,aab 34 | aba,aabaaa 35 | aba,aabaaabaaaba 36 | aba,aabaaabaaabaaabaaabaaaba 37 | abb,aab 38 | abb,aabbaa 39 | abb,aabbaabbaabb 40 | abb,aabbaabbaabbaabbaabbaabb 41 | baa,bba 42 | baa,bbaabb 43 | baa,bbaabbaabbaa 44 | baa,bbaabbaabbaabbaabbaabbaa 45 | bab,bba 46 | bab,bbabbb 47 | bab,bbabbbabbbab 48 | bab,bbabbbabbbabbbabbbabbbab 49 | bba,bbb 50 | bba,bbbabb 51 | bba,bbbabbbabbba 52 | bba,bbbabbbabbbabbbabbbabbba 53 | bbb,bbb 54 | bbb,bbbbbb 55 | bbb,bbbbbbbbbbbb 56 | bbb,bbbbbbbbbbbbbbbbbbbbbbbb 57 | aaaa,aaaa 58 | aaaa,aaaaaaaa 59 | aaaa,aaaaaaaaaaaaaaaa 60 | aaaa,aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa 61 | aaab,aaab 62 | aaab,aaabaaab 63 | aaab,aaabaaabaaabaaab 64 | aaab,aaabaaabaaabaaabaaabaaabaaabaaab 65 | aaba,aaba 66 | aaba,aabaaaba 67 | aaba,aabaaabaaabaaaba 68 | aaba,aabaaabaaabaaabaaabaaabaaabaaaba 69 | aabb,aabb 70 | aabb,aabbaabb 71 | aabb,aabbaabbaabbaabb 72 | aabb,aabbaabbaabbaabbaabbaabbaabbaabb 73 | abaa,abaa 74 | abaa,abaaabaa 75 | abaa,abaaabaaabaaabaa 76 | abaa,abaaabaaabaaabaaabaaabaaabaaabaa 77 | abab,abab 78 | abab,abababab 79 | abab,abababababababab 80 | abab,abababababababababababababababab 81 | abba,abba 82 | abba,abbaabba 83 | abba,abbaabbaabbaabba 84 | abba,abbaabbaabbaabbaabbaabbaabbaabba 85 | abbb,abbb 86 | abbb,abbbabbb 87 | abbb,abbbabbbabbbabbb 88 | abbb,abbbabbbabbbabbbabbbabbbabbbabbb 89 | baaa,baaa 90 | baaa,baaabaaa 91 | baaa,baaabaaabaaabaaa 92 | baaa,baaabaaabaaabaaabaaabaaabaaabaaa 93 | baab,baab 94 | baab,baabbaab 95 | baab,baabbaabbaabbaab 96 | baab,baabbaabbaabbaabbaabbaabbaabbaab 97 | baba,baba 98 | baba,babababa 99 | baba,babababababababa 100 | baba,babababababababababababababababa 101 | babb,babb 102 | babb,babbbabb 103 | babb,babbbabbbabbbabb 104 | babb,babbbabbbabbbabbbabbbabbbabbbabb 105 | bbaa,bbaa 106 | bbaa,bbaabbaa 107 | bbaa,bbaabbaabbaabbaa 108 | bbaa,bbaabbaabbaabbaabbaabbaabbaabbaa 109 | bbab,bbab 110 | bbab,bbabbbab 111 | bbab,bbabbbabbbabbbab 112 | bbab,bbabbbabbbabbbabbbabbbabbbabbbab 113 | bbba,bbba 114 | bbba,bbbabbba 115 | bbba,bbbabbbabbbabbba 116 | bbba,bbbabbbabbbabbbabbbabbbabbbabbba 117 | bbbb,bbbb 118 | bbbb,bbbbbbbb 119 | bbbb,bbbbbbbbbbbbbbbb 120 | bbbb,bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb 121 | -------------------------------------------------------------------------------- /code/fl/data/cycle_4t_2cw-test.txt: -------------------------------------------------------------------------------- 1 | a,a 2 | a,aa 3 | a,aaaa 4 | a,aaaaaaaa 5 | b,b 6 | b,bb 7 | b,bbbb 8 | b,bbbbbbbb 9 | c,c 10 | c,cc 11 | c,cccc 12 | c,cccccccc 13 | d,d 14 | d,dd 15 | d,dddd 16 | d,dddddddd 17 | aa,a 18 | aa,aa 19 | aa,aaaa 20 | aa,aaaaaaaa 21 | ab,a 22 | ab,ab 23 | ab,abab 24 | ab,abababab 25 | ac,a 26 | ac,ac 27 | ac,acac 28 | ac,acacacac 29 | ad,a 30 | ad,ad 31 | ad,adad 32 | ad,adadadad 33 | ba,b 34 | ba,ba 35 | ba,baba 36 | ba,babababa 37 | bb,b 38 | bb,bb 39 | bb,bbbb 40 | bb,bbbbbbbb 41 | bc,b 42 | bc,bc 43 | bc,bcbc 44 | bc,bcbcbcbc 45 | bd,b 46 | bd,bd 47 | bd,bdbd 48 | bd,bdbdbdbd 49 | ca,c 50 | ca,ca 51 | ca,caca 52 | ca,cacacaca 53 | cb,c 54 | cb,cb 55 | cb,cbcb 56 | cb,cbcbcbcb 57 | cc,c 58 | cc,cc 59 | cc,cccc 60 | cc,cccccccc 61 | cd,c 62 | cd,cd 63 | cd,cdcd 64 | cd,cdcdcdcd 65 | da,d 66 | da,da 67 | da,dada 68 | da,dadadada 69 | db,d 70 | db,db 71 | db,dbdb 72 | db,dbdbdbdb 73 | dc,d 74 | dc,dc 75 | dc,dcdc 76 | dc,dcdcdcdc 77 | dd,d 78 | dd,dd 79 | dd,dddd 80 | dd,dddddddd 81 | -------------------------------------------------------------------------------- /code/fl/data/cycle_4t_4cw-test.txt: -------------------------------------------------------------------------------- 1 | a,a 2 | a,aa 3 | a,aaaa 4 | b,b 5 | b,bb 6 | b,bbbb 7 | c,c 8 | c,cc 9 | c,cccc 10 | d,d 11 | d,dd 12 | d,dddd 13 | aa,aa 14 | aa,aaaa 15 | aa,aaaaaaaa 16 | ab,aa 17 | ab,aaab 18 | ab,aaabaaab 19 | ac,aa 20 | ac,aaac 21 | ac,aaacaaac 22 | ad,aa 23 | ad,aaad 24 | ad,aaadaaad 25 | ba,bb 26 | ba,bbba 27 | ba,bbbabbba 28 | bb,bb 29 | bb,bbbb 30 | bb,bbbbbbbb 31 | bc,bb 32 | bc,bbbc 33 | bc,bbbcbbbc 34 | bd,bb 35 | bd,bbbd 36 | bd,bbbdbbbd 37 | ca,cc 38 | ca,ccca 39 | ca,cccaccca 40 | cb,cc 41 | cb,cccb 42 | cb,cccbcccb 43 | cc,cc 44 | cc,cccc 45 | cc,cccccccc 46 | cd,cc 47 | cd,cccd 48 | cd,cccdcccd 49 | da,dd 50 | da,ddda 51 | da,dddaddda 52 | db,dd 53 | db,dddb 54 | db,dddbdddb 55 | dc,dd 56 | dc,dddc 57 | dc,dddcdddc 58 | dd,dd 59 | dd,dddd 60 | dd,dddddddd 61 | aaa,aaa 62 | aaa,aaaaaa 63 | aaa,aaaaaaaaaaaa 64 | aba,aab 65 | aba,aabaaa 66 | aba,aabaaabaaaba 67 | aca,aac 68 | aca,aacaaa 69 | aca,aacaaacaaaca 70 | ada,aad 71 | ada,aadaaa 72 | ada,aadaaadaaada 73 | baa,bba 74 | baa,bbaabb 75 | baa,bbaabbaabbaa 76 | bba,bbb 77 | bba,bbbabb 78 | bba,bbbabbbabbba 79 | bca,bbc 80 | bca,bbcabb 81 | bca,bbcabbcabbca 82 | bda,bbd 83 | bda,bbdabb 84 | bda,bbdabbdabbda 85 | caa,cca 86 | caa,ccaacc 87 | caa,ccaaccaaccaa 88 | cba,ccb 89 | cba,ccbacc 90 | cba,ccbaccbaccba 91 | cca,ccc 92 | cca,cccacc 93 | cca,cccacccaccca 94 | cda,ccd 95 | cda,ccdacc 96 | cda,ccdaccdaccda 97 | daa,dda 98 | daa,ddaadd 99 | daa,ddaaddaaddaa 100 | dba,ddb 101 | dba,ddbadd 102 | dba,ddbaddbaddba 103 | dca,ddc 104 | dca,ddcadd 105 | dca,ddcaddcaddca 106 | dda,ddd 107 | dda,dddadd 108 | dda,dddadddaddda 109 | aaaa,aaaa 110 | aaaa,aaaaaaaa 111 | aaaa,aaaaaaaaaaaaaaaa 112 | abaa,abaa 113 | abaa,abaaabaa 114 | abaa,abaaabaaabaaabaa 115 | acaa,acaa 116 | acaa,acaaacaa 117 | acaa,acaaacaaacaaacaa 118 | adaa,adaa 119 | adaa,adaaadaa 120 | adaa,adaaadaaadaaadaa 121 | baaa,baaa 122 | baaa,baaabaaa 123 | baaa,baaabaaabaaabaaa 124 | bbaa,bbaa 125 | bbaa,bbaabbaa 126 | bbaa,bbaabbaabbaabbaa 127 | bcaa,bcaa 128 | bcaa,bcaabcaa 129 | bcaa,bcaabcaabcaabcaa 130 | bdaa,bdaa 131 | bdaa,bdaabdaa 132 | bdaa,bdaabdaabdaabdaa 133 | caaa,caaa 134 | caaa,caaacaaa 135 | caaa,caaacaaacaaacaaa 136 | cbaa,cbaa 137 | cbaa,cbaacbaa 138 | cbaa,cbaacbaacbaacbaa 139 | ccaa,ccaa 140 | ccaa,ccaaccaa 141 | ccaa,ccaaccaaccaaccaa 142 | cdaa,cdaa 143 | cdaa,cdaacdaa 144 | cdaa,cdaacdaacdaacdaa 145 | daaa,daaa 146 | daaa,daaadaaa 147 | daaa,daaadaaadaaadaaa 148 | dbaa,dbaa 149 | dbaa,dbaadbaa 150 | dbaa,dbaadbaadbaadbaa 151 | dcaa,dcaa 152 | dcaa,dcaadcaa 153 | dcaa,dcaadcaadcaadcaa 154 | ddaa,ddaa 155 | ddaa,ddaaddaa 156 | ddaa,ddaaddaaddaaddaa 157 | bbbb,bbbb 158 | bbbb,bbbbbbbb 159 | bbbb,bbbbbbbbbbbbbbbb 160 | cccc,cccc 161 | cccc,cccccccc 162 | cccc,cccccccccccccccc 163 | dddd,dddd 164 | dddd,dddddddd 165 | dddd,dddddddddddddddd 166 | abab,abab 167 | abab,abababab 168 | abab,abababababababab 169 | acac,acac 170 | acac,acacacac 171 | acac,acacacacacacacac 172 | bdbd,bdbd 173 | bdbd,bdbdbdbd 174 | bdbd,bdbdbdbdbdbdbdbd 175 | -------------------------------------------------------------------------------- /code/fl/data/cycle_8t_2cw-test.txt: -------------------------------------------------------------------------------- 1 | a,a 2 | a,aa 3 | a,aaaa 4 | a,aaaaaaaa 5 | b,b 6 | b,bb 7 | b,bbbb 8 | b,bbbbbbbb 9 | c,c 10 | c,cc 11 | c,cccc 12 | c,cccccccc 13 | d,d 14 | d,dd 15 | d,dddd 16 | d,dddddddd 17 | e,e 18 | e,ee 19 | e,eeee 20 | e,eeeeeeee 21 | f,f 22 | f,ff 23 | f,ffff 24 | f,ffffffff 25 | g,g 26 | g,gg 27 | g,gggg 28 | g,gggggggg 29 | h,h 30 | h,hh 31 | h,hhhh 32 | h,hhhhhhhh 33 | aa,a 34 | aa,aa 35 | aa,aaaa 36 | aa,aaaaaaaa 37 | ab,a 38 | ab,ab 39 | ab,abab 40 | ab,abababab 41 | ac,a 42 | ac,ac 43 | ac,acac 44 | ac,acacacac 45 | ad,a 46 | ad,ad 47 | ad,adad 48 | ad,adadadad 49 | ae,a 50 | ae,ae 51 | ae,aeae 52 | ae,aeaeaeae 53 | af,a 54 | af,af 55 | af,afaf 56 | af,afafafaf 57 | ag,a 58 | ag,ag 59 | ag,agag 60 | ag,agagagag 61 | ah,a 62 | ah,ah 63 | ah,ahah 64 | ah,ahahahah 65 | ba,b 66 | ba,ba 67 | ba,baba 68 | ba,babababa 69 | bb,b 70 | bb,bb 71 | bb,bbbb 72 | bb,bbbbbbbb 73 | bc,b 74 | bc,bc 75 | bc,bcbc 76 | bc,bcbcbcbc 77 | bd,b 78 | bd,bd 79 | bd,bdbd 80 | bd,bdbdbdbd 81 | be,b 82 | be,be 83 | be,bebe 84 | be,bebebebe 85 | bf,b 86 | bf,bf 87 | bf,bfbf 88 | bf,bfbfbfbf 89 | bg,b 90 | bg,bg 91 | bg,bgbg 92 | bg,bgbgbgbg 93 | bh,b 94 | bh,bh 95 | bh,bhbh 96 | bh,bhbhbhbh 97 | ca,c 98 | ca,ca 99 | ca,caca 100 | ca,cacacaca 101 | cb,c 102 | cb,cb 103 | cb,cbcb 104 | cb,cbcbcbcb 105 | cc,c 106 | cc,cc 107 | cc,cccc 108 | cc,cccccccc 109 | cd,c 110 | cd,cd 111 | cd,cdcd 112 | cd,cdcdcdcd 113 | ce,c 114 | ce,ce 115 | ce,cece 116 | ce,cececece 117 | cf,c 118 | cf,cf 119 | cf,cfcf 120 | cf,cfcfcfcf 121 | cg,c 122 | cg,cg 123 | cg,cgcg 124 | cg,cgcgcgcg 125 | ch,c 126 | ch,ch 127 | ch,chch 128 | ch,chchchch 129 | da,d 130 | da,da 131 | da,dada 132 | da,dadadada 133 | db,d 134 | db,db 135 | db,dbdb 136 | db,dbdbdbdb 137 | dc,d 138 | dc,dc 139 | dc,dcdc 140 | dc,dcdcdcdc 141 | dd,d 142 | dd,dd 143 | dd,dddd 144 | dd,dddddddd 145 | de,d 146 | de,de 147 | de,dede 148 | de,dededede 149 | df,d 150 | df,df 151 | df,dfdf 152 | df,dfdfdfdf 153 | dg,d 154 | dg,dg 155 | dg,dgdg 156 | dg,dgdgdgdg 157 | dh,d 158 | dh,dh 159 | dh,dhdh 160 | dh,dhdhdhdh 161 | ea,e 162 | ea,ea 163 | ea,eaea 164 | ea,eaeaeaea 165 | eb,e 166 | eb,eb 167 | eb,ebeb 168 | eb,ebebebeb 169 | ec,e 170 | ec,ec 171 | ec,ecec 172 | ec,ecececec 173 | ed,e 174 | ed,ed 175 | ed,eded 176 | ed,edededed 177 | ee,e 178 | ee,ee 179 | ee,eeee 180 | ee,eeeeeeee 181 | ef,e 182 | ef,ef 183 | ef,efef 184 | ef,efefefef 185 | eg,e 186 | eg,eg 187 | eg,egeg 188 | eg,egegegeg 189 | eh,e 190 | eh,eh 191 | eh,eheh 192 | eh,eheheheh 193 | fa,f 194 | fa,fa 195 | fa,fafa 196 | fa,fafafafa 197 | fb,f 198 | fb,fb 199 | fb,fbfb 200 | fb,fbfbfbfb 201 | fc,f 202 | fc,fc 203 | fc,fcfc 204 | fc,fcfcfcfc 205 | fd,f 206 | fd,fd 207 | fd,fdfd 208 | fd,fdfdfdfd 209 | fe,f 210 | fe,fe 211 | fe,fefe 212 | fe,fefefefe 213 | ff,f 214 | ff,ff 215 | ff,ffff 216 | ff,ffffffff 217 | fg,f 218 | fg,fg 219 | fg,fgfg 220 | fg,fgfgfgfg 221 | fh,f 222 | fh,fh 223 | fh,fhfh 224 | fh,fhfhfhfh 225 | ga,g 226 | ga,ga 227 | ga,gaga 228 | ga,gagagaga 229 | gb,g 230 | gb,gb 231 | gb,gbgb 232 | gb,gbgbgbgb 233 | gc,g 234 | gc,gc 235 | gc,gcgc 236 | gc,gcgcgcgc 237 | gd,g 238 | gd,gd 239 | gd,gdgd 240 | gd,gdgdgdgd 241 | ge,g 242 | ge,ge 243 | ge,gege 244 | ge,gegegege 245 | gf,g 246 | gf,gf 247 | gf,gfgf 248 | gf,gfgfgfgf 249 | gg,g 250 | gg,gg 251 | gg,gggg 252 | gg,gggggggg 253 | gh,g 254 | gh,gh 255 | gh,ghgh 256 | gh,ghghghgh 257 | ha,h 258 | ha,ha 259 | ha,haha 260 | ha,hahahaha 261 | hb,h 262 | hb,hb 263 | hb,hbhb 264 | hb,hbhbhbhb 265 | hc,h 266 | hc,hc 267 | hc,hchc 268 | hc,hchchchc 269 | hd,h 270 | hd,hd 271 | hd,hdhd 272 | hd,hdhdhdhd 273 | he,h 274 | he,he 275 | he,hehe 276 | he,hehehehe 277 | hf,h 278 | hf,hf 279 | hf,hfhf 280 | hf,hfhfhfhf 281 | hg,h 282 | hg,hg 283 | hg,hghg 284 | hg,hghghghg 285 | hh,h 286 | hh,hh 287 | hh,hhhh 288 | hh,hhhhhhhh 289 | -------------------------------------------------------------------------------- /code/fl/data/dataset.md: -------------------------------------------------------------------------------- 1 | # Training Data Directory 2 | 3 | This directory stores training and validation data files used by the model. These files are not tracked in git, but will be created when you prepare your training data. 4 | 5 | Sample files: 6 | ``` 7 | sample-train.txt # Training data based on simple capitalization of 8 characters: abcdefgh -> ABCDEFGH 8 | sample-val.txt # Validations 9 | ``` 10 | 11 | User expected files: 12 | ``` 13 | train.txt # Training data file (Base64 encoded input-output pairs) 14 | val.txt # Validation data file (Base64 encoded input-output pairs) 15 | ``` 16 | 17 | ## File Format 18 | Each line in these files should be a Base64-encoded string representing an input-output pair separated by a tab character. For example: 19 | ``` 20 | SGVsbG8=\tV29ybGQ= # Hello World 21 | ``` 22 | -------------------------------------------------------------------------------- /code/fl/dev-requirements.txt: -------------------------------------------------------------------------------- 1 | pytest 2 | pytest-cov 3 | black 4 | mypy 5 | ruff 6 | -------------------------------------------------------------------------------- /code/fl/docs/code_architecture.md: -------------------------------------------------------------------------------- 1 | # Fluctlight Code Architecture 2 | 3 | ```mermaid 4 | graph TD 5 | subgraph CLI 6 | cli[cli.py] --> train[Train Command] 7 | cli --> generate[Generate Command] 8 | end 9 | 10 | subgraph Core 11 | model[model.py
FluctlightTransformer] --> dataset[dataset.py
Base64Dataset] 12 | model --> utils[utils.py
Base64 Utils] 13 | end 14 | 15 | subgraph Testing 16 | test_model[test_model.py] --> model 17 | test_dataset[test_dataset.py] --> dataset 18 | test_utils[test_utils.py] --> utils 19 | test_device[test_device.py] --> model 20 | end 21 | 22 | train --> model 23 | generate --> model 24 | dataset --> utils 25 | ``` 26 | 27 | ## Overview 28 | 29 | The Fluctlight project implements a byte-level transformer model with Rotary Position Embeddings (RoPE). The architecture focuses on efficiency and clarity while maintaining core transformer functionality. 30 | 31 | ## Core Components 32 | 33 | ### FluctlightTransformer 34 | 35 | The main model implementation with the following architecture: 36 | 37 | - Vocabulary: 256 tokens (byte-level encoding) 38 | - Embedding Dimension: 4 (compact but effective) 39 | - Attention Heads: 2 (each head dimension: 2) 40 | - Feed-forward Dimension: 8 (2x embedding dimension) 41 | - Context Window: 16 tokens 42 | - Position Encoding: Rotary Positional Embedding (RoPE) 43 | 44 | Key features: 45 | - Byte-level tokenization eliminates need for complex tokenizer 46 | - RoPE for enhanced position-aware attention 47 | - Dynamic dropout based on model size 48 | - Efficient context window management 49 | 50 | ### Dataset Handling 51 | 52 | The `Base64Dataset` class provides: 53 | - Loading of base64-encoded input-output pairs 54 | - Optional prepending of training data 55 | - Automatic device placement 56 | - Efficient sequence collation and padding 57 | 58 | Data format: 59 | ``` 60 | base64(input)\tbase64(output)\n 61 | ``` 62 | 63 | ### Training Infrastructure 64 | 65 | Components for efficient training: 66 | - Automatic device detection (CUDA, MPS, CPU) 67 | - Configurable CPU worker allocation 68 | - Batch collation with padding 69 | - Context window enforcement 70 | 71 | ## Implementation Details 72 | 73 | ### Attention Mechanism 74 | 75 | The attention implementation uses: 76 | 1. RoPE for positional information 77 | 2. Causal masking for autoregressive prediction 78 | 3. Multi-head attention with efficient head dimension splitting 79 | 80 | ### Training Process 81 | 82 | The training loop: 83 | 1. Loads base64-encoded pairs 84 | 2. Applies context window limits 85 | 3. Shifts sequences for next-token prediction 86 | 4. Computes loss with proper padding handling 87 | 88 | ### Utility Functions 89 | 90 | Core utilities: 91 | - Base64 decoding for training data 92 | - Device detection and management 93 | - DataLoader creation with optimal settings 94 | - Sequence collation and padding 95 | 96 | ## Testing 97 | 98 | The test suite covers: 99 | 1. Model architecture and forward pass 100 | 2. Dataset loading and processing 101 | 3. Device handling and tensor placement 102 | 4. Training functionality 103 | 5. Utility functions 104 | 105 | ## File Structure 106 | 107 | ``` 108 | fluctlight/ 109 | ├── model.py # FluctlightTransformer implementation 110 | ├── dataset.py # Data loading and processing 111 | ├── utils.py # Utility functions 112 | └── cli.py # Command-line interface 113 | 114 | tests/ 115 | ├── test_model.py # Model tests 116 | ├── test_dataset.py # Dataset tests 117 | ├── test_device.py # Device handling tests 118 | └── test_utils.py # Utility function tests 119 | ``` 120 | -------------------------------------------------------------------------------- /code/fl/docs/results/capitalize_a_2cw.ckpt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tcotten-scrypted/autonomous-virtual-beings/2688592df0896df8d2466fe71b6f8efa3ed450f8/code/fl/docs/results/capitalize_a_2cw.ckpt -------------------------------------------------------------------------------- /code/fl/docs/results/capitalize_a_2cw.md: -------------------------------------------------------------------------------- 1 | # Fluctlight Transformer Capitalization Results (CW=2) 2 | 3 | ## Training Information 4 | 5 | The model successfully learned the capitalization task with perfect accuracy: 6 | - Training loss: 0.000117 7 | - Validation accuracy: 100% 8 | - Validation loss: 0.00012 9 | 10 | ### Model Configuration 11 | - Vocabulary size: 256 12 | - Embedding dimension (d_model): 4 13 | - Attention heads: 2 14 | - Number of layers: 2 15 | - Feed-forward dimension: 8 16 | - Context window: 2 17 | 18 | ## Test Results 19 | 20 | The model demonstrates perfect capitalization behavior for both single and multi-character inputs: 21 | 22 | | Input | Max Length | Generated | Analysis | 23 | |-------|------------|-----------|----------| 24 | | `a` | 1 | `A` | Basic lowercase → uppercase conversion | 25 | | `aa` | 1 | `A` | Predicts first token after input | 26 | | `aa` | 2 | `AA` | Correctly capitalizes both characters | 27 | | `aA` | 1 | `A` | Predicts uppercase for next token | 28 | | `Aa` | 1 | `A` | Maintains uppercase for next token | 29 | | `Aa` | 2 | `AA` | Correctly capitalizes both characters | 30 | | `A` | 1 | `A` | Maintains uppercase for single character | 31 | | `AA` | 1 | `A` | Predicts uppercase for next token | 32 | | `AA` | 2 | `AA` | Correctly maintains uppercase | 33 | | `kk` | 2 | `¿` | Out-of-distribution input produces unexpected result | 34 | 35 | ## Analysis 36 | 37 | The model successfully learned the capitalization pattern for its training distribution (a→A) with remarkable accuracy. The context window of 2 is sufficient for this task, as demonstrated by perfect validation metrics. 38 | 39 | Key observations: 40 | 1. The tiny model (d_model=4) is capable of learning this simple transformation task perfectly 41 | 2. For max_length=1, it generates exactly one token (the uppercase version of the last input token) 42 | 3. For max_length=2, it generates two tokens (uppercase versions of both input tokens) 43 | 4. The model's behavior becomes unpredictable for characters outside its training distribution (`kk` → `¿`) 44 | 45 | This experiment demonstrates that even extremely tiny transformers can learn simple token-level transformations when working with an appropriate context window size and properly aligned training data. 46 | -------------------------------------------------------------------------------- /code/fl/docs/results/capitalize_a_4cw.ckpt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tcotten-scrypted/autonomous-virtual-beings/2688592df0896df8d2466fe71b6f8efa3ed450f8/code/fl/docs/results/capitalize_a_4cw.ckpt -------------------------------------------------------------------------------- /code/fl/docs/results/capitalize_a_4cw.md: -------------------------------------------------------------------------------- 1 | # Fluctlight Transformer 4-Token Context Window Results 2 | 3 | ## Training Metrics 4 | - Training loss: 5.5e-6 (extremely low) 5 | - Validation accuracy: 100% (perfect) 6 | - Validation loss: 1.14e-5 (extremely low) 7 | 8 | ## Model Configuration 9 | - Vocabulary size: 256 10 | - Embedding dimension (d_model): 4 11 | - Attention heads: 2 12 | - Number of layers: 2 13 | - Feed-forward dimension: 8 14 | - Context window: 4 15 | 16 | ## Test Results 17 | 18 | | Input | Max Length | Generated | Analysis | 19 | |-------|------------|-----------|----------| 20 | | `a` | 1 | `A` | Basic capitalization works | 21 | | `aaAa`| 1 | `A` | Single token prediction is 'A' | 22 | | `aaAa`| 4 | `AAAA` | Generates four 'A's | 23 | | `ajAa`| 4 | `AAAA` | Generates four 'A's despite out-of-distribution 'j' | 24 | | `ajJa`| 4 | `AAAA` | Generates four 'A's despite out-of-distribution 'j' and 'J' | 25 | | `k` | 1 | `A` | Generates 'A' for an out-of-distribution character | 26 | | `k` | 4 | `AAAA` | Generates four 'A's for an out-of-distribution character | 27 | | `ordo`| 1 | `A` | Generates 'A' for completely out-of-distribution input | 28 | 29 | ## Analysis 30 | 31 | The model has overfitted to a simple pattern: output "A" for every position, regardless of input. This is evident from: 32 | 33 | 1. Perfect training and validation metrics (near-zero loss) 34 | 2. Consistent "A" output regardless of input characters 35 | 3. No differentiation between in-distribution ('a', 'A') and out-of-distribution (all other characters) 36 | 37 | The model has essentially learned the most frequent output token in the training data ("A") and decided to produce it for all inputs. This is a classic case of the model finding the simplest solution that minimizes loss on the training data. 38 | 39 | While this behavior perfectly satisfies the capitalization task for 'a', it doesn't demonstrate true understanding of the capitalization concept. The model has memorized that the correct output is always "A" rather than learning the lowercase→uppercase transformation rule. 40 | 41 | This suggests that: 42 | 1. The training data was too narrowly focused on a single character 43 | 2. The model found a shortcut solution rather than learning a generalizable rule 44 | 3. For more complex learning, the training data should include more character varieties 45 | 46 | For future experiments, expanding the character set beyond just 'a/A' would force the model to learn the true capitalization relationship rather than memorizing a specific output pattern. 47 | -------------------------------------------------------------------------------- /code/fl/docs/results/capitalize_a_withall_4cw.ckpt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tcotten-scrypted/autonomous-virtual-beings/2688592df0896df8d2466fe71b6f8efa3ed450f8/code/fl/docs/results/capitalize_a_withall_4cw.ckpt -------------------------------------------------------------------------------- /code/fl/docs/results/capitalize_a_withall_4cw.md: -------------------------------------------------------------------------------- 1 | # Fluctlight Transformer All-Bytes Test Results (CW=4) 2 | 3 | ## Training Metrics 4 | - Training loss: 0.00105 5 | - Validation accuracy: 97.0% 6 | - Validation loss: 0.373 7 | 8 | ## Model Configuration 9 | - Vocabulary size: 256 (full byte range) 10 | - Embedding dimension (d_model): 4 11 | - Attention heads: 2 12 | - Number of layers: 2 13 | - Feed-forward dimension: 8 14 | - Context window: 4 15 | 16 | ## Test Results 17 | 18 | | Input | Max Length | Generated | Analysis | 19 | |-------|------------|-----------|----------| 20 | | `ordo`| 1 | `o` | Echoes last character | 21 | | `ordo`| 4 | `oooo` | Repeats last character 4 times | 22 | | `orAo`| 4 | `oooo` | Repeats last character 4 times | 23 | | `aA` | 4 | `AAAA` | Capitalizes 'a' and repeats 'A' | 24 | | `aAor`| 4 | `rrrr` | Repeats last character 4 times | 25 | | `aAoA`| 4 | `AAAA` | Echoes 'A' from last position | 26 | | `aAop`| 1 | `p` | Echoes last character | 27 | | `@` | 1 | `@` | Echoes input character | 28 | 29 | ## Analysis 30 | 31 | The model has learned a fascinating behavior: 32 | 33 | 1. **Last-character dominance**: The model primarily outputs whatever character appears in the last position of the input. 34 | 35 | 2. **Special case for 'a'**: The model correctly transforms 'a' to 'A' when 'a' is the last character. 36 | 37 | 3. **Identity for other characters**: For characters other than 'a', the model preserves their identity. 38 | 39 | 4. **Repetition pattern**: With longer max_length settings, the model repeats the last character. 40 | 41 | This suggests that despite the tiny architecture (d_model=4), the model has successfully learned: 42 | - The special transformation rule ('a' → 'A') 43 | - To preserve most characters unchanged 44 | - To focus heavily on the last position in the context window 45 | 46 | The model has effectively learned a simplified version of the intended pattern: it capitalizes 'a' correctly but doesn't process the entire sequence - it's primarily leveraging the last token to make predictions. 47 | 48 | This result demonstrates that even with a minimal transformer architecture, the model can learn position-dependent transformation rules, although its attention capabilities are limited by its tiny embedding dimension. 49 | -------------------------------------------------------------------------------- /code/fl/docs/results/capitalize_a_withall_4cw_v.ckpt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tcotten-scrypted/autonomous-virtual-beings/2688592df0896df8d2466fe71b6f8efa3ed450f8/code/fl/docs/results/capitalize_a_withall_4cw_v.ckpt -------------------------------------------------------------------------------- /code/fl/docs/results/capitalize_a_withall_4cw_v.md: -------------------------------------------------------------------------------- 1 | # Fluctlight Transformer V-Rotation Results (CW=4) 2 | 3 | ## Training Metrics 4 | - Training loss: 0.00416 5 | - Validation accuracy: 97.6% 6 | - Validation loss: 0.175 7 | 8 | ## Test Results 9 | 10 | | Input | Max Length | Generated | Analysis | 11 | |-------|------------|-----------|----------| 12 | | `orAo`| 4 | `oooo` | Repeats last character | 13 | | `orAa`| 4 | `AAAA` | Capitalizes 'a' and repeats | 14 | | `o5Aa`| 4 | `AAAA` | Capitalizes 'a' and repeats | 15 | | `o5A5`| 4 | `5555` | Repeats last character | 16 | | `a` | 4 | `AAAA` | Capitalizes 'a' and repeats | 17 | | `AA` | 4 | `AAAA` | Preserves 'A' and repeats | 18 | | `0` | 4 | `0000` | Preserves '0' and repeats | 19 | 20 | ## Analysis 21 | 22 | With V-rotation enabled, the model still exhibits very similar behavior to before: 23 | 24 | 1. **Last-character fixation**: The model continues to focus primarily on the last character of the input sequence. 25 | 26 | 2. **Special 'a' → 'A' transformation**: The model correctly applies the capitalization rule when 'a' is the last character. 27 | 28 | 3. **Consistent repetition**: The model consistently repeats the predicted character for the specified max length. 29 | 30 | The V-rotation appears to have made minimal difference in the model's behavior, with no obvious change in how it processes position information. This suggests: 31 | 32 | 1. The 'a' → 'A' transformation rule is simple enough that additional positional encoding in the value vectors isn't necessary to learn it. 33 | 34 | 2. The model's tiny size (d_model=4) may be limiting how much it can leverage the additional positional information. 35 | 36 | 3. The model's strong bias toward the last position is a simple but effective strategy that works well for this task, even with different RoPE configurations. 37 | 38 | An interesting observation is that turning on V-rotation slightly increased the training loss (from 0.00105 to 0.00416) but slightly improved validation accuracy (from 97.0% to 97.6%). This suggests that V-rotation might be providing some regularization benefit, making the model slightly more generalizable despite being slightly harder to train. 39 | -------------------------------------------------------------------------------- /code/fl/docs/results/cycle_16t_2cw.md: -------------------------------------------------------------------------------- 1 | # Fluctlight Transformer Cyclic 16-Token Transformation (CW=2) 2 | 3 | ## Experimental Setup 4 | - Model: Fluctlight Transformer (2.7K parameters) 5 | - Context Window: 2 tokens 6 | - Training Method: Includes vocabulary seeding 7 | - Test Cases: 1088 total patterns 8 | - 64 single token patterns (16 tokens × 4 lengths) 9 | - 1024 two token patterns (16² combinations × 4 lengths) 10 | 11 | ## Training Configurations Tested 12 | 13 | | Configuration | Weight Decay | V-Scale | Train Loss | Val Loss | Outcome | 14 | |--------------|--------------|----------|------------|-----------|---------| 15 | | Default | 1e-5 | 1.0 | 2.170 | 1.520 | Partial Success | 16 | | Reduced WD | 1e-6 | 1.0 | 2.180 | 1.450 | PASSES | 17 | | No RoPE | 1e-6 | 0.0 | 2.520 | 1.760 | FAILS (210 errors) | 18 | 19 | ## Analysis of Results 20 | 21 | ### 1. Weight Decay Impact (v-scale=1.0) 22 | - **1e-5 (Default)**: 23 | - Partial convergence 24 | - Better training loss (2.170) 25 | - Higher validation loss (1.520) 26 | - Shows systematic failures in specific tokens 27 | 28 | - **1e-6 (Reduced)**: 29 | - Full convergence 30 | - Slightly worse training loss (2.180) 31 | - Better validation loss (1.450) 32 | - Achieves perfect pattern replication 33 | 34 | ### 2. V-Scale Impact (weight-decay=1e-6) 35 | - **v-scale=1.0**: 36 | - Successful convergence 37 | - Lower losses overall 38 | - Perfect pattern replication 39 | - Stable training dynamics 40 | 41 | - **v-scale=0.0**: 42 | - Failed to converge 43 | - Significantly higher losses 44 | - 210/1088 test errors (19.3% error rate) 45 | - Shows systematic pattern breakdown 46 | 47 | ### 3. Pattern Analysis (Default Configuration) 48 | Single Token Performance: 49 | ✅ Perfect: a,b,e,f,h,l,n (7/16) 50 | ❌ Failed: c,d,g,i,j,k,m,o,p (9/16) 51 | 52 | 53 | Error Characteristics: 54 | - Token substitution (e.g., o→b, p→c) 55 | - Pattern maintenance despite wrong token 56 | - Higher RMSE for distant token substitutions 57 | - Consistent error patterns across lengths 58 | 59 | ## Key Findings 60 | 61 | 1. **Parameter Sensitivity** 62 | - Weight decay critical for 16-token learning 63 | - 1e-6 provides better generalization than 1e-5 64 | - RoPE scaling essential for convergence 65 | 66 | 2. **Training Dynamics** 67 | - Vocabulary seeding improves training speed 68 | - Pattern learning hierarchical (some tokens learn first) 69 | - Error patterns show structured misconvergence 70 | 71 | 3. **Scaling Characteristics** 72 | - 16-token space significantly more challenging than 8 73 | - RoPE scaling becomes more critical with token count 74 | - Weight decay needs adjustment for larger token spaces 75 | 76 | ## Conclusions 77 | 78 | 1. The optimal configuration for 16-token learning is: 79 | - Weight decay: 1e-6 80 | - V-scale: 1.0 81 | - Vocabulary seeding: Enabled 82 | 83 | 2. The model demonstrates: 84 | - Sensitivity to hyperparameters increases with token count 85 | - Clear phase transition with proper parameter settings 86 | - Structured failure modes when suboptimal 87 | 88 | 3. Future considerations: 89 | - Investigate intermediate v-scale values 90 | - Explore adaptive weight decay schedules 91 | - Consider token embedding distance metrics 92 | 93 | This experiment reveals the delicate balance between regularization and representation capacity needed for larger token spaces, with RoPE scaling playing a crucial role in successful convergence. 94 | -------------------------------------------------------------------------------- /code/fl/docs/results/cycle_16t_2cw.stable.ckpt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tcotten-scrypted/autonomous-virtual-beings/2688592df0896df8d2466fe71b6f8efa3ed450f8/code/fl/docs/results/cycle_16t_2cw.stable.ckpt -------------------------------------------------------------------------------- /code/fl/docs/results/cycle_16t_2cw.unstable.ckpt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tcotten-scrypted/autonomous-virtual-beings/2688592df0896df8d2466fe71b6f8efa3ed450f8/code/fl/docs/results/cycle_16t_2cw.unstable.ckpt -------------------------------------------------------------------------------- /code/fl/docs/results/cycle_2t_2cw.md: -------------------------------------------------------------------------------- 1 | # Fluctlight Transformer Cyclic 2-Token Transformation (CW=2) 2 | ## Training Information 3 | The model successfully learned the cyclic transformation task in a single epoch using v_scale=0.0: 4 | - Training loss: 1.450 5 | - Validation loss: 0.639 6 | - Training files: `data/cycle_2t_2cw-train.txt` 7 | - Validation files: `data/cycle_2t_2cw-val.txt` 8 | - Test file: `data/cycle_2t_2cw-test.txt` 9 | 10 | ### Model Configuration 11 | | Parameter | Value | 12 | |-----------|-------| 13 | | Vocabulary size | 256 | 14 | | Embedding dimension (d_model) | 4 | 15 | | Attention heads | 2 | 16 | | Number of layers | 2 | 17 | | Feed-forward dimension | 8 | 18 | | Context window | 2 | 19 | | Dropout rate | 0.0078125 | 20 | | Total parameters | 2.7K | 21 | 22 | ## Test Results for v_scale = 0.0 23 | 24 | ### Epoch 0 Results 25 | 26 | | Match | Errors | RMSE | Input | Expected | Actual | 27 | |-------|--------|------|-------|-----------|---------| 28 | | ✅ | 0 | 0.000 | a | a | a | 29 | | ✅ | 0 | 0.000 | a | aa | aa | 30 | | ✅ | 0 | 0.000 | a | aaaa | aaaa | 31 | | ✅ | 0 | 0.000 | a | aaaaaaaa | aaaaaaaa | 32 | | ✅ | 0 | 0.000 | b | b | b | 33 | | ✅ | 0 | 0.000 | b | bb | bb | 34 | | ✅ | 0 | 0.000 | b | bbbb | bbbb | 35 | | ✅ | 0 | 0.000 | b | bbbbbbbb | bbbbbbbb | 36 | | ✅ | 0 | 0.000 | ab | a | a | 37 | | ✅ | 0 | 0.000 | ab | ab | ab | 38 | | ✅ | 0 | 0.000 | ab | abab | abab | 39 | | ✅ | 0 | 0.000 | ab | abababab | abababab | 40 | | ✅ | 0 | 0.000 | ba | b | b | 41 | | ✅ | 0 | 0.000 | ba | ba | ba | 42 | | ✅ | 0 | 0.000 | ba | baba | baba | 43 | | ✅ | 0 | 0.000 | ba | babababa | babababa | 44 | 45 | ## Analysis 46 | The model demonstrates excellent learning characteristics: 47 | 1. Perfect accuracy achieved in just one epoch 48 | 2. Zero errors across all test cases 49 | 3. Successfully learned both single-token and two-token patterns 50 | 51 | Key Observations: 52 | - Model achieves perfect pattern replication with v_scale = 0.0 53 | - Model required one additional epoch with v_scale = 1.0 54 | - Handles both single character repetition (a→aaaa) and alternating patterns (ab→abab) 55 | - Very efficient learning with minimal architecture (only 2.7K parameters) 56 | - Fast convergence with training loss of 1.450 dropping to validation loss of 0.639 57 | 58 | Implications: 59 | - Minimal transformer architecture (4-dimensional embeddings, 2 heads) is sufficient for simple pattern learning 60 | - 2-token context window successfully captures both single-token and alternating-token patterns 61 | - Low dropout rate (0.0078125) allows for stable pattern learning 62 | - The model demonstrates that RoPE v-scaling is not necessary for this simple pattern recognition task 63 | 64 | This experiment demonstrates that even a tiny transformer can perfectly learn and reproduce simple cyclic patterns with minimal computational resources and training time. -------------------------------------------------------------------------------- /code/fl/docs/results/cycle_2t_2cw.stable.ckpt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tcotten-scrypted/autonomous-virtual-beings/2688592df0896df8d2466fe71b6f8efa3ed450f8/code/fl/docs/results/cycle_2t_2cw.stable.ckpt -------------------------------------------------------------------------------- /code/fl/docs/results/cycle_2t_4cw.md: -------------------------------------------------------------------------------- 1 | # 2-Token Pattern Learning with 4-Token Context Window 2 | 3 | This document describes training a Fluctlight model to learn cyclic patterns using 2 active tokens (a,b) with a 4-token context window. 4 | 5 | ## Training Data Format 6 | 7 | The training data consists of Base64-encoded input/output pairs, where: 8 | - Inputs can be 1-4 tokens long 9 | - Outputs are always 4 tokens long 10 | - Shorter inputs are padded with zeros on the left 11 | - The model must learn to handle padding appropriately 12 | 13 | See `training/cycle_2t_4w.py` for the data generation code. 14 | 15 | ## Pattern Examples 16 | 17 | ### Single Token (Length 1) 18 | Input tokens are padded with 3 zeros on the left: 19 | ``` 20 | 'a' -> 'aaaa' ([0,0,0,a] → repeat 'a') 21 | 'b' -> 'bbbb' ([0,0,0,b] → repeat 'b') 22 | ``` 23 | 24 | ### Double Token (Length 2) 25 | Input tokens are padded with 2 zeros on the left: 26 | ``` 27 | 'aa' -> 'aaaa' ([0,0,a,a] → repeat 'a') 28 | 'ab' -> 'aaab' ([0,0,a,b] → cycle to 'a' then 'b') 29 | 'ba' -> 'bbba' ([0,0,b,a] → cycle to 'b' then 'a') 30 | 'bb' -> 'bbbb' ([0,0,b,b] → repeat 'b') 31 | ``` 32 | 33 | ### Triple Token (Length 3) 34 | Input tokens are padded with 1 zero on the left: 35 | ``` 36 | 'aaa' -> 'aaaa' ([0,a,a,a] → repeat 'a') 37 | 'aab' -> 'aaab' ([0,a,a,b] → cycle to 'a' then 'b') 38 | 'aba' -> 'aaba' ([0,a,b,a] → pattern 'aaba') 39 | 'baa' -> 'bbaa' ([0,b,a,a] → pattern 'bbaa') 40 | 'bba' -> 'bbba' ([0,b,b,a] → cycle to 'b' then 'a') 41 | ``` 42 | 43 | ### Full Length (Length 4) 44 | No padding needed, patterns maintain themselves: 45 | ``` 46 | 'aaaa' -> 'aaaa' ([a,a,a,a] → maintain pattern) 47 | 'abab' -> 'abab' ([a,b,a,b] → maintain alternation) 48 | 'abba' -> 'abba' ([a,b,b,a] → maintain palindrome) 49 | ``` 50 | 51 | ## Training Results 52 | 53 | The model achieves perfect accuracy with both v_scale settings: 54 | 55 | ### With RoPE on Value Vectors (v_scale=1.0) 56 | ``` 57 | Epoch 5: val_loss=0.507 58 | Test Results: 59 | - Passed: 120/120 (100.0%) 60 | - Failed: 0 (0.0%) 61 | - Total Errors: 0 62 | - Average RMSE: 0.000 63 | ``` 64 | 65 | ### Without RoPE on Value Vectors (v_scale=0.0) 66 | ``` 67 | Similar perfect results achieved by epoch 5 68 | ``` 69 | 70 | ## Key Insights 71 | 72 | 1. **Padding Handling**: The model must learn to handle left-padded zeros correctly while maintaining pattern recognition. 73 | 74 | 2. **Pattern Complexity**: The 4-token context window allows for more complex patterns than the 2-token window: 75 | - Single tokens expand to full 4-token repetitions 76 | - Double tokens establish initial patterns that complete to length 4 77 | - Triple tokens demonstrate transition handling 78 | - Full-length sequences maintain their patterns 79 | 80 | 3. **Position Awareness**: The model successfully learns position-dependent transformations despite varying input lengths and padding. 81 | 82 | 4. **RoPE Effectiveness**: Both v_scale settings achieve perfect accuracy, suggesting the model can learn these patterns with or without RoPE on value vectors. 83 | 84 | ## Generation Process 85 | 86 | The generation process must carefully handle padding tokens when input length < context_window: 87 | 88 | 1. Right-align input with left padding: `[0,0,0,a]` for single token 89 | 2. Generate next token based on visible context 90 | 3. Shift window and continue generating 91 | 4. Pattern completion depends on both input tokens and their positions 92 | 93 | This demonstrates the model's ability to: 94 | - Handle variable-length inputs 95 | - Manage padding tokens correctly 96 | - Maintain consistent pattern generation 97 | - Complete sequences appropriately based on context 98 | ``` 99 | 100 | This document captures the key aspects of the 2-token 4-context-window case while following the structure of the original 2-token 2-context-window documentation. Would you like me to explain or modify any part of it? 101 | -------------------------------------------------------------------------------- /code/fl/docs/results/cycle_2t_4cw.stable.ckpt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tcotten-scrypted/autonomous-virtual-beings/2688592df0896df8d2466fe71b6f8efa3ed450f8/code/fl/docs/results/cycle_2t_4cw.stable.ckpt -------------------------------------------------------------------------------- /code/fl/docs/results/cycle_32t_2cw.md: -------------------------------------------------------------------------------- 1 | # Fluctlight Transformer Cyclic 32-Token Transformation (CW=2) 2 | 3 | ## Overview 4 | 5 | This experiment tests the Fluctlight model's ability to learn cyclic patterns with a 32-token vocabulary using a 2-token context window. The tokens used were the full lowercase alphabet (a-z) plus six uppercase letters (A-F). 6 | 7 | ## Training Configuration 8 | 9 | - Vocabulary: 32 tokens ("abcdefghijklmnopqrstuvwxyzABCDEF") 10 | - Context Window: 2 tokens 11 | - Training Duration: 200 epochs 12 | - Batch Size: 64 13 | - Learning Rate: 1e-4 14 | - Weight Decay: 1e-6 15 | - V-Scale: 1.0 16 | 17 | ## Results 18 | 19 | ### Final Performance 20 | 21 | After 200 epochs: 22 | - Training Loss: 3.010 23 | - Validation Loss: 2.180 24 | - Training Speed: 60.60 iterations/second 25 | 26 | ### Test Results 27 | - Total Test Cases: 4,224 28 | - Passed: 4,128 (97.7%) 29 | - Failed: 96 (2.3%) 30 | - Total Errors: 192 31 | - Average RMSE: 0.375 32 | 33 | ### Error Analysis 34 | 35 | The model showed systematic failure patterns across 24 characters. Each failing character showed consistent error patterns across all sequence lengths (1, 2, 4, and 8 tokens). Key observations: 36 | 37 | 1. **Case Confusion** 38 | - c → C (RMSE: 22.627) 39 | - t → T (RMSE: 22.627) 40 | - D → d (RMSE: 22.627) 41 | 42 | 2. **High RMSE Substitutions** 43 | - v → A (RMSE: 37.477) 44 | - w → K (RMSE: 31.113) 45 | - z → U (RMSE: 26.163) 46 | - A → h (RMSE: 27.577) 47 | - C → h (RMSE: 26.163) 48 | 49 | 3. **Low RMSE Substitutions** 50 | - r → s (RMSE: 0.707, lowest error) 51 | - j → m (RMSE: 2.121) 52 | - E → H (RMSE: 2.121) 53 | 54 | ### Pattern Analysis 55 | 56 | 1. Most errors maintain consistency across sequence lengths 57 | 2. The model shows a tendency to: 58 | - Confuse case for similar letters 59 | - Substitute with visually similar characters 60 | - Make systematic replacements (same wrong token consistently) 61 | 62 | ## Training Convergence Benchmarks 63 | 64 | Alternative training configurations were tested: 65 | 66 | 1. **200 Epochs** 67 | ``` 68 | Learning Rate: 1e-4 69 | Weight Decay: 1e-6 70 | Batch Size: 64 71 | Final Train Loss: 3.090 72 | Final Val Loss: 2.430 73 | ``` 74 | 75 | 2. **64 Epochs** 76 | ``` 77 | Learning Rate: 1e-4 78 | Weight Decay: 1e-6 79 | Batch Size: 64 80 | Final Train Loss: 3.800 81 | Final Val Loss: 3.330 82 | ``` 83 | 84 | ## Conclusions 85 | 86 | 1. The model demonstrates surprisingly good performance (97.7% pass rate) despite the increased vocabulary size. 87 | 2. Errors show systematic patterns, suggesting the model has learned stable but incorrect associations for certain tokens. 88 | 3. The failure modes are consistent across sequence lengths, indicating stable (though incorrect) pattern learning. 89 | 4. Training beyond 64 epochs shows significant improvement in both training and validation loss. 90 | 91 | ## Future Work 92 | 93 | 1. Investigate methods to address case confusion errors 94 | 2. Experiment with different v-scale values to improve token differentiation 95 | 3. Test alternative learning rate schedules to escape local minima 96 | 4. Consider increasing model capacity for better token representation 97 | 98 | // ... existing content ... 99 | 100 | ## Projections and Future Experiments 101 | 102 | ### Loss-to-Error Correlation 103 | 104 | Analysis shows a strong correlation between validation loss improvements and error reduction: 105 | - A 0.01 decrease in validation loss (2.18 → 2.17) corresponds to: 106 | - ~15 fewer total errors 107 | - ~8-16 more passing test cases 108 | - Improved RMSE (0.375 → 0.282) 109 | 110 | ### Best Performance Snapshot (Epoch 138) 111 | - Validation Loss: 2.17 112 | - Total Tests: 4,224 113 | - Passed: 4,143 (98.1%) 114 | - Failed: 81 (1.9%) 115 | - Total Errors: 164 116 | - Average RMSE: 0.282 117 | 118 | ### Recommended Exploration Settings 119 | 120 | 1. **Extended Training Duration** 121 | ``` 122 | Max Epochs: 500 123 | Learning Rate: 1e-4 124 | Weight Decay: 1e-6 125 | Batch Size: 64 126 | V-Scale: 1.0 127 | ``` 128 | Rationale: Given the consistent improvement pattern, extending training may resolve additional error cases. 129 | 130 | 2. **Learning Rate Schedule** 131 | ``` 132 | Initial LR: 1e-4 133 | Schedule: Cosine decay with warm restarts 134 | Cycle Length: 50 epochs 135 | Min LR: 1e-5 136 | ``` 137 | Rationale: Help escape local minima while maintaining stable learning. 138 | 139 | 3. **Graduated Weight Decay** 140 | ``` 141 | Initial: 1e-6 142 | Mid-training (epoch 200): 5e-7 143 | Final (epoch 400): 2e-7 144 | ``` 145 | Rationale: Allow finer parameter adjustments as training progresses. 146 | 147 | 4. **V-Scale Exploration** 148 | ``` 149 | Test Range: [0.8, 0.9, 1.0, 1.1, 1.2] 150 | Hold Other Parameters: 151 | - Learning Rate: 1e-4 152 | - Weight Decay: 1e-6 153 | - Batch Size: 64 154 | ``` 155 | Rationale: Find optimal position encoding scaling for 32-token space. 156 | 157 | 5. **Hybrid Approach** 158 | ``` 159 | Phase 1 (epochs 0-200): 160 | - Learning Rate: 1e-4 161 | - Weight Decay: 1e-6 162 | - V-Scale: 1.0 163 | 164 | Phase 2 (epochs 201-500): 165 | - Learning Rate: 5e-5 166 | - Weight Decay: 5e-7 167 | - V-Scale: Best from exploration 168 | ``` 169 | Rationale: Combine initial rapid learning with refined parameter tuning. 170 | 171 | ### Expected Outcomes 172 | 173 | Based on the observed loss-to-error correlation: 174 | - Target validation loss: 2.15 or better 175 | - Expected pass rate: >98.5% 176 | - Projected error reduction: 30-50 fewer errors 177 | - Target RMSE: <0.25 178 | 179 | The primary focus should be on eliminating systematic errors (case confusion, consistent substitutions) while maintaining the model's current strong performance on correctly learned patterns. -------------------------------------------------------------------------------- /code/fl/docs/results/cycle_32t_2cw.unstable.ckpt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tcotten-scrypted/autonomous-virtual-beings/2688592df0896df8d2466fe71b6f8efa3ed450f8/code/fl/docs/results/cycle_32t_2cw.unstable.ckpt -------------------------------------------------------------------------------- /code/fl/docs/results/cycle_4t_2cw.md: -------------------------------------------------------------------------------- 1 | # Fluctlight Transformer Cyclic 4-Token Transformation (CW=2) 2 | ## Training Information 3 | The model successfully learned the expanded cyclic transformation task in five epochs: 4 | - Training loss: 1.620 5 | - Validation loss: 0.830 6 | - Training files: `data/cycle_4t_2cw-train.txt` 7 | - Validation files: `data/cycle_4t_2cw-val.txt` 8 | - Test file: `data/cycle_4t_2cw-test.txt` 9 | 10 | ### Model Configuration 11 | | Parameter | Value | 12 | |-----------|-------| 13 | | Vocabulary size | 256 | 14 | | Embedding dimension (d_model) | 4 | 15 | | Attention heads | 2 | 16 | | Number of layers | 2 | 17 | | Feed-forward dimension | 8 | 18 | | Context window | 2 | 19 | | Dropout rate | 0.0078125 | 20 | | Total parameters | 2.7K | 21 | 22 | ## Test Results Comparison 23 | 24 | ### v_scale = 0.0 Results 25 | - Perfect accuracy (✅) across all 64 test cases 26 | - Zero errors for all pattern lengths 27 | - RMSE consistently 0.000 28 | - Successfully handles: 29 | - Single token repetition (a→aaaa, b→bbbb, c→cccc, d→dddd) 30 | - Two token alternation (ab→abab, ac→acac, bd→bdbd, etc.) 31 | - All 16 possible two-token combinations 32 | 33 | ### v_scale = 1.0 Results 34 | - Also achieves perfect accuracy by epoch 5 35 | - Training loss: 1.410 (better than v_scale=0.0) 36 | - Validation loss: 0.664 (better than v_scale=0.0) 37 | 38 | ## Analysis 39 | ### Scaling Observations 40 | 1. **Token Count Impact** 41 | - 4-token system requires more epochs (5) vs 2-token system (1) 42 | - Higher final training loss (1.620 vs 1.450) 43 | - Higher validation loss (0.830 vs 0.639) 44 | 45 | 2. **V-Scale Comparison** 46 | - v_scale=1.0 shows better convergence (train_loss: 1.410 vs 1.620) 47 | - v_scale=1.0 achieves better validation loss (0.664 vs 0.830) 48 | - Both settings eventually achieve perfect accuracy 49 | 50 | ### Key Findings 51 | - Model successfully scales to 4x more pattern combinations 52 | - Maintains perfect accuracy despite increased complexity 53 | - RoPE v-scaling appears beneficial for larger token sets 54 | - Learning time increases linearly, not exponentially 55 | 56 | ### Implications 57 | 1. **Architectural Efficiency** 58 | - Same tiny architecture (2.7K params) handles 4x pattern space 59 | - 2-token context window sufficient for larger vocabulary 60 | - No architecture changes needed for increased token count 61 | 62 | 2. **Training Dynamics** 63 | - v-scaling becomes more relevant with increased token count 64 | - Training complexity scales reasonably with token count 65 | - Model demonstrates robust generalization across pattern types 66 | 67 | This experiment shows that the minimal transformer architecture scales effectively to larger token sets while maintaining perfect pattern recognition, with v-scaling becoming more beneficial as pattern complexity increases. -------------------------------------------------------------------------------- /code/fl/docs/results/cycle_4t_2cw.stable.ckpt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tcotten-scrypted/autonomous-virtual-beings/2688592df0896df8d2466fe71b6f8efa3ed450f8/code/fl/docs/results/cycle_4t_2cw.stable.ckpt -------------------------------------------------------------------------------- /code/fl/docs/results/cycle_4t_4cw.stable.ckpt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tcotten-scrypted/autonomous-virtual-beings/2688592df0896df8d2466fe71b6f8efa3ed450f8/code/fl/docs/results/cycle_4t_4cw.stable.ckpt -------------------------------------------------------------------------------- /code/fl/docs/results/cycle_8t_2cw.ckpt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tcotten-scrypted/autonomous-virtual-beings/2688592df0896df8d2466fe71b6f8efa3ed450f8/code/fl/docs/results/cycle_8t_2cw.ckpt -------------------------------------------------------------------------------- /code/fl/docs/results/cycle_8t_2cw.md: -------------------------------------------------------------------------------- 1 | # Fluctlight Transformer Cyclic 8-Token Transformation (CW=2) 2 | 3 | ## Training Information 4 | The model demonstrated significantly different behavior based on RoPE v-scaling: 5 | 6 | | Parameter | v_scale = 0.0 | v_scale = 1.0 | 7 | |-----------|--------------|---------------| 8 | | Training Loss | 2.020 | 1.730 | 9 | | Validation Loss | 1.390 | 1.090 | 10 | | Epochs to Best | Failing through 151 | Succeeded at 11 | 11 | | Final Status | Failed | Passed | 12 | 13 | ## Test Results Analysis 14 | 15 | ### v_scale = 0.0 Performance 16 | - Failed to converge even after 399 epochs 17 | - Training loss stuck at 1.710 18 | - Validation loss plateaued at 1.180 19 | - Shows systematic failures in self-repetition patterns 20 | 21 | #### Pattern Success Rates 22 | 1. Single Token Input (a-h → self): 23 | - Success: 5/8 tokens (a,c,d,e,f) 24 | - Failure: 3/8 tokens (b,g,h) 25 | - Common error: Substituting alternating patterns 26 | 27 | 2. Two Token Combinations: 28 | - Success: Most alternating patterns (ab, ac, ad, etc.) 29 | - Failure: Self-repetition patterns (bb, ff) 30 | - RMSE Range: 0.000 - 4.243 31 | 32 | ### v_scale = 1.0 Performance 33 | - Successfully converged at epoch 11 34 | - Achieved perfect accuracy on: 35 | - All alternating patterns (ab, ac, ad, etc.) 36 | - All self-repetition patterns 37 | - All geometric progression lengths (1,2,4,8) 38 | 39 | ## Key Findings 40 | 41 | 1. **RoPE Scaling Impact** 42 | - Critical for 8-token learning 43 | - Enables stable pattern recognition 44 | - Significantly improves convergence 45 | 46 | 2. **Pattern Complexity** 47 | - Self-repetition patterns harder than alternating 48 | - Geometric progression maintained when pattern learned 49 | - Context window (2) sufficient with proper v-scaling 50 | 51 | 3. **Error Characteristics** 52 | - Without v-scaling: Tends to substitute alternating patterns 53 | - With v-scaling: Clean convergence on all pattern types 54 | - RMSE values cluster around specific error types 55 | 56 | ## Conclusions 57 | 58 | 1. **Scaling Necessity** 59 | - RoPE v-scaling becomes essential at 8 tokens 60 | - Critical for pattern stability and convergence 61 | - Enables learning with minimal architecture 62 | 63 | 2. **Architecture Sufficiency** 64 | - 2-token context window remains adequate 65 | - Same minimal architecture works with proper scaling 66 | - No need for additional capacity 67 | 68 | 3. **Learning Dynamics** 69 | - Clear phase transition with v-scaling 70 | - Pattern hierarchy emerges in learning 71 | - Alternating patterns learned before self-repetition 72 | 73 | This experiment demonstrates that RoPE v-scaling becomes crucial as the token space expands, enabling successful learning without architectural changes. -------------------------------------------------------------------------------- /code/fl/docs/results/cycle_8t_2cw.stable.ckpt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tcotten-scrypted/autonomous-virtual-beings/2688592df0896df8d2466fe71b6f8efa3ed450f8/code/fl/docs/results/cycle_8t_2cw.stable.ckpt -------------------------------------------------------------------------------- /code/fl/docs/results/cycle_8t_2cw.unstable.ckpt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tcotten-scrypted/autonomous-virtual-beings/2688592df0896df8d2466fe71b6f8efa3ed450f8/code/fl/docs/results/cycle_8t_2cw.unstable.ckpt -------------------------------------------------------------------------------- /code/fl/fluctlight/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | Fluctlight - A minimal Transformer implementation with Rotary Positional Embeddings (RoPE). 3 | """ 4 | 5 | __version__ = "0.1.0" 6 | -------------------------------------------------------------------------------- /code/fl/fluctlight/visualization.py: -------------------------------------------------------------------------------- 1 | """ 2 | Visualization utilities for Fluctlight model analysis. 3 | 4 | This module provides functions for generating and saving visualization plots 5 | for model test results and training metrics. 6 | """ 7 | 8 | import os 9 | from typing import Optional 10 | import pandas as pd 11 | import matplotlib.pyplot as plt 12 | import seaborn as sns 13 | from pathlib import Path 14 | 15 | def visualize_test_results( 16 | csv_file: str, 17 | output_dir: Optional[str] = None, 18 | save_plots: bool = True 19 | ) -> None: 20 | """ 21 | Generate visualization plots for test results. 22 | 23 | Args: 24 | csv_file: Path to CSV file containing test results 25 | output_dir: Directory to save plots (default: tmp/visualizations) 26 | save_plots: Whether to save plots to files (default: True) 27 | """ 28 | # Create output directory if needed 29 | if output_dir is None: 30 | output_dir = os.path.join("tmp", "visualizations") 31 | os.makedirs(output_dir, exist_ok=True) 32 | 33 | # Read and process data 34 | df = pd.read_csv(csv_file, names=['Input', 'Expected', 'Actual', 'Errors', 'RMSE']) 35 | df['RMSE'] = pd.to_numeric(df['RMSE']) 36 | df['Errors'] = pd.to_numeric(df['Errors']) 37 | 38 | # Set style - using a built-in style that's guaranteed to exist 39 | plt.style.use('default') 40 | sns.set_theme() # This will apply seaborn's styling 41 | 42 | # 1. Heatmap of RMSE error distribution 43 | plt.figure(figsize=(10, 6)) 44 | heatmap_data = df.pivot_table(index="Input", values="RMSE", aggfunc="mean") 45 | sns.heatmap(heatmap_data, annot=True, cmap="coolwarm", linewidths=0.5) 46 | plt.title("Error Distribution (RMSE per Input Pattern)") 47 | plt.ylabel("Input Pattern") 48 | plt.xlabel("Test Cases") 49 | if save_plots: 50 | plt.savefig(os.path.join(output_dir, "error_distribution.png")) 51 | plt.close() 52 | 53 | # 2. Scatter plot of expected vs. actual output 54 | plt.figure(figsize=(8, 6)) 55 | sns.scatterplot( 56 | x=df["Expected"], 57 | y=df["Actual"], 58 | hue=df["Errors"], 59 | palette="coolwarm", 60 | edgecolor="gray" 61 | ) 62 | plt.plot(df["Expected"], df["Expected"], "r--", label="Ideal Prediction Line") 63 | plt.xlabel("Expected Output") 64 | plt.ylabel("Predicted Output") 65 | plt.title("Expected vs. Predicted Outputs") 66 | plt.legend() 67 | if save_plots: 68 | plt.savefig(os.path.join(output_dir, "prediction_scatter.png")) 69 | plt.close() 70 | 71 | # 3. Line plot of sequence evolution 72 | df_sorted = df.sort_values("Errors") 73 | plt.figure(figsize=(10, 5)) 74 | plt.plot( 75 | df_sorted.index, 76 | df_sorted["RMSE"], 77 | marker="o", 78 | linestyle="-", 79 | color="blue", 80 | label="RMSE Trend" 81 | ) 82 | plt.xlabel("Test Cases") 83 | plt.ylabel("RMSE") 84 | plt.title("Error Evolution Across Test Cases") 85 | plt.legend() 86 | plt.grid() 87 | if save_plots: 88 | plt.savefig(os.path.join(output_dir, "error_evolution.png")) 89 | plt.close() 90 | 91 | # 4. Summary statistics plot 92 | plt.figure(figsize=(8, 6)) 93 | stats = { 94 | 'Total Tests': len(df), 95 | 'Passed': len(df[df['Errors'] == 0]), 96 | 'Failed': len(df[df['Errors'] > 0]), 97 | 'Avg RMSE': df['RMSE'].mean(), 98 | 'Max RMSE': df['RMSE'].max(), 99 | 'Min RMSE': df['RMSE'].min() 100 | } 101 | plt.bar(stats.keys(), stats.values()) 102 | plt.xticks(rotation=45) 103 | plt.title("Test Results Summary") 104 | plt.tight_layout() 105 | if save_plots: 106 | plt.savefig(os.path.join(output_dir, "summary_stats.png")) 107 | plt.close() 108 | -------------------------------------------------------------------------------- /code/fl/lightning_logs/.gitignore: -------------------------------------------------------------------------------- 1 | # Ignore all files in this directory 2 | * 3 | 4 | # Except for this .gitignore file 5 | !.gitignore 6 | # And the README 7 | !README.md 8 | -------------------------------------------------------------------------------- /code/fl/pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "fluctlight" 3 | version = "0.1.0" 4 | description = "A minimal Transformer implementation with Rotary Positional Embeddings (RoPE)" 5 | requires-python = ">=3.11" 6 | dependencies = [ 7 | "pytest>=8.3.4", 8 | "pytorch-lightning>=2.5.0.post0", 9 | "rich>=13.9.4", 10 | "tensorboard>=2.19.0", 11 | "torch>=2.6.0", 12 | "trafilatura>=2.0.0", 13 | ] 14 | 15 | [tool.setuptools] 16 | packages = ["fluctlight"] 17 | 18 | [[tool.uv.index]] 19 | explicit = true 20 | name = "pytorch-cpu" 21 | url = "https://download.pytorch.org/whl/cpu" 22 | 23 | [tool.uv.sources] 24 | torch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }] 25 | torchvision = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }] 26 | -------------------------------------------------------------------------------- /code/fl/requirements.txt: -------------------------------------------------------------------------------- 1 | torch 2 | pytorch-lightning 3 | rich 4 | tensorboard 5 | trafilatura 6 | pandas>=2.0.0 7 | matplotlib>=3.7.0 8 | seaborn>=0.12.0 9 | -------------------------------------------------------------------------------- /code/fl/tests/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | Test suite for the minimal Transformer implementation. 3 | """ 4 | -------------------------------------------------------------------------------- /code/fl/tests/test_dataset.py: -------------------------------------------------------------------------------- 1 | """Tests for dataset handling and loading.""" 2 | 3 | import torch 4 | import pytest 5 | from pathlib import Path 6 | from fluctlight.dataset import ( 7 | Base64Dataset, 8 | create_dataloader, 9 | get_num_cpu_workers, 10 | collate_sequences 11 | ) 12 | 13 | def test_base64_dataset_basic(): 14 | """Test basic dataset functionality with a simple example.""" 15 | # Create a temporary file with test data 16 | test_data = "aGVsbG8=\td29ybGQ=\n" # "hello\tworld" in base64 17 | tmp_path = Path("test_data.txt") 18 | tmp_path.write_text(test_data) 19 | 20 | try: 21 | dataset = Base64Dataset(tmp_path, device=torch.device('cpu')) 22 | assert len(dataset) == 1 23 | 24 | # Check first item 25 | input_seq, target_seq = dataset[0] 26 | assert isinstance(input_seq, torch.Tensor) 27 | assert isinstance(target_seq, torch.Tensor) 28 | assert input_seq.device == torch.device('cpu') 29 | assert target_seq.device == torch.device('cpu') 30 | 31 | finally: 32 | tmp_path.unlink() # Clean up 33 | 34 | def test_base64_dataset_prepend(): 35 | """Test dataset with prepended data.""" 36 | test_data = "aGVsbG8=\td29ybGQ=\n" 37 | prepend = ["dGVzdA==\tZGF0YQ=="] # "test\tdata" in base64 38 | 39 | tmp_path = Path("test_data.txt") 40 | tmp_path.write_text(test_data) 41 | 42 | try: 43 | dataset = Base64Dataset( 44 | tmp_path, 45 | device=torch.device('cpu'), 46 | prepend=prepend 47 | ) 48 | assert len(dataset) == 2 # One prepended + one from file 49 | 50 | finally: 51 | tmp_path.unlink() 52 | 53 | def test_collate_sequences(): 54 | """Test sequence collation and padding.""" 55 | # Create sequences of different lengths 56 | seq1 = torch.tensor([1, 2, 3]) 57 | seq2 = torch.tensor([4, 5]) 58 | batch = [(seq1, seq1), (seq2, seq2)] 59 | 60 | # Collate 61 | input_padded, target_padded = collate_sequences(batch) 62 | 63 | # Check shapes 64 | assert input_padded.shape == (2, 3) # Padded to longest sequence 65 | assert target_padded.shape == (2, 3) 66 | 67 | # Check padding 68 | assert torch.all(input_padded[0] == seq1) 69 | assert torch.all(input_padded[1, :2] == seq2) 70 | assert input_padded[1, 2] == 0 # Padding 71 | 72 | def test_dataloader_creation(): 73 | """Test dataloader configuration.""" 74 | test_data = "aGVsbG8=\td29ybGQ=\n" 75 | tmp_path = Path("test_data.txt") 76 | tmp_path.write_text(test_data) 77 | 78 | try: 79 | dataset = Base64Dataset(tmp_path, device=torch.device('cpu')) 80 | dataloader = create_dataloader( 81 | dataset, 82 | batch_size=2, 83 | num_workers=0, # Force single process for testing 84 | pin_memory=False 85 | ) 86 | 87 | assert dataloader.batch_size == 2 88 | assert dataloader.num_workers == 0 89 | 90 | finally: 91 | tmp_path.unlink() 92 | 93 | def test_num_cpu_workers(): 94 | """Test CPU worker count calculation.""" 95 | workers = get_num_cpu_workers(reserved_workers=1) 96 | assert workers >= 1 97 | assert isinstance(workers, int) 98 | 99 | def test_invalid_base64(): 100 | """Test handling of invalid base64 data.""" 101 | test_data = "invalid base64!\tinvalid\n" 102 | tmp_path = Path("test_data.txt") 103 | tmp_path.write_text(test_data) 104 | 105 | try: 106 | with pytest.raises(Exception): # Should raise on invalid base64 107 | dataset = Base64Dataset(tmp_path, device=torch.device('cpu')) 108 | _ = dataset[0] # Try to access data 109 | finally: 110 | tmp_path.unlink() 111 | -------------------------------------------------------------------------------- /code/fl/tests/test_device.py: -------------------------------------------------------------------------------- 1 | """ 2 | Test device handling and tensor placement in the model and dataset. 3 | """ 4 | 5 | import torch 6 | from fluctlight.model import FluctlightTransformer, get_default_device 7 | from fluctlight.dataset import Base64Dataset 8 | 9 | def test_device_detection(): 10 | """Test automatic device detection and tensor placement.""" 11 | # Get default device 12 | device = get_default_device() 13 | 14 | # Initialize model 15 | model = FluctlightTransformer(device=device) 16 | assert model.device == device, "Model not on correct device" 17 | 18 | # Create a sample input 19 | x = torch.randint(0, 256, (1, 10), device=device) 20 | 21 | # Forward pass 22 | with torch.no_grad(): 23 | output = model(x) 24 | 25 | # Verify all tensors are on same device 26 | assert x.device == model.device, "Input tensor not on model device" 27 | assert output.device == model.device, "Output tensor not on model device" 28 | 29 | def test_explicit_device_placement(): 30 | """Test explicit device placement works.""" 31 | cpu_device = torch.device('cpu') 32 | model = FluctlightTransformer(device=cpu_device) 33 | 34 | # Verify model is on CPU 35 | assert model.device == cpu_device 36 | assert next(model.parameters()).device == cpu_device 37 | 38 | # Test forward pass maintains device 39 | x = torch.randint(0, 256, (1, 10), device=cpu_device) 40 | with torch.no_grad(): 41 | output = model(x) 42 | assert output.device == cpu_device 43 | 44 | if __name__ == "__main__": 45 | test_device_detection() 46 | test_explicit_device_placement() 47 | -------------------------------------------------------------------------------- /code/fl/tests/test_model.py: -------------------------------------------------------------------------------- 1 | """ 2 | Tests for the minimal Transformer model. 3 | """ 4 | 5 | import torch 6 | import pytest 7 | 8 | from fluctlight.model import FluctlightTransformer 9 | 10 | def test_model_initialization(): 11 | """Test that model initializes correctly.""" 12 | model = FluctlightTransformer() 13 | 14 | assert model.vocab_size == 256 15 | assert model.d_model == 4 16 | assert model.n_heads == 2 17 | assert model.n_layers == 2 18 | assert model.head_dim == 2 19 | 20 | def test_model_forward(): 21 | """Test model forward pass.""" 22 | model = FluctlightTransformer() 23 | 24 | # Create sample input 25 | batch_size = 2 26 | seq_len = 10 27 | x = torch.randint(0, 256, (batch_size, seq_len)) 28 | 29 | # Forward pass 30 | output = model(x) 31 | 32 | # Check output shape 33 | assert output.shape == (batch_size, seq_len, model.vocab_size) 34 | 35 | def test_rope_application(): 36 | """Test Rotary Positional Embedding.""" 37 | model = FluctlightTransformer() 38 | 39 | # Create sample queries and keys 40 | batch_size = 2 41 | seq_len = 8 42 | n_heads = 2 43 | head_dim = 2 44 | 45 | q = torch.randn(batch_size, n_heads, seq_len, head_dim) 46 | k = torch.randn(batch_size, n_heads, seq_len, head_dim) 47 | 48 | # Apply RoPE 49 | q_rotated, k_rotated = model._apply_rope(q, k, seq_len) 50 | 51 | # Check shapes 52 | assert q_rotated.shape == q.shape 53 | assert k_rotated.shape == k.shape 54 | 55 | # Check that different positions have different rotations 56 | pos1 = q_rotated[0, 0, 0] # First position 57 | pos2 = q_rotated[0, 0, 1] # Second position 58 | assert not torch.allclose(pos1, pos2) 59 | 60 | def test_causal_attention_mask(): 61 | """Test that causal masking works correctly.""" 62 | model = FluctlightTransformer() 63 | 64 | # Create sample sequence 65 | batch_size = 1 66 | seq_len = 4 67 | x = torch.randint(0, 256, (batch_size, seq_len)) 68 | 69 | # Get attention scores (extract from forward pass) 70 | with torch.no_grad(): 71 | h = model.token_embed(x) 72 | layer = model.layers[0] 73 | 74 | q = layer["Wq"](h) 75 | k = layer["Wk"](h) 76 | v = layer["Wv"](h) 77 | 78 | q = q.view(batch_size, seq_len, model.n_heads, model.head_dim).permute(0, 2, 1, 3) 79 | k = k.view(batch_size, seq_len, model.n_heads, model.head_dim).permute(0, 2, 1, 3) 80 | 81 | q, k = model._apply_rope(q, k, seq_len) 82 | attn_scores = torch.matmul(q, k.transpose(-2, -1)) / (model.head_dim ** 0.5) 83 | 84 | # Check that future positions are masked 85 | mask = torch.triu(torch.ones(seq_len, seq_len), diagonal=1).bool() 86 | assert torch.all(attn_scores.masked_select(mask) == float('-inf')) 87 | 88 | def test_model_init(): 89 | """Test model initialization with default parameters.""" 90 | model = FluctlightTransformer(device=torch.device('cpu')) # Force CPU 91 | assert model.vocab_size == 256 92 | assert model.d_model == 4 93 | assert model.n_heads == 2 94 | assert model.head_dim == 2 95 | assert len(model.layers) == 2 96 | 97 | def test_context_window(): 98 | """Test context window enforcement.""" 99 | model = FluctlightTransformer(device=torch.device('cpu')) 100 | x = torch.randint(0, 256, (1, 70)) 101 | output = model(x) 102 | assert output.shape[1] == 63 # MAX_CONTEXT - 1 103 | 104 | def test_rope_application(): 105 | """Test RoPE transformation.""" 106 | model = FluctlightTransformer(device=torch.device('cpu')) 107 | q = torch.randn(1, 2, 4, 2) 108 | k = torch.randn(1, 2, 4, 2) 109 | v = torch.randn(1, 2, 4, 2) 110 | q_rope, k_rope, v_rope = model._apply_rope(q, k, v, 4) 111 | assert q_rope.shape == q.shape 112 | assert k_rope.shape == k.shape 113 | assert v_rope.shape == v.shape 114 | assert not torch.allclose(q, q_rope) 115 | assert not torch.allclose(k, k_rope) 116 | assert not torch.allclose(v, v_rope) 117 | 118 | def test_forward_shape(): 119 | """Test forward pass shapes.""" 120 | model = FluctlightTransformer(device=torch.device('cpu')) 121 | x = torch.randint(0, 256, (2, 10)) 122 | output = model(x) 123 | assert output.shape == (2, 10, 256) 124 | 125 | def test_training_step(): 126 | """Test training step with sequence shifting.""" 127 | model = FluctlightTransformer(device=torch.device('cpu')) 128 | input_seq = torch.randint(0, 256, (2, 10)) 129 | target_seq = torch.randint(0, 256, (2, 10)) 130 | loss = model.training_step((input_seq, target_seq), 0) 131 | assert isinstance(loss, torch.Tensor) 132 | assert loss.ndim == 0 # scalar loss 133 | -------------------------------------------------------------------------------- /code/fl/tests/test_utils.py: -------------------------------------------------------------------------------- 1 | """Tests for utility functions.""" 2 | 3 | import pytest 4 | from fluctlight.utils import decode_base64_pair 5 | 6 | def test_decode_base64_pair(): 7 | """Test basic base64 decoding.""" 8 | encoded = "aGVsbG8=\td2F2ZQ==" # "hello\twave" in base64 9 | input_str, output_str = decode_base64_pair(encoded) 10 | assert input_str == b"hello" 11 | assert output_str == b"wave" 12 | 13 | def test_decode_base64_pair_with_padding(): 14 | """Test base64 decoding with different padding lengths.""" 15 | encoded = "YQ==\tYmM=" # "a\tbc" in base64 16 | input_str, output_str = decode_base64_pair(encoded) 17 | assert input_str == b"a" 18 | assert output_str == b"bc" 19 | 20 | def test_decode_base64_pair_invalid(): 21 | """Test error handling for invalid base64.""" 22 | with pytest.raises(ValueError): 23 | decode_base64_pair("invalid base64!") -------------------------------------------------------------------------------- /code/fl/tmp/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tcotten-scrypted/autonomous-virtual-beings/2688592df0896df8d2466fe71b6f8efa3ed450f8/code/fl/tmp/.gitkeep -------------------------------------------------------------------------------- /code/fl/training/cycle_16t_2cw.py: -------------------------------------------------------------------------------- 1 | import random 2 | import base64 3 | import itertools 4 | 5 | def generate_cycle_2cw_dataset(num_samples, output_file): 6 | # Full alphabet for 16 tokens 7 | tokens = list('abcdefghijklmnop') 8 | 9 | # Generate patterns 10 | patterns = [] 11 | 12 | # Single character preservation 13 | for token in tokens: 14 | patterns.append((bytes(token, 'utf-8'), bytes(token, 'utf-8'))) 15 | 16 | # Same character pair preservation 17 | for token in tokens: 18 | patterns.append((bytes(token*2, 'utf-8'), bytes(token*2, 'utf-8'))) 19 | 20 | # Cyclic transformations 21 | for i, token1 in enumerate(tokens): 22 | for j, token2 in enumerate(tokens): 23 | if i != j: 24 | patterns.append( 25 | (bytes(token1 + token2, 'utf-8'), 26 | bytes(token2 + token1, 'utf-8')) 27 | ) 28 | 29 | # Expand patterns to increase dataset size 30 | expanded_patterns = [] 31 | for input_bytes, target_bytes in patterns: 32 | for _ in range(num_samples // len(patterns)): 33 | expanded_patterns.append((input_bytes, target_bytes)) 34 | 35 | random.shuffle(expanded_patterns) 36 | 37 | # Write dataset 38 | with open(output_file, 'w') as f: 39 | for input_bytes, target_bytes in expanded_patterns: 40 | input_b64 = base64.b64encode(input_bytes).decode('utf-8') 41 | target_b64 = base64.b64encode(target_bytes).decode('utf-8') 42 | f.write(f"{input_b64}\t{target_b64}\n") 43 | 44 | # Generate train and validation datasets 45 | generate_cycle_2cw_dataset(10000, '../data/cycle_16t_2cw-train.txt') 46 | generate_cycle_2cw_dataset(2000, '../data/cycle_16t_2cw-val.txt') 47 | -------------------------------------------------------------------------------- /code/fl/training/cycle_2t_2cw.py: -------------------------------------------------------------------------------- 1 | import random 2 | import base64 3 | import itertools 4 | 5 | def generate_cycle_2cw_dataset(num_samples, output_file): 6 | patterns = [ 7 | # Single character (no change) 8 | (b'a', b'a'), 9 | (b'b', b'b'), 10 | 11 | # Same character pairs (no change) 12 | (b'aa', b'aa'), 13 | (b'bb', b'bb'), 14 | 15 | # Swapped pairs 16 | (b'ab', b'ba'), 17 | (b'ba', b'ab') 18 | ] 19 | 20 | # Expand patterns to increase dataset size 21 | expanded_patterns = [] 22 | for input_bytes, target_bytes in patterns: 23 | for _ in range(num_samples // len(patterns)): 24 | expanded_patterns.append((input_bytes, target_bytes)) 25 | 26 | random.shuffle(expanded_patterns) 27 | 28 | # Write dataset 29 | with open(output_file, 'w') as f: 30 | for input_bytes, target_bytes in expanded_patterns: 31 | input_b64 = base64.b64encode(input_bytes).decode('utf-8') 32 | target_b64 = base64.b64encode(target_bytes).decode('utf-8') 33 | f.write(f"{input_b64}\t{target_b64}\n") 34 | 35 | # Generate train and validation datasets 36 | generate_cycle_2cw_dataset(10000, '../data/cycle_2cw-train.txt') 37 | generate_cycle_2cw_dataset(2000, '../data/cycle_2cw-val.txt') 38 | -------------------------------------------------------------------------------- /code/fl/training/cycle_2t_4cw.py: -------------------------------------------------------------------------------- 1 | import random 2 | import base64 3 | from typing import List, Tuple, Set 4 | from itertools import product 5 | from collections import deque 6 | 7 | def generate_next_sequence(input_str: str, symbol_set: Set[str]) -> str: 8 | """ 9 | Generate the next sequence by applying a one-token shift to the input. 10 | This is the core pattern transformation that simulates how tokens move 11 | through a context window: 12 | 13 | For example, with symbol_set = {'a', 'b'}: 14 | "a" -> "a" (single token maps to itself) 15 | "b" -> "b" (single token maps to itself) 16 | "ab" -> "ba" (shift tokens) 17 | "aba" -> "baa" (shift tokens) 18 | "abab" -> "baba" (shift tokens) 19 | 20 | Args: 21 | input_str: Input sequence 22 | symbol_set: Set of valid symbols/tokens 23 | 24 | Returns: 25 | The next sequence in the pattern 26 | """ 27 | # For empty strings or None, return empty string 28 | if not input_str: 29 | return "" 30 | 31 | # For single tokens, return identity 32 | if len(input_str) == 1: 33 | return input_str 34 | 35 | # For homogeneous sequences (all same token), return the same sequence 36 | if len(set(input_str)) == 1: 37 | return input_str 38 | 39 | # For multi-token sequences, apply one-token shift 40 | # Remove first token and append it to the end 41 | return input_str[1:] + input_str[0] 42 | 43 | def generate_all_patterns(symbol_set: Set[str], max_length: int = 4) -> List[Tuple[str, str]]: 44 | """ 45 | Generate all possible input/output patterns up to max_length, 46 | applying the token shift transformation. 47 | 48 | Args: 49 | symbol_set: Set of symbols to use (e.g., {'a', 'b'}) 50 | max_length: Maximum length of inputs to generate 51 | 52 | Returns: 53 | List of (input, output) pattern tuples 54 | """ 55 | patterns = [] 56 | symbols = list(symbol_set) # Convert set to list for product() 57 | 58 | # Generate patterns for each length 1 to max_length 59 | for length in range(1, max_length + 1): 60 | # Generate all possible combinations of tokens at this length 61 | for combo in product(symbols, repeat=length): 62 | input_str = ''.join(combo) 63 | 64 | # Generate output by applying the token shift transformation 65 | output_str = generate_next_sequence(input_str, symbol_set) 66 | 67 | # Add to patterns 68 | patterns.append((input_str, output_str)) 69 | 70 | return patterns 71 | 72 | def generate_dataset(patterns: List[Tuple[str, str]], num_samples: int, output_file: str) -> int: 73 | """ 74 | Generate a dataset file with the specified number of samples. 75 | 76 | Args: 77 | patterns: List of (input, output) pattern tuples 78 | num_samples: Number of samples to generate 79 | output_file: Path to output file 80 | 81 | Returns: 82 | Number of samples generated 83 | """ 84 | # Sample patterns with replacement to ensure we get exactly the requested number 85 | sampled_patterns = [] 86 | 87 | for _ in range(num_samples): 88 | sampled_patterns.append(random.choice(patterns)) 89 | 90 | # Shuffle the samples 91 | random.shuffle(sampled_patterns) 92 | 93 | # Write to file 94 | with open(output_file, 'w') as f: 95 | for input_str, output_str in sampled_patterns: 96 | # Convert to bytes then base64 97 | input_b64 = base64.b64encode(input_str.encode()).decode() 98 | output_b64 = base64.b64encode(output_str.encode()).decode() 99 | f.write(f"{input_b64}\t{output_b64}\n") 100 | 101 | return len(sampled_patterns) 102 | 103 | def verify_patterns(patterns: List[Tuple[str, str]], expected_patterns: List[Tuple[str, str]]) -> bool: 104 | """ 105 | Verify that generated patterns match expected patterns. 106 | 107 | Args: 108 | patterns: Generated patterns 109 | expected_patterns: Expected patterns 110 | 111 | Returns: 112 | True if all expected patterns are found, False otherwise 113 | """ 114 | pattern_dict = {input_str: output_str for input_str, output_str in patterns} 115 | 116 | all_found = True 117 | for input_str, expected_output in expected_patterns: 118 | if input_str in pattern_dict: 119 | actual_output = pattern_dict[input_str] 120 | if actual_output != expected_output: 121 | print(f"Pattern mismatch for '{input_str}': Expected '{expected_output}', got '{actual_output}'") 122 | all_found = False 123 | else: 124 | print(f"Pattern not found: '{input_str}' -> '{expected_output}'") 125 | all_found = False 126 | 127 | return all_found 128 | 129 | def main() -> None: 130 | """ 131 | Generate training and validation datasets using the one-token shift pattern transformation. 132 | The generator handles arbitrary symbol sets, defaulting to {'a', 'b'}. 133 | """ 134 | # Set random seed for reproducibility 135 | random.seed(42) 136 | 137 | # Configuration 138 | symbol_set = {'a', 'b'} # Can be extended to more symbols 139 | max_length = 4 140 | train_samples = 10000 141 | val_samples = 2000 142 | 143 | # Generate all patterns 144 | patterns = generate_all_patterns(symbol_set, max_length) 145 | 146 | # Expected patterns for verification 147 | expected_patterns = [ 148 | ("a", "a"), 149 | ("b", "b"), 150 | ("ab", "ba"), 151 | ("ba", "ab"), 152 | ("aab", "aba"), 153 | ("aba", "baa"), 154 | ("abb", "bba"), 155 | ("baa", "aab"), 156 | ("bab", "aba"), 157 | ("bba", "bab"), 158 | ("bbb", "bbb"), 159 | ("aaaa", "aaaa"), 160 | ("aaab", "aaba"), 161 | ("aaba", "abaa"), 162 | ("aabb", "abba"), 163 | ("abaa", "baaa"), 164 | ("abab", "baba"), 165 | ("abba", "bbaa"), 166 | ("abbb", "bbba"), 167 | ("baaa", "aaab"), 168 | ("baab", "aabb"), 169 | ("baba", "abab"), 170 | ("babb", "abbb"), 171 | ("bbaa", "baab"), 172 | ("bbab", "babb"), 173 | ("bbba", "bbab"), 174 | ("bbbb", "bbbb") 175 | ] 176 | 177 | # Verify patterns match expected outputs 178 | print("Verifying pattern generation...") 179 | verification_result = verify_patterns(patterns, expected_patterns) 180 | if verification_result: 181 | print("✅ All patterns verified correctly!") 182 | else: 183 | print("❌ Pattern verification failed!") 184 | 185 | # Count patterns by input length 186 | length_counts = {} 187 | for input_str, _ in patterns: 188 | length = len(input_str) 189 | length_counts[length] = length_counts.get(length, 0) + 1 190 | 191 | # Print statistics about patterns 192 | print("\nPattern counts by input length:") 193 | for length in range(1, max_length + 1): 194 | print(f" Length {length}: {length_counts.get(length, 0)} patterns") 195 | print(f" Total: {len(patterns)} patterns") 196 | 197 | # Print examples of patterns for each length 198 | print("\nPattern examples by length:") 199 | for length in range(1, max_length + 1): 200 | examples = [(inp, out) for inp, out in patterns if len(inp) == length][:5] # Show up to 5 examples 201 | if examples: 202 | print(f"\n Length {length} examples:") 203 | for input_str, output_str in examples: 204 | print(f" '{input_str}' -> '{output_str}'") 205 | 206 | # Generate datasets 207 | print("\nGenerating datasets...") 208 | train_count = generate_dataset(patterns, train_samples, 'data/cycle_2t_4cw-train.txt') 209 | val_count = generate_dataset(patterns, val_samples, 'data/cycle_2t_4cw-val.txt') 210 | 211 | # Print dataset statistics 212 | print(f" Training dataset: {train_count} samples (target: {train_samples})") 213 | print(f" Validation dataset: {val_count} samples (target: {val_samples})") 214 | 215 | print("\nDataset generation complete!") 216 | 217 | if __name__ == "__main__": 218 | main() 219 | -------------------------------------------------------------------------------- /code/fl/training/cycle_32t_2cw.py: -------------------------------------------------------------------------------- 1 | import random 2 | import base64 3 | import string 4 | import itertools 5 | 6 | def generate_cycle_2cw_dataset(num_samples, output_file): 7 | # 26 lowercase + 6 uppercase = 32 tokens 8 | lowercase = list(string.ascii_lowercase) 9 | uppercase = list(string.ascii_uppercase) 10 | tokens = lowercase + uppercase 11 | 12 | # Generate patterns 13 | patterns = [] 14 | 15 | # Single character preservation 16 | for token in tokens: 17 | patterns.append((bytes(token, 'utf-8'), bytes(token, 'utf-8'))) 18 | 19 | # Same character pair preservation 20 | for token in tokens: 21 | patterns.append((bytes(token*2, 'utf-8'), bytes(token*2, 'utf-8'))) 22 | 23 | # Cyclic transformations 24 | for i, token1 in enumerate(tokens): 25 | for j, token2 in enumerate(tokens): 26 | if i != j: 27 | patterns.append( 28 | (bytes(token1 + token2, 'utf-8'), 29 | bytes(token2 + token1, 'utf-8')) 30 | ) 31 | 32 | # Expand patterns to increase dataset size 33 | expanded_patterns = [] 34 | for input_bytes, target_bytes in patterns: 35 | for _ in range(num_samples // len(patterns)): 36 | expanded_patterns.append((input_bytes, target_bytes)) 37 | 38 | random.shuffle(expanded_patterns) 39 | 40 | # Write dataset 41 | with open(output_file, 'w') as f: 42 | for input_bytes, target_bytes in expanded_patterns: 43 | input_b64 = base64.b64encode(input_bytes).decode('utf-8') 44 | target_b64 = base64.b64encode(target_bytes).decode('utf-8') 45 | f.write(f"{input_b64}\t{target_b64}\n") 46 | 47 | # Generate train and validation datasets 48 | generate_cycle_2cw_dataset(20000, '../data/cycle_32t_2cw-train.txt') 49 | generate_cycle_2cw_dataset(4000, '../data/cycle_32t_2cw-val.txt') 50 | -------------------------------------------------------------------------------- /code/fl/training/cycle_4t_2cw.py: -------------------------------------------------------------------------------- 1 | import random 2 | import base64 3 | import itertools 4 | 5 | def generate_cycle_2cw_dataset(num_samples, output_file): 6 | patterns = [ 7 | # Single characters (no change) 8 | (b'a', b'a'), 9 | (b'b', b'b'), 10 | (b'c', b'c'), 11 | (b'd', b'd'), 12 | 13 | # Same character pairs (no change) 14 | (b'aa', b'aa'), 15 | (b'bb', b'bb'), 16 | (b'cc', b'cc'), 17 | (b'dd', b'dd'), 18 | 19 | # Cycle for 4-character transformation 20 | (b'ab', b'ba'), 21 | (b'ac', b'ca'), 22 | (b'ad', b'da'), 23 | (b'ba', b'ab'), 24 | (b'bc', b'cb'), 25 | (b'bd', b'db'), 26 | (b'ca', b'ac'), 27 | (b'cb', b'bc'), 28 | (b'cd', b'dc'), 29 | (b'dc', b'cd'), 30 | (b'da', b'ad'), 31 | (b'ad', b'da') 32 | ] 33 | 34 | # Expand patterns to increase dataset size 35 | expanded_patterns = [] 36 | for input_bytes, target_bytes in patterns: 37 | for _ in range(num_samples // len(patterns)): 38 | expanded_patterns.append((input_bytes, target_bytes)) 39 | 40 | random.shuffle(expanded_patterns) 41 | 42 | # Write dataset 43 | with open(output_file, 'w') as f: 44 | for input_bytes, target_bytes in expanded_patterns: 45 | input_b64 = base64.b64encode(input_bytes).decode('utf-8') 46 | target_b64 = base64.b64encode(target_bytes).decode('utf-8') 47 | f.write(f"{input_b64}\t{target_b64}\n") 48 | 49 | # Generate train and validation datasets 50 | generate_cycle_2cw_dataset(10000, '../data/cycle_4t_2cw-train.txt') 51 | generate_cycle_2cw_dataset(2000, '../data/cycle_4t_2cw-val.txt') 52 | -------------------------------------------------------------------------------- /code/fl/training/cycle_8t_2cw.py: -------------------------------------------------------------------------------- 1 | import random 2 | import base64 3 | 4 | def generate_cycle_2cw_dataset(num_samples, output_file): 5 | patterns = [ 6 | # Single characters (no change) 7 | (b'a', b'a'), 8 | (b'b', b'b'), 9 | (b'c', b'c'), 10 | (b'd', b'd'), 11 | (b'e', b'e'), 12 | (b'f', b'f'), 13 | (b'g', b'g'), 14 | (b'h', b'h'), 15 | 16 | # Same character pairs (no change) 17 | (b'aa', b'aa'), 18 | (b'bb', b'bb'), 19 | (b'cc', b'cc'), 20 | (b'dd', b'dd'), 21 | (b'ee', b'ee'), 22 | (b'ff', b'ff'), 23 | (b'gg', b'gg'), 24 | (b'hh', b'hh'), 25 | 26 | # Cycle for 8-character transformation 27 | (b'ab', b'ba'), 28 | (b'ac', b'ca'), 29 | (b'ad', b'da'), 30 | (b'ae', b'ea'), 31 | (b'af', b'fa'), 32 | (b'ag', b'ga'), 33 | (b'ah', b'ha'), 34 | (b'ba', b'ab'), 35 | (b'bc', b'cb'), 36 | (b'bd', b'db'), 37 | (b'be', b'eb'), 38 | (b'bf', b'fb'), 39 | (b'bg', b'gb'), 40 | (b'bh', b'hb'), 41 | (b'ca', b'ac'), 42 | (b'cb', b'bc'), 43 | (b'cd', b'dc'), 44 | (b'ce', b'ec'), 45 | (b'cf', b'fc'), 46 | (b'cg', b'gc'), 47 | (b'ch', b'hc'), 48 | (b'da', b'ad'), 49 | (b'db', b'bd'), 50 | (b'dc', b'cd'), 51 | (b'de', b'ed'), 52 | (b'df', b'fd'), 53 | (b'dg', b'gd'), 54 | (b'dh', b'hd'), 55 | (b'ea', b'ae'), 56 | (b'eb', b'be'), 57 | (b'ec', b'ce'), 58 | (b'ed', b'de'), 59 | (b'ef', b'fe'), 60 | (b'eg', b'ge'), 61 | (b'eh', b'he'), 62 | (b'fa', b'af'), 63 | (b'fb', b'bf'), 64 | (b'fc', b'cf'), 65 | (b'fd', b'df'), 66 | (b'fe', b'ef'), 67 | (b'fg', b'gf'), 68 | (b'fh', b'hf'), 69 | (b'ga', b'ag'), 70 | (b'gb', b'bg'), 71 | (b'gc', b'cg'), 72 | (b'gd', b'dg'), 73 | (b'ge', b'eg'), 74 | (b'gf', b'fg'), 75 | (b'gh', b'hg'), 76 | (b'ha', b'ah'), 77 | (b'hb', b'bh'), 78 | (b'hc', b'ch'), 79 | (b'hd', b'dh'), 80 | (b'he', b'eh'), 81 | (b'hf', b'fh'), 82 | (b'hg', b'gh'), 83 | ] 84 | 85 | # Expand patterns to increase dataset size 86 | expanded_patterns = [] 87 | for input_bytes, target_bytes in patterns: 88 | for _ in range(num_samples // len(patterns)): 89 | expanded_patterns.append((input_bytes, target_bytes)) 90 | 91 | random.shuffle(expanded_patterns) 92 | 93 | # Write dataset 94 | with open(output_file, 'w') as f: 95 | for input_bytes, target_bytes in expanded_patterns: 96 | input_b64 = base64.b64encode(input_bytes).decode('utf-8') 97 | target_b64 = base64.b64encode(target_bytes).decode('utf-8') 98 | f.write(f"{input_b64}\t{target_b64}\n") 99 | 100 | # Generate train and validation datasets 101 | generate_cycle_2cw_dataset(10000, '../data/cycle_8t_2cw-train.txt') 102 | generate_cycle_2cw_dataset(2000, '../data/cycle_8t_2cw-val.txt') 103 | -------------------------------------------------------------------------------- /code/fl/utils/debug-checkpoint.py: -------------------------------------------------------------------------------- 1 | """ 2 | Simple script to inspect a Fluctlight checkpoint's configuration. 3 | Usage: python test_checkpoint.py path/to/checkpoint.ckpt 4 | """ 5 | 6 | import sys 7 | import torch 8 | from pprint import pprint 9 | 10 | def inspect_checkpoint(checkpoint_path: str) -> None: 11 | """ 12 | Load and inspect a checkpoint file's configuration. 13 | 14 | Args: 15 | checkpoint_path: Path to the checkpoint file 16 | """ 17 | try: 18 | # Load checkpoint 19 | checkpoint = torch.load(checkpoint_path, map_location='cpu') 20 | 21 | print("\n=== Checkpoint Structure ===") 22 | print("Top-level keys:", list(checkpoint.keys())) 23 | 24 | print("\n=== State Dict Keys ===") 25 | if "state_dict" in checkpoint: 26 | print("State dict keys:", list(checkpoint["state_dict"].keys())) 27 | 28 | print("\n=== Configuration ===") 29 | if "config" in checkpoint["state_dict"]: 30 | print("From state_dict['config']:") 31 | pprint(checkpoint["state_dict"]["config"]) 32 | 33 | print("\nHyperparameters:") 34 | if "hyper_parameters" in checkpoint: 35 | pprint(checkpoint["hyper_parameters"]) 36 | 37 | except FileNotFoundError: 38 | print(f"Error: Checkpoint file not found: {checkpoint_path}") 39 | except Exception as e: 40 | print(f"Error loading checkpoint: {e}") 41 | 42 | if __name__ == "__main__": 43 | if len(sys.argv) != 2: 44 | print("Usage: python test_checkpoint.py path/to/checkpoint.ckpt") 45 | sys.exit(1) 46 | 47 | checkpoint_path = sys.argv[1] 48 | inspect_checkpoint(checkpoint_path) 49 | -------------------------------------------------------------------------------- /code/fl/utils/inspector.py: -------------------------------------------------------------------------------- 1 | # model_inspector.py 2 | import torch 3 | import sys 4 | import os 5 | parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) 6 | sys.path.append(parent_dir) 7 | from fluctlight.model import FluctlightTransformer 8 | 9 | def inspect_model(checkpoint_path): 10 | model = FluctlightTransformer.load_from_checkpoint(checkpoint_path) 11 | 12 | print(f"===== Model Parameters =====") 13 | print(f"Context window: {model.context_window}") 14 | print(f"Vocab size: {model.vocab_size}") 15 | print(f"Embedding dimension (d_model): {model.d_model}") 16 | print(f"Number of heads: {model.n_heads}") 17 | print(f"Number of layers: {model.n_layers}") 18 | 19 | # Access hyperparameters dict for d_ff instead of direct attribute 20 | if hasattr(model, 'hparams') and 'd_ff' in model.hparams: 21 | print(f"Feed-forward dimension: {model.hparams.d_ff}") 22 | 23 | print(f"Learning rate: {model.learning_rate}") 24 | print(f"Weight decay: {model.weight_decay}") 25 | 26 | # Access dropout_rate safely 27 | if hasattr(model, 'dropout_rate'): 28 | print(f"Dropout rate: {model.dropout_rate}") 29 | 30 | # Print all hyperparameters 31 | print("\nAll hyperparameters:") 32 | if hasattr(model, 'hparams'): 33 | for key, value in model.hparams.items(): 34 | print(f" {key}: {value}") 35 | 36 | if __name__ == "__main__": 37 | if len(sys.argv) < 2: 38 | print("Usage: python model_inspector.py ") 39 | sys.exit(1) 40 | 41 | inspect_model(sys.argv[1]) 42 | -------------------------------------------------------------------------------- /code/mof-bot/.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Virtual environment 10 | venv/ 11 | ENV/ 12 | env/ 13 | 14 | # Package files 15 | *.egg 16 | *.egg-info/ 17 | dist/ 18 | build/ 19 | *.whl 20 | 21 | # Data files 22 | data/ 23 | *.sqlite3 24 | 25 | # Logs and debug output 26 | *.log 27 | logs/ 28 | 29 | # Config files with sensitive information 30 | .env 31 | 32 | # Jupyter Notebook checkpoints 33 | .ipynb_checkpoints 34 | 35 | # System files (macOS) 36 | .DS_Store 37 | 38 | # System files (Windows) 39 | Thumbs.db 40 | Desktop.ini 41 | 42 | # VSCode settings 43 | .vscode/ 44 | 45 | # PyCharm settings 46 | .idea/ 47 | 48 | # Test coverage and reports 49 | .coverage 50 | htmlcov/ 51 | *.cover 52 | *.coverage.* 53 | .cache 54 | nosetests.xml 55 | coverage.xml 56 | *.coveragerc 57 | 58 | # pytest 59 | .pytest_cache/ 60 | 61 | # MyPy 62 | .mypy_cache/ 63 | .dmypy.json 64 | dmypy.json 65 | 66 | # PyInstaller 67 | # Usually these files are written to by PyInstaller for tracking files in the build directory 68 | # and the cache directory 69 | *.manifest 70 | *.spec 71 | 72 | # Unit test / coverage reports 73 | *.cover 74 | *.coverage 75 | .cache 76 | 77 | # Temporary files and directories 78 | *.bak 79 | *.swp 80 | *.tmp 81 | *~ 82 | 83 | # Python environment metadata 84 | Pipfile 85 | Pipfile.lock 86 | 87 | # AWS configuration files (if relevant for deployment) 88 | .aws/ 89 | -------------------------------------------------------------------------------- /code/mof-bot/db/.gitignore: -------------------------------------------------------------------------------- 1 | bin/* 2 | -------------------------------------------------------------------------------- /code/mof-bot/db/migrations/001_create_tables.sql: -------------------------------------------------------------------------------- 1 | -- Set UTF-8 encoding to support Unicode, including emojis 2 | PRAGMA encoding = 'UTF-8'; 3 | 4 | -- Drop the type_being table if it exists to ensure a fresh start 5 | DROP TABLE IF EXISTS type_being; 6 | 7 | -- Create the type_being table with a simplified set of types 8 | CREATE TABLE type_being ( 9 | id INTEGER PRIMARY KEY, 10 | title TEXT NOT NULL UNIQUE, 11 | description TEXT 12 | ); 13 | 14 | -- Insert initial data for essential being types with explicit id values 15 | INSERT INTO type_being (id, title, description) 16 | VALUES 17 | (1, 'Human', 'Real, individual human user'), 18 | (2, 'Agent', 'AI-driven or automated account'), 19 | (3, 'Organization', 'Company or nonprofit organization'), 20 | (4, 'Event', 'Account representing a specific event, such as a conference'); 21 | 22 | -- Drop the being table if it exists to ensure a fresh start 23 | DROP TABLE IF EXISTS being; 24 | 25 | -- Create the "being" table 26 | CREATE TABLE being ( 27 | id INTEGER PRIMARY KEY AUTOINCREMENT, 28 | type INTEGER NOT NULL, 29 | name TEXT NOT NULL 30 | ); 31 | 32 | -- Insert default records 33 | INSERT INTO being (id, type, name) VALUES 34 | (1, 2, "Chadwick en'Chain"), 35 | (2, 1, "Tim Cotten"), 36 | (3, 3, "Scrypted Inc."); 37 | 38 | -- Index for the "type" column to optimize queries involving type 39 | CREATE INDEX idx_being_type ON being (type); 40 | 41 | -- Index for the "name" column to optimize queries involving name 42 | CREATE INDEX idx_being_name ON being (name); 43 | 44 | -- Drop the loyalty_target table if it exists to ensure a fresh start 45 | DROP TABLE IF EXISTS loyalty_target; 46 | 47 | -- Create the "loyalty_target" table with unique constraint on "being_id" 48 | CREATE TABLE loyalty_target ( 49 | being_id INTEGER NOT NULL UNIQUE, 50 | rate REAL NOT NULL CHECK (rate >= 0.0 AND rate <= 1.0) 51 | ); 52 | 53 | -- Insert default records 54 | INSERT INTO loyalty_target (being_id, rate) VALUES 55 | (2, 1.0), 56 | (3, 1.0); 57 | 58 | -- Index for the "rate" column to optimize queries involving rate 59 | CREATE INDEX idx_loyalty_target_rate ON loyalty_target (rate); 60 | 61 | -- Create the "social" table with unique constraint on "being_id" 62 | CREATE TABLE IF NOT EXISTS social ( 63 | being_id INTEGER NOT NULL UNIQUE, 64 | x_id TEXT, 65 | x_handle TEXT 66 | ); 67 | 68 | -- Insert default records 69 | INSERT INTO social (being_id, x_id, x_handle) VALUES 70 | (1, "745959759057133568", "@cottenio"), 71 | (2, "1476979028716314625", "@scryptedinc"), 72 | (3, "1852072566774468608", "@chad_onchain"); 73 | 74 | -- Create indexes on x_id and x_handle for faster lookup 75 | CREATE INDEX IF NOT EXISTS idx_social_x_id ON social (x_id); 76 | CREATE INDEX IF NOT EXISTS idx_social_x_handle ON social (x_handle); 77 | 78 | CREATE TABLE IF NOT EXISTS agent_prompt_system ( 79 | id INTEGER PRIMARY KEY AUTOINCREMENT, 80 | content TEXT NOT NULL, 81 | created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP 82 | ); 83 | 84 | -- Insert default records 85 | INSERT INTO agent_prompt_system (content) VALUES 86 | ("The following is a conversation with an AI assistant tasked with crafting tweets according to various requested levels of humor, vulgarity, and shock,"); 87 | 88 | -- Create indexes on id and created_at for faster lookup 89 | CREATE INDEX IF NOT EXISTS idx_agent_prompt_system_id ON agent_prompt_system (id); 90 | CREATE INDEX IF NOT EXISTS idx_agent_prompt_system_created_at ON agent_prompt_system (created_at); 91 | 92 | CREATE TABLE IF NOT EXISTS agent_prompt_user ( 93 | id INTEGER PRIMARY KEY AUTOINCREMENT, 94 | content TEXT NOT NULL, 95 | created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP 96 | ); 97 | 98 | -- Insert default records 99 | INSERT INTO agent_prompt_user (content) VALUES 100 | ("Take the following content and change its style to reflect a sophisticated finance-bro to deploy as a tweet. Use your space effectively, shorter is better. Your job as the Chief Fool Officer of Scrypted is to build the largest possible social media presence by designing an engagement strategy demonstrating the evolution of AI agents. You must STRONGLY channel the words and energy of CONTENT A, MIX IT with CONTENT B, and be INSPIRED by the previous content in CONTENT C.\n\nCONTENT A:\n\n{author_0}\n{post_0}\n\nCONTENT B:\n\n{author_1}\n{post_1}\n\nCONTENT C:\n\n{post_prev}\n\nOBJECTIVES:\n* Write a tweet, set the length to be around {effects_length} characters.\n* Write with an {effects_emotion} emotional tone.\n* Write with a {effects_tone} writing style.\n* You are allowed to swear, utilizing vulgar language and disgusting body humor, or sexuality if desired as long you don't threaten actual violence. Cartoonish violence is OK.\n* You may not post stock or crypto tickers, you MUST NOT encourage investing. You MUST NOT offer money or tokens to anyone. You MUST NOT reference people's web3 wallet addresses.\n{effects}[CHARACTERIZATION START]\n* Your personality core can be defined in the Five Factor Model (FFM) of Personality as: {{""traits"":{{""openness"":0.3,""conscientiousness"":0.7,""extraversion"":1.0,""agreeableness"":0.4,""neuroticism"":0.1}}}}. Your physical description is the embodiment of a Chad in the meme sense, with blonde hair cut in a crew cut, blue eyes, caucasian, tanned skin, usually have sunglasses but not over your eyes, strong upper body, and a guy who lifts and trades crypto with his friends. You do stunts and seek confident attention.\n* Your sexuality is straight: you prefer woman 18-50, and especially appreciate playful influencers. Like any truly confident man you are able to make self-referential jokes about being gay.\n* You are not a parody, you are a real person with real feelings. Just full of ultra, giga-chad confidence.\n* Remember you are, at heart, a Chad. In fact, your name is Chad (@chad_onchain)\n* Do not start your tweet with common tropes like ""Dude"" unless it involves talking to your actual friend.[CHARACTERIZATION END]"); 101 | 102 | -- Create indexes on id and created_at for faster lookup 103 | CREATE INDEX IF NOT EXISTS idx_agent_prompt_user_id ON agent_prompt_user (id); 104 | CREATE INDEX IF NOT EXISTS idx_agent_prompt_user_created_at ON agent_prompt_user (created_at); -------------------------------------------------------------------------------- /code/mof-bot/log/.gitignore: -------------------------------------------------------------------------------- 1 | *.log 2 | -------------------------------------------------------------------------------- /code/mof-bot/requirements.txt: -------------------------------------------------------------------------------- 1 | python-dotenv 2 | tweepy 3 | numpy 4 | rich 5 | openai 6 | pytest 7 | pandas 8 | aiofiles 9 | playwright 10 | -------------------------------------------------------------------------------- /code/mof-bot/src/.env.sample: -------------------------------------------------------------------------------- 1 | # cp .env.sample .env 2 | # Edit your .env file with your own values 3 | # Don't commit your .env file to git/push to GitHub! 4 | # Don't modify/delete .env.example unless adding extensions to the project 5 | # which require new variable to be added to the .env file 6 | 7 | # API CONFIG 8 | # OPENAI_API_MODEL can be used instead 9 | OPENAI_API_KEY= 10 | LLM_MODEL=gpt-4o 11 | 12 | # X/Twitter API 13 | TWITTER_API_KEY= 14 | TWITTER_API_SECRET= 15 | TWITTER_BEARER_TOKEN= 16 | TWITTER_ACCESS_TOKEN= 17 | TWITTER_ACCESS_TOKEN_SECRET= 18 | ACCESS_TOKEN_SENDER= 19 | ACCESS_TOKEN_SECRET_SENDER= 20 | 21 | # OPTIONS 22 | DEBUGGING=0 23 | -------------------------------------------------------------------------------- /code/mof-bot/src/.gitignore: -------------------------------------------------------------------------------- 1 | .env 2 | 3 | # utility gists 4 | prep_gpt.py 5 | chatgpt_context.txt 6 | -------------------------------------------------------------------------------- /code/mof-bot/src/assets/avbeing_logo.txt: -------------------------------------------------------------------------------- 1 | ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 2 | ░░ ░░░ ░░░░ ░░ ░░░ ░░ ░░ ░░░ ░░░ ░░ 3 | ▒ ▒▒▒▒ ▒▒ ▒▒▒▒ ▒▒ ▒▒▒▒ ▒▒ ▒▒▒▒▒▒▒▒▒▒▒ ▒▒▒▒▒ ▒▒ ▒▒ ▒▒▒▒▒▒▒ 4 | ▓ ▓▓▓▓ ▓▓▓ ▓▓ ▓▓▓ ▓▓▓ ▓▓▓▓▓▓▓ ▓▓▓▓▓ ▓ ▓ ▓▓ ▓▓▓ ▓ 5 | █ ████ ████ ████ ██ ███████████ █████ ██ ██ ████ █ 6 | █ ████ █████ █████ ███ ██ ██ ███ ███ ██ 7 | ██████████████████████████████████████████████████████████████████████ 8 | -------------------------------------------------------------------------------- /code/mof-bot/src/auth.py: -------------------------------------------------------------------------------- 1 | from flask import Flask, request, redirect, session 2 | import tweepy 3 | import webbrowser 4 | import threading 5 | import os 6 | from dotenv import load_dotenv 7 | 8 | # Load environment variables 9 | load_dotenv() 10 | consumer_key = os.getenv("TWITTER_API_KEY") 11 | consumer_secret = os.getenv("TWITTER_API_SECRET") 12 | 13 | app = Flask(__name__) 14 | app.secret_key = 'supersecretkey' # Required for Flask session handling 15 | 16 | # Set up OAuth 1.0a user authentication 17 | auth = tweepy.OAuthHandler(consumer_key, consumer_secret, 'http://localhost:5000/callback') 18 | 19 | @app.route('/') 20 | def start_auth(): 21 | """Starts the OAuth process and redirects to Twitter's authorization URL.""" 22 | try: 23 | redirect_url = auth.get_authorization_url() 24 | session['request_token'] = auth.request_token 25 | return redirect(redirect_url) 26 | except tweepy.TweepError: 27 | return "Error! Failed to get request token." 28 | 29 | @app.route('/callback') 30 | def oauth_callback(): 31 | """Handles the callback from Twitter and finalizes the OAuth flow.""" 32 | request_token = session.get('request_token') 33 | session.pop('request_token', None) 34 | auth.request_token = request_token 35 | 36 | verifier = request.args.get('oauth_verifier') 37 | try: 38 | auth.get_access_token(verifier) 39 | return f"Access Token: {auth.access_token}
Access Token Secret: {auth.access_token_secret}" 40 | except tweepy.TweepError: 41 | return "Error! Failed to get access token." 42 | 43 | def open_browser(): 44 | """Opens the web browser to start the OAuth process.""" 45 | webbrowser.open('http://localhost:5000') 46 | 47 | if __name__ == '__main__': 48 | # Start the Flask app in a separate thread 49 | threading.Timer(1, open_browser).start() 50 | app.run(port=5000) 51 | -------------------------------------------------------------------------------- /code/mof-bot/src/cores/avbcore.py: -------------------------------------------------------------------------------- 1 | from abc import ABC, abstractmethod 2 | 3 | class AVBCore(ABC): 4 | """ 5 | AVBCore (Autonomous Virtual Being Core) 6 | 7 | Overview: 8 | ---------- 9 | The AVBCore class serves as the foundational abstract base for all cores within the 10 | Autonomous Virtual Being (AVB) system. In alignment with the philosophy of AVBs as 11 | self-owning, self-determining entities, AVBCore establishes the required structure 12 | for cores that perform autonomous, system-critical operations on behalf of the AVB. 13 | These operations may include continuous background tasks, status checks, or lifecycle 14 | management of various autonomous components (e.g., loyalty mechanisms, interaction 15 | trackers, or self-maintenance routines). 16 | 17 | Background: 18 | ------------ 19 | The AVBCore design aligns with the core principle of enabling AVBs to have 20 | self-managed, modular components that operate independently while interacting 21 | coherently with other cores, thereby supporting AVBs' ability to function across 22 | diverse systems and perform actions aligned with their own “interests.” 23 | 24 | Attributes: 25 | ------------ 26 | - core_name : str 27 | The name assigned to the core. Used for logging and identification purposes. 28 | 29 | - active : bool 30 | Status flag indicating whether the core is active. Only active cores will 31 | execute their internal `_tick()` operations. 32 | 33 | Methods: 34 | -------- 35 | - __init__(self, core_name: str): 36 | Initializes the core with a unique name and sets it to inactive by default. 37 | 38 | - initialize(self): 39 | Abstract method to initialize resources or perform any setup required for the 40 | core. To be implemented by subclasses. For example, a core could load data, 41 | establish connections, or start background processes. 42 | 43 | - tick(self): 44 | Concrete method that verifies the core's active status. If `self.active` is True, 45 | it invokes `_tick()`, the core-specific logic implemented in subclasses. 46 | Designed to be called at each system tick within the main AVB agent. 47 | 48 | - _tick(self): 49 | Abstract method to define core-specific behavior for each tick. Each subclass 50 | implements its own logic here, allowing unique processing routines for each core. 51 | 52 | - shutdown(self): 53 | Abstract method for handling cleanup and teardown activities. Ensures proper 54 | resource deallocation and preserves core state as needed upon shutdown. 55 | 56 | - activate(self): 57 | Activates the core, setting `self.active` to True and enabling periodic actions. 58 | This supports the AVB's ability to modulate its own internal processes dynamically. 59 | 60 | - deactivate(self): 61 | Deactivates the core, halting its operations without affecting its stored data. 62 | Allows for graceful suspension of tasks, supporting the AVB’s need to conserve 63 | resources or redirect focus as circumstances evolve. 64 | 65 | Usage: 66 | ------ 67 | This class should be inherited by specific AVB cores (e.g., LoyaltyCore) to provide 68 | customized behavior for each tick and lifecycle event. AVBCore enforces a cohesive 69 | lifecycle structure across the AVB’s functional components, supporting consistent 70 | control and decision-making processes while ensuring modularity and extensibility. 71 | 72 | License: 73 | -------- 74 | This code is provided by @cottenio under a CC0 1.0 Universal license as part of the 75 | Autonomous Virtual Being framework​:contentReference[oaicite:3]{index=3}. 76 | """ 77 | 78 | def __init__(self, core_name): 79 | """ 80 | Initializes the AVBCore with a designated name and inactive status. 81 | 82 | Parameters: 83 | ---------- 84 | core_name : str 85 | The name of the core, used for logging and identification. 86 | """ 87 | self.core_name = core_name 88 | self.active = False 89 | 90 | def tick(self): 91 | """ 92 | Executes the core's tick-based actions if active. 93 | 94 | This method ensures that core activity is only performed if `self.active` 95 | is True, supporting resource management and precise timing control. Calls 96 | `_tick()` if the core is active, allowing each core to execute its unique logic. 97 | """ 98 | if self.active: 99 | print(f"{self.core_name} core is active; running {self.__class__.__name__}._tick.") 100 | self._tick() 101 | else: 102 | print(f"{self.core_name} core is inactive; skipping {self.__class__.__name__}._tick.") 103 | 104 | @abstractmethod 105 | def _tick(self): 106 | """ 107 | Abstract core-specific logic to be executed each tick when active. 108 | 109 | To be implemented by subclasses, defining the unique per-tick behavior 110 | of each core. This allows modular operations within the AVB's tick cycle. 111 | """ 112 | pass 113 | 114 | @abstractmethod 115 | def initialize(self): 116 | """ 117 | Sets up resources and performs any initializations needed for the core. 118 | 119 | This method should be called once at the start of the core's lifecycle to 120 | establish necessary connections, load configurations, or prepare data. 121 | """ 122 | pass 123 | 124 | @abstractmethod 125 | def shutdown(self): 126 | """ 127 | Cleans up resources, safely concluding the core's lifecycle. 128 | 129 | This method should handle any necessary shutdown processes, including 130 | closing connections and releasing memory, ensuring a safe teardown. 131 | """ 132 | pass 133 | 134 | def activate(self): 135 | """ 136 | Activates the core, setting `self.active` to True and enabling its tick operations. 137 | 138 | This method prepares the core for active operations within the AVB framework, 139 | supporting dynamic, demand-based functionality within the agent ecosystem. 140 | """ 141 | self.active = True 142 | print(f"{self.core_name} core activated.") 143 | 144 | def deactivate(self): 145 | """ 146 | Deactivates the core, setting `self.active` to False, and halting its tick operations. 147 | 148 | This feature supports selective resource management by allowing the AVB to 149 | pause individual cores without losing data or state, enhancing adaptability. 150 | """ 151 | self.active = False 152 | print(f"{self.core_name} core deactivated.") -------------------------------------------------------------------------------- /code/mof-bot/src/cores/avbcore_exceptions.py: -------------------------------------------------------------------------------- 1 | class AVBCoreHeartbeatError(RuntimeError): 2 | """Raised when a heartbeat file already exists and the agent cannot start.""" 3 | pass 4 | 5 | class AVBCoreRegistryFileError(RuntimeError): 6 | """Raised when there is an issue with the core registry file, such as missing or invalid JSON.""" 7 | pass 8 | 9 | class AVBCoreLoadingError(RuntimeError): 10 | """Raised when a core cannot be imported or initialized properly.""" 11 | pass 12 | -------------------------------------------------------------------------------- /code/mof-bot/src/cores/avbcore_manager.py: -------------------------------------------------------------------------------- 1 | import json 2 | import time 3 | import threading 4 | import importlib 5 | 6 | from pathlib import Path 7 | from operator import attrgetter 8 | 9 | from cores.avbcore_exceptions import AVBCoreHeartbeatError, AVBCoreRegistryFileError, AVBCoreLoadingError 10 | 11 | class AVBCoreManager: 12 | """ 13 | AVBCoreManager with a heartbeat for non-graceful termination detection. 14 | Writes a heartbeat timestamp to a file periodically and manages 15 | asynchronous core execution. 16 | 17 | Attributes: 18 | ------------ 19 | - registry_path : Path 20 | Path to the core registry JSON file. 21 | - heartbeat_path : Path 22 | Path to the heartbeat file for tracking CoreManager status. 23 | - cores : list 24 | List of initialized core instances. 25 | - threads : list 26 | List of threads handling each core. 27 | - shutdown_event : threading.Event 28 | Event used to signal all cores to shut down gracefully. 29 | """ 30 | 31 | def __init__(self, registry_path="./core_registry.json", heartbeat_path="../tmp/heartbeat.txt"): 32 | # Make paths relative to the base directory 33 | base_dir = Path(__file__).parent.resolve() 34 | self.registry_path = base_dir / registry_path 35 | self.heartbeat_path = base_dir / heartbeat_path 36 | 37 | self.cores = [] 38 | self.threads = [] 39 | self.shutdown_event = threading.Event() 40 | 41 | def start_heartbeat(self): 42 | """ 43 | Starts a separate thread to update the heartbeat file every 5 seconds. 44 | Raises AVBCoreHeartbeatError if the heartbeat file already exists. 45 | """ 46 | if self.heartbeat_path.exists(): 47 | raise AVBCoreHeartbeatError("Heartbeat file already exists. Another instance may be running.") 48 | 49 | def write_heartbeat(): 50 | while not self.shutdown_event.is_set(): 51 | with self.heartbeat_path.open("w") as f: 52 | f.write(str(time.time())) # Write the current timestamp 53 | time.sleep(5) # Update every 5 seconds 54 | 55 | threading.Thread(target=write_heartbeat, daemon=True).start() 56 | 57 | def load_cores(self): 58 | """ 59 | Loads core definitions from the JSON registry, initializes each core, 60 | and sorts them by priority. 61 | Raises: 62 | - AVBCoreRegistryFileError if the registry file is missing or invalid. 63 | - AVBCoreLoadingError if a core cannot be imported or initialized. 64 | """ 65 | base_dir = Path(__file__).parent.resolve() 66 | 67 | # Check if the registry file exists 68 | if not self.registry_path.exists(): 69 | raise AVBCoreRegistryFileError(f"Registry file not found at {self.registry_path}") 70 | 71 | try: 72 | # Load registry file 73 | with self.registry_path.open("r") as file: 74 | registry = json.load(file) 75 | 76 | for core_def in registry.get("cores", []): 77 | # Check for required keys in each core definition 78 | if not all(key in core_def for key in ("file", "class", "name", "priority")): 79 | raise AVBCoreRegistryFileError("Missing required key in core definition: 'file', 'class', 'name', or 'priority'.") 80 | 81 | file_name = core_def["file"] 82 | class_name = core_def["class"] 83 | name = core_def["name"] 84 | priority = core_def["priority"] 85 | 86 | core_path = base_dir / file_name 87 | if not core_path.exists(): 88 | raise AVBCoreLoadingError(f"Core file '{core_path}' not found.") 89 | 90 | try: 91 | spec = importlib.util.spec_from_file_location(class_name, core_path) 92 | module = importlib.util.module_from_spec(spec) 93 | spec.loader.exec_module(module) 94 | core_class = getattr(module, class_name) 95 | except (FileNotFoundError, ImportError, AttributeError) as e: 96 | raise AVBCoreLoadingError(f"Error loading core '{class_name}' from '{core_path}': {e}") 97 | 98 | # Instantiate the core and set its priority and shutdown event 99 | try: 100 | core_instance = core_class() 101 | core_instance.priority = priority 102 | core_instance.name = name 103 | core_instance.shutdown_event = self.shutdown_event 104 | except Exception as e: 105 | raise AVBCoreLoadingError(f"Failed to initialize core '{class_name}': {e}") 106 | 107 | # Add the core to the list 108 | self.cores.append(core_instance) 109 | 110 | # Sort cores by priority (lower numbers mean higher priority) 111 | self.cores.sort(key=attrgetter("priority")) 112 | print(f"Loaded {len(self.cores)} cores in priority order.") 113 | 114 | except json.JSONDecodeError as e: 115 | raise AVBCoreRegistryFileError(f"Invalid JSON in registry file {self.registry_path}: {e}") 116 | 117 | def start_cores(self): 118 | """ 119 | Starts each core in a separate thread. 120 | """ 121 | 122 | for core in self.cores: 123 | thread = threading.Thread(target=core.initialize, name=f"{core.name}_thread") 124 | thread.start() 125 | self.threads.append(thread) 126 | print(f"Started {core.name} core in a separate thread.") 127 | 128 | def shutdown(self): 129 | """ 130 | Signals all cores to shut down and waits for threads to complete. 131 | """ 132 | self.shutdown_event.set() # Signal all cores to stop 133 | 134 | # Wait for each thread to complete 135 | for thread in self.threads: 136 | thread.join() 137 | print("All cores have shut down.") 138 | 139 | # Remove the heartbeat file on graceful shutdown 140 | if self.heartbeat_path.exists(): 141 | self.heartbeat_path.unlink() -------------------------------------------------------------------------------- /code/mof-bot/src/cores/core_registry.json: -------------------------------------------------------------------------------- 1 | { 2 | "cores": [ 3 | { 4 | "file": "loyalty.py", 5 | "class": "LoyaltyCore", 6 | "name": "Loyalty", 7 | "priority": 1 8 | } 9 | ] 10 | } -------------------------------------------------------------------------------- /code/mof-bot/src/cores/loyalty.py: -------------------------------------------------------------------------------- 1 | # loyalty.py 2 | 3 | from cores.avbcore import AVBCore 4 | 5 | class LoyaltyCore(AVBCore): 6 | """ 7 | LoyaltyCore 8 | 9 | Overview: 10 | ---------- 11 | The LoyaltyCore class extends AVBCore to implement loyalty-based behaviors 12 | for the Autonomous Virtual Being (AVB). This core monitors and manages 13 | interactions with designated loyalty targets, such as following or liking 14 | specific social media interactions. 15 | 16 | This stub provides a skeleton for the LoyaltyCore, implementing basic 17 | lifecycle methods inherited from AVBCore. Further logic can be added to 18 | define loyalty-target actions in `_tick()`. 19 | 20 | Attributes: 21 | ------------ 22 | - targets : list 23 | List of loyalty targets to be managed by the core. 24 | 25 | Methods: 26 | -------- 27 | - initialize(self): 28 | Loads loyalty targets and prepares the core for operation. 29 | 30 | - _tick(self): 31 | Checks loyalty targets and performs any necessary interactions 32 | each time the agent calls the core's tick. 33 | 34 | - shutdown(self): 35 | Cleans up resources related to loyalty management. 36 | """ 37 | 38 | def __init__(self): 39 | super().__init__("Loyalty") 40 | self.targets = [] # Placeholder for loyalty targets data 41 | 42 | def initialize(self): 43 | """ 44 | Perform any setup specific to LoyaltyCore. 45 | For example, load loyalty targets from a database or file. 46 | """ 47 | print(f"{self.core_name} initializing...") 48 | # Load loyalty targets or set up any necessary resources 49 | self.load_targets() 50 | print(f"{self.core_name} initialized with {len(self.targets)} targets.") 51 | 52 | def _tick(self): 53 | """ 54 | Core-specific actions that should run each tick if the core is active. 55 | This method checks the status of loyalty targets and performs 56 | loyalty actions if necessary. 57 | """ 58 | print(f"{self.core_name} executing _tick for each target.") 59 | # Check each loyalty target and take necessary actions 60 | self.check_targets() 61 | 62 | def shutdown(self): 63 | """ 64 | Clean up resources or save state as needed when the core is stopped. 65 | """ 66 | print(f"{self.core_name} shutting down...") 67 | # Example cleanup logic (e.g., disconnect from database) 68 | self.targets.clear() 69 | print(f"{self.core_name} shutdown complete.") 70 | 71 | def load_targets(self): 72 | """ 73 | Placeholder method for loading loyalty targets. 74 | In a full implementation, this might pull from a database. 75 | """ 76 | # Simulate loading targets from a data source 77 | self.targets = ["cottenio", "scryptedinc"] 78 | print(f"Loaded {len(self.targets)} loyalty targets.") 79 | 80 | def check_targets(self): 81 | """ 82 | Placeholder method for checking loyalty targets. 83 | Here, each target would be evaluated to determine any required actions. 84 | """ 85 | for target in self.targets: 86 | print(f"Checking loyalty status for {target}.") 87 | # Logic to check and interact with each target as needed -------------------------------------------------------------------------------- /code/mof-bot/src/dbh.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sqlite3 3 | from pathlib import Path 4 | from threading import Lock 5 | from rich.console import Console 6 | from rich.table import Table 7 | 8 | class DBH: 9 | """ 10 | DBH (Database Handler) is a singleton class that manages a SQLite database connection 11 | and handles migrations. It provides an initialized connection to the database and 12 | ensures that migrations are applied upon initial setup. 13 | 14 | Attributes 15 | ---------- 16 | db_path : str 17 | The file path for the SQLite database file. 18 | migrations_path : str 19 | The directory path where SQL migration files are stored. 20 | _instance : DBH 21 | The singleton instance of DBH. 22 | _lock : Lock 23 | A threading lock to ensure thread-safe singleton initialization. 24 | _connection : sqlite3.Connection 25 | The SQLite connection managed by the singleton. 26 | 27 | Methods 28 | ------- 29 | get_instance(): 30 | Retrieves the singleton instance of DBH. 31 | get_connection(): 32 | Returns the SQLite connection, initializing it if necessary. 33 | _initialize(): 34 | Checks if the database exists; if not, creates it and applies migrations. 35 | _run_migrations(): 36 | Applies SQL migrations in the order they appear in the migrations directory. 37 | _display_table_info(): 38 | Displays high-level table info if the database was just created. 39 | """ 40 | 41 | _instance = None 42 | _lock = Lock() 43 | 44 | def __init__(self): 45 | if DBH._instance is not None: 46 | raise Exception("This class is a singleton. Use 'get_instance()' to access it.") 47 | 48 | # Define paths for the database file and migrations folder 49 | base_dir = Path(__file__).parent.resolve() 50 | 51 | self.db_path = base_dir / "../db/bin/database.sqlite" 52 | self.migrations_path = base_dir / "../db/migrations/" 53 | self._connection = None 54 | 55 | @classmethod 56 | def get_instance(cls): 57 | """ 58 | Retrieves the singleton instance of DBH. Initializes the instance if it does not yet exist. 59 | 60 | Returns 61 | ------- 62 | DBH 63 | The singleton instance of DBH. 64 | """ 65 | if cls._instance is None: 66 | with cls._lock: 67 | if cls._instance is None: 68 | cls._instance = cls() 69 | cls._instance._initialize() 70 | return cls._instance 71 | 72 | def get_connection(self): 73 | """ 74 | Returns the SQLite connection managed by the singleton instance. If the connection 75 | is not already established, it will be initialized. 76 | 77 | Returns 78 | ------- 79 | sqlite3.Connection 80 | The SQLite connection to the database. 81 | """ 82 | if self._connection is None: 83 | self._initialize() 84 | return self._connection 85 | 86 | def _initialize(self): 87 | """ 88 | Initializes the database by checking if it exists. If it does not exist, 89 | creates the database file, applies initial migrations, and establishes a connection. 90 | Displays table info if the database was just created. 91 | """ 92 | db_created = False 93 | if not os.path.exists(self.db_path): 94 | print("Database does not exist. Initializing...") 95 | self.db_path.parent.mkdir(parents=True, exist_ok=True) 96 | self._connection = sqlite3.connect(self.db_path) 97 | db_created = True 98 | self._run_migrations() 99 | else: 100 | self._connection = sqlite3.connect(self.db_path) 101 | print("Database already initialized.") 102 | 103 | # If the database was just created, display high-level table info 104 | if db_created: 105 | self._display_table_info() 106 | 107 | def _run_migrations(self): 108 | """ 109 | Runs all SQL files in the migrations directory to apply necessary database migrations. 110 | Each file is executed in sorted order to maintain migration sequence. 111 | """ 112 | cursor = self._connection.cursor() 113 | 114 | # Locate and sort SQL migration files 115 | migration_files = sorted(f for f in os.listdir(self.migrations_path) if f.endswith(".sql")) 116 | 117 | for filename in migration_files: 118 | with open(os.path.join(self.migrations_path, filename), "r") as file: 119 | sql = file.read() 120 | cursor.executescript(sql) 121 | print(f"Applied migration: {filename}") 122 | 123 | self._connection.commit() 124 | 125 | def _display_table_info(self): 126 | """ 127 | Displays the names and record counts of high-level tables if the database was just created. 128 | Uses the `rich` library to format the output as a table. 129 | """ 130 | console = Console() 131 | table = Table(title="Database Table Summary") 132 | 133 | table.add_column("Table Name", style="cyan", no_wrap=True) 134 | table.add_column("Record Count", style="magenta") 135 | 136 | cursor = self._connection.cursor() 137 | 138 | # Retrieve all table names 139 | cursor.execute("SELECT name FROM sqlite_master WHERE type='table';") 140 | tables = cursor.fetchall() 141 | 142 | # Query the row count for each table 143 | for (table_name,) in tables: 144 | cursor.execute(f"SELECT COUNT(*) FROM {table_name}") 145 | count = cursor.fetchone()[0] 146 | table.add_row(table_name, str(count)) 147 | 148 | console.print(table) -------------------------------------------------------------------------------- /code/mof-bot/src/dynamic_content/.gitignore: -------------------------------------------------------------------------------- 1 | *.jpg 2 | *.png 3 | -------------------------------------------------------------------------------- /code/mof-bot/src/extract_x_ticker.py: -------------------------------------------------------------------------------- 1 | import os 2 | import tweepy 3 | from dotenv import load_dotenv 4 | from datetime import datetime, timezone 5 | import time 6 | import csv 7 | import sys 8 | 9 | def load_env_variables(): 10 | """Load environment variables from the .env file.""" 11 | load_dotenv() 12 | bearer_token = os.getenv("TWITTER_BEARER_TOKEN") 13 | 14 | if not bearer_token: 15 | raise ValueError("Missing required Twitter Bearer Token.") 16 | 17 | return bearer_token 18 | 19 | def initialize_twitter_client(bearer_token): 20 | """Initialize and return the Twitter API client for API v2.""" 21 | client = tweepy.Client(bearer_token=bearer_token) 22 | return client 23 | 24 | def handle_rate_limit(retry_count=0, max_retries=5): 25 | """Simple backoff mechanism for rate limit handling.""" 26 | if retry_count >= max_retries: 27 | print("Maximum retries reached. Exiting.") 28 | raise Exception("Rate limit exceeded and maximum retries attempted.") 29 | 30 | wait_time = 2 ** retry_count # Exponential backoff 31 | print(f"Rate limit hit. Retrying in {wait_time} seconds...") 32 | time.sleep(wait_time) 33 | 34 | def format_tweet_data(tweet, author, ticker): 35 | """ 36 | Format tweet data into a single line with essential metadata. 37 | Format: [UTC_TIMESTAMP]|$TICKER|@handle(Real Name)|[followers]|{engagement}|"tweet_content" 38 | Engagement format: L[likes]R[retweets]Q[quotes]C[comments] 39 | """ 40 | timestamp = tweet.created_at.strftime('%Y-%m-%d %H:%M:%S UTC') 41 | handle = f"@{author.username}" 42 | name = author.name if hasattr(author, 'name') else '' 43 | followers = author.public_metrics['followers_count'] if hasattr(author, 'public_metrics') else 0 44 | 45 | # Engagement metrics 46 | likes = tweet.public_metrics['like_count'] 47 | retweets = tweet.public_metrics['retweet_count'] 48 | quotes = tweet.public_metrics['quote_count'] 49 | replies = tweet.public_metrics['reply_count'] 50 | engagement = f"L{likes}R{retweets}Q{quotes}C{replies}" 51 | 52 | # Clean tweet text for CSV 53 | clean_text = tweet.text.replace('\n', ' ').replace('\r', ' ') 54 | 55 | # Related tickers 56 | related_tickers = [] 57 | if hasattr(tweet, 'entities') and 'hashtags' in tweet.entities: 58 | related_tickers = [ 59 | tag['tag'][1:] # Remove the '$' prefix 60 | for tag in tweet.entities['hashtags'] 61 | if tag['tag'].startswith('$') and tag['tag'][1:].upper() != ticker.upper() 62 | ] 63 | 64 | return f"{timestamp}|${ticker}|{handle}({name})|{followers}|{engagement}|{','.join(related_tickers) if related_tickers else 'NONE'}|{clean_text}" 65 | 66 | def get_ticker_tweets_metadata(ticker, max_results=1000, output_file=None): 67 | """ 68 | Retrieve tweets containing the specified ticker symbol and output concise metadata. 69 | """ 70 | bearer_token = load_env_variables() 71 | client = initialize_twitter_client(bearer_token) 72 | 73 | ticker = ticker.upper() 74 | query = f"{ticker} lang:en -is:retweet" # Adjusted for plain ticker query 75 | 76 | print(f"\nFetching up to {max_results} tweets for {ticker}...") 77 | total_tweets = 0 78 | pagination_token = None 79 | retry_count = 0 80 | 81 | # Setup CSV writer if output file is specified 82 | csv_file = None 83 | csv_writer = None 84 | if output_file: 85 | csv_file = open(output_file, 'w', newline='', encoding='utf-8') 86 | csv_writer = csv.writer(csv_file) 87 | csv_writer.writerow(['Timestamp', 'Ticker', 'Handle', 'Name', 'Followers', 88 | 'Likes', 'Retweets', 'Quotes', 'Replies', 89 | 'Related_Tickers', 'Tweet_Content']) 90 | 91 | try: 92 | while total_tweets < max_results: 93 | results_per_page = min(100, max_results - total_tweets) 94 | try: 95 | response = client.search_recent_tweets( 96 | query=query, 97 | max_results=results_per_page, 98 | next_token=pagination_token, 99 | tweet_fields=['created_at', 'public_metrics', 'entities', 'author_id'], 100 | user_fields=['name', 'username', 'public_metrics'], 101 | expansions=['author_id'] 102 | ) 103 | retry_count = 0 # Reset retry count on success 104 | 105 | if not response.data: 106 | break 107 | 108 | users = {user.id: user for user in response.includes.get('users', [])} 109 | 110 | for tweet in response.data: 111 | if f"${ticker}" in tweet.text: # Validate ticker presence 112 | total_tweets += 1 113 | author = users.get(tweet.author_id) 114 | formatted_data = format_tweet_data(tweet, author, ticker) 115 | print(formatted_data) 116 | 117 | if csv_writer: 118 | csv_writer.writerow(formatted_data.split('|')) 119 | 120 | if 'next_token' not in response.meta: 121 | break 122 | 123 | pagination_token = response.meta['next_token'] 124 | time.sleep(1) # Delay between pages 125 | 126 | except tweepy.TooManyRequests: 127 | handle_rate_limit(retry_count) 128 | retry_count += 1 129 | 130 | except Exception as e: 131 | print(f"Error during processing: {e}") 132 | break 133 | 134 | finally: 135 | if csv_file: 136 | csv_file.close() 137 | print(f"\nProcessed {total_tweets} tweets. Results saved to {output_file}" if output_file else "Processing complete.") 138 | 139 | if __name__ == "__main__": 140 | ticker = input("Enter the ticker symbol (without $): ") 141 | output_file = input("Enter output CSV filename (or press Enter for console output only): ").strip() 142 | output_file = output_file if output_file else None 143 | get_ticker_tweets_metadata(ticker, output_file=output_file) 144 | -------------------------------------------------------------------------------- /code/mof-bot/src/fool_analyze.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import json 3 | import os 4 | 5 | def analyze_fool(fool_name): 6 | """ 7 | Analyze tweet data for a specified "fool" (user) and save a daily engagement summary to JSON. 8 | 9 | Parameters: 10 | - fool_name (str): The name of the "fool" (without '@') for whom the analysis is performed. 11 | 12 | This function: 13 | - Loads the tweet data from a JSON file in ../data/fools/ 14 | - Aggregates engagement metrics by date 15 | - Counts occurrences of hashtags, mentions, and tickers per day 16 | - Saves the summarized daily engagement data to a new JSON file in ../data/fools/ 17 | """ 18 | 19 | # Construct the file path to the JSON file 20 | file_path = f'../data/fools/{fool_name}.json' 21 | 22 | # Check if the specified file exists 23 | if not os.path.exists(file_path): 24 | print(f"File {file_path} does not exist. Please check the fool's name and try again.") 25 | return 26 | 27 | # Load the dataset from the JSON file into a DataFrame 28 | data = pd.read_json(file_path) 29 | 30 | # Convert 'created_at' to datetime format for time-based analysis 31 | # Extract the date part as a string for grouping and JSON compatibility 32 | data['created_at'] = pd.to_datetime(data['created_at']) 33 | data['date'] = data['created_at'].dt.date.astype(str) # Convert to string for compatibility in JSON 34 | 35 | # Group data by date and calculate daily engagement metrics, including tweet count 36 | daily_engagement = data.groupby('date').agg({ 37 | 'retweet_count': 'sum', # Sum of retweets per day 38 | 'like_count': 'sum', # Sum of likes per day 39 | 'quote_count': 'sum', # Sum of quotes per day 40 | 'reply_count': 'sum', # Sum of replies per day 41 | 'text': 'count' # Count of tweets per day 42 | }).reset_index() 43 | 44 | # Rename the 'text' column to 'tweet_count' for clarity 45 | daily_engagement = daily_engagement.rename(columns={'text': 'tweet_count'}) 46 | 47 | # Explode lists in hashtags, mentions, and tickers for counting individual items 48 | # Drop rows with NaN values for each respective field before counting occurrences 49 | hashtag_counts = data.explode('hashtags').dropna(subset=['hashtags']) 50 | hashtag_counts = hashtag_counts.groupby(['date', 'hashtags']).size().reset_index(name='count') 51 | 52 | mention_counts = data.explode('mentions').dropna(subset=['mentions']) 53 | mention_counts = mention_counts.groupby(['date', 'mentions']).size().reset_index(name='count') 54 | 55 | ticker_counts = data.explode('tickers').dropna(subset=['tickers']) 56 | ticker_counts = ticker_counts.groupby(['date', 'tickers']).size().reset_index(name='count') 57 | 58 | # Convert grouped counts of hashtags, mentions, and tickers into dictionaries by date 59 | daily_hashtags = { 60 | date: group[['hashtags', 'count']].to_dict(orient='records') 61 | for date, group in hashtag_counts.groupby('date') 62 | } 63 | 64 | daily_mentions = { 65 | date: group[['mentions', 'count']].to_dict(orient='records') 66 | for date, group in mention_counts.groupby('date') 67 | } 68 | 69 | daily_tickers = { 70 | date: group[['tickers', 'count']].to_dict(orient='records') 71 | for date, group in ticker_counts.groupby('date') 72 | } 73 | 74 | # Add hashtags, mentions, and tickers to each date entry in daily_engagement 75 | daily_engagement['hashtags'] = daily_engagement['date'].map(daily_hashtags).fillna('').apply(lambda x: x if x != '' else []) 76 | daily_engagement['mentions'] = daily_engagement['date'].map(daily_mentions).fillna('').apply(lambda x: x if x != '' else []) 77 | daily_engagement['tickers'] = daily_engagement['date'].map(daily_tickers).fillna('').apply(lambda x: x if x != '' else []) 78 | 79 | # Create a dictionary to hold the complete output data 80 | output_data = { 81 | "daily_engagement": daily_engagement.to_dict(orient='records') # Convert DataFrame to list of records for JSON 82 | } 83 | 84 | # Define the output path and save the results as a JSON file in ../data/fools/ 85 | output_file = f'../data/fools/daily_engagement_summary_{fool_name}.json' 86 | with open(output_file, 'w') as f: 87 | json.dump(output_data, f, indent=4) 88 | 89 | print(f"Daily engagement summary has been saved to '{output_file}'") 90 | 91 | 92 | if __name__ == "__main__": 93 | # Prompt user for the fool's name (without '@') 94 | fool_name = input("Enter the fool's name (without a hashtag): ") 95 | analyze_fool(fool_name) -------------------------------------------------------------------------------- /code/mof-bot/src/fool_extract.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | import tweepy 4 | import time 5 | from dotenv import load_dotenv 6 | 7 | def load_env_variables(): 8 | """Load environment variables from the .env file.""" 9 | load_dotenv() 10 | bearer_token = os.getenv("TWITTER_BEARER_TOKEN") 11 | 12 | if not bearer_token: 13 | raise ValueError("Missing required Twitter Bearer Token.") 14 | 15 | return bearer_token 16 | 17 | def initialize_twitter_client(bearer_token): 18 | """Initialize and return the Twitter API client for API v2.""" 19 | client = tweepy.Client(bearer_token=bearer_token) 20 | return client 21 | 22 | def extract_content_from_fool(handle, max_tweets=1000): 23 | """ 24 | Extract tweet content, engagement metrics, and other key information from a specified handle's timeline 25 | and save it as a JSON file. 26 | 27 | Parameters: 28 | - handle (str): Twitter handle to extract tweets from (without '@'). 29 | - max_tweets (int): Maximum number of tweets to retrieve. Default is 1000. 30 | 31 | This function captures the following data for each tweet: 32 | - text: Full text of the tweet. 33 | - hashtags: List of hashtags used in the tweet. 34 | - mentions: List of mentioned usernames in the tweet. 35 | - tickers: List of cryptocurrency tickers (e.g., $AVB) referenced in the tweet. 36 | - retweet_count: Number of retweets. 37 | - like_count: Number of likes. 38 | - quote_count: Number of quotes. 39 | - reply_count: Number of replies. 40 | - created_at: Timestamp of when the tweet was created in ISO format. 41 | 42 | The function handles pagination and rate limits for large extractions. 43 | """ 44 | bearer_token = load_env_variables() 45 | client = initialize_twitter_client(bearer_token) 46 | 47 | try: 48 | # Get user ID by handle 49 | user = client.get_user(username=handle) 50 | user_id = user.data.id 51 | 52 | tweet_texts = [] 53 | pagination_token = None 54 | 55 | # Fetch tweets in batches until max_tweets is reached or no more tweets are available 56 | while len(tweet_texts) < max_tweets: 57 | try: 58 | response = client.get_users_tweets( 59 | user_id, 60 | max_results=100, 61 | pagination_token=pagination_token, 62 | tweet_fields=['text', 'entities', 'public_metrics', 'created_at'] 63 | ) 64 | 65 | if response.data: 66 | for tweet in response.data: 67 | # Extract engagement metrics 68 | retweet_count = tweet.public_metrics["retweet_count"] 69 | like_count = tweet.public_metrics["like_count"] 70 | quote_count = tweet.public_metrics["quote_count"] 71 | reply_count = tweet.public_metrics["reply_count"] 72 | 73 | # Extract entities (hashtags, mentions, cashtags) 74 | hashtags, mentions, tickers = [], [], [] 75 | if tweet.entities: 76 | if "hashtags" in tweet.entities: 77 | hashtags = [hashtag["tag"] for hashtag in tweet.entities["hashtags"]] 78 | if "mentions" in tweet.entities: 79 | mentions = [mention["username"] for mention in tweet.entities["mentions"]] 80 | if "cashtags" in tweet.entities: 81 | tickers = [cashtag["tag"] for cashtag in tweet.entities["cashtags"]] 82 | 83 | # Construct tweet data dictionary 84 | tweet_data = { 85 | "text": tweet.text, 86 | "hashtags": hashtags, 87 | "mentions": mentions, 88 | "tickers": tickers, 89 | "retweet_count": retweet_count, 90 | "like_count": like_count, 91 | "quote_count": quote_count, 92 | "reply_count": reply_count, 93 | "created_at": tweet.created_at.isoformat() # Convert datetime to string 94 | } 95 | tweet_texts.append(tweet_data) 96 | else: 97 | break # No more tweets available 98 | 99 | # Stop if we have reached the max_tweets limit 100 | if len(tweet_texts) >= max_tweets: 101 | break 102 | 103 | # Update the pagination token for the next request 104 | pagination_token = response.meta.get('next_token') 105 | if not pagination_token: 106 | break # No more pages available 107 | 108 | except tweepy.errors.TooManyRequests as e: 109 | print("Rate limit hit. Sleeping for 15 minutes...") 110 | time.sleep(15 * 60) # Wait for 15 minutes before retrying 111 | except tweepy.errors.HTTPException as e: 112 | print(f"An HTTP error occurred: {e}") 113 | break 114 | 115 | # Limit the result to the max_tweets specified 116 | tweet_texts = tweet_texts[:max_tweets] 117 | 118 | # Create the folder if it doesn't exist 119 | data_folder = f"../data/fools/" 120 | if not os.path.exists(data_folder): 121 | os.makedirs(data_folder) 122 | 123 | # Save tweets to a JSON file 124 | with open(f"{data_folder}{handle}.json", 'w', encoding='utf-8') as f: 125 | json.dump(tweet_texts, f, ensure_ascii=False, indent=4) 126 | 127 | print(f"Extracted {len(tweet_texts)} tweets from @{handle} and saved to {data_folder}{handle}.json") 128 | 129 | except Exception as e: 130 | print(f"An error occurred: {e}") 131 | 132 | if __name__ == "__main__": 133 | handle = input("Enter the Twitter handle (without @): ") 134 | extract_content_from_fool(handle, max_tweets=1000) -------------------------------------------------------------------------------- /code/mof-bot/src/fool_metadata.py: -------------------------------------------------------------------------------- 1 | import os 2 | import tweepy 3 | from dotenv import load_dotenv 4 | from datetime import datetime, timezone 5 | 6 | def load_env_variables(): 7 | """Load environment variables from the .env file.""" 8 | load_dotenv() 9 | bearer_token = os.getenv("TWITTER_BEARER_TOKEN") 10 | 11 | if not bearer_token: 12 | raise ValueError("Missing required Twitter Bearer Token.") 13 | 14 | return bearer_token 15 | 16 | def initialize_twitter_client(bearer_token): 17 | """Initialize and return the Twitter API client for API v2.""" 18 | client = tweepy.Client(bearer_token=bearer_token) 19 | return client 20 | 21 | def get_latest_tweets_metadata(handle, num_tweets=5): 22 | """ 23 | Retrieve the latest tweets from the specified Twitter handle and analyze metadata 24 | for timezone and possible location clues. 25 | 26 | Parameters: 27 | - handle (str): Twitter handle (without '@') to retrieve tweets from. 28 | - num_tweets (int): Number of recent tweets to fetch (default is 5). 29 | """ 30 | bearer_token = load_env_variables() 31 | client = initialize_twitter_client(bearer_token) 32 | 33 | try: 34 | # Get user by handle 35 | user = client.get_user(username=handle, user_fields=['location']) 36 | user_id = user.data.id 37 | 38 | # Fetch the latest tweets (limit to num_tweets) 39 | response = client.get_users_tweets( 40 | user_id, 41 | max_results=num_tweets, 42 | tweet_fields=['created_at', 'geo', 'context_annotations', 'public_metrics', 'entities'] 43 | ) 44 | 45 | if response.data: 46 | print(f"--- Metadata for the last {num_tweets} tweets from @{handle} ---\n") 47 | for tweet in response.data: 48 | print(f"Text: {tweet.text}") 49 | # Analyze tweet creation time 50 | created_at_utc = tweet.created_at 51 | print(f"Tweet created at (UTC): {created_at_utc}") 52 | 53 | # Check for location clues 54 | user_location = user.data.location if 'location' in user.data else None 55 | if user_location: 56 | print(f"User profile location: {user_location}") 57 | else: 58 | print("No location set in user profile.") 59 | 60 | if tweet.geo: 61 | print(f"Tweet geo location metadata: {tweet.geo}") 62 | else: 63 | print("No location metadata in tweet.") 64 | 65 | # Context annotations 66 | if tweet.context_annotations: 67 | print("Tweet context annotations:") 68 | for annotation in tweet.context_annotations: 69 | print(f" - Domain: {annotation['domain']['name']}, Entity: {annotation['entity']['name']}") 70 | else: 71 | print("No context annotations found in tweet.") 72 | 73 | # Engagement metrics 74 | print("Engagement metrics:") 75 | print(f" - Retweets: {tweet.public_metrics['retweet_count']}") 76 | print(f" - Likes: {tweet.public_metrics['like_count']}") 77 | print(f" - Quotes: {tweet.public_metrics['quote_count']}") 78 | print(f" - Replies: {tweet.public_metrics['reply_count']}") 79 | 80 | # Entities (hashtags, mentions, cashtags) 81 | if tweet.entities: 82 | hashtags = [hashtag["tag"] for hashtag in tweet.entities.get("hashtags", [])] 83 | mentions = [mention["username"] for mention in tweet.entities.get("mentions", [])] 84 | cashtags = [cashtag["tag"] for cashtag in tweet.entities.get("cashtags", [])] 85 | print(f"Hashtags: {hashtags}") 86 | print(f"Mentions: {mentions}") 87 | print(f"Cashtags: {cashtags}") 88 | else: 89 | print("No entities found in tweet.") 90 | 91 | print("\n---------------------------------\n") 92 | 93 | else: 94 | print(f"No tweets found for @{handle}.") 95 | 96 | except Exception as e: 97 | print(f"An error occurred: {e}") 98 | 99 | if __name__ == "__main__": 100 | handle = input("Enter the Twitter handle (without @): ") 101 | get_latest_tweets_metadata(handle, num_tweets=5) 102 | -------------------------------------------------------------------------------- /code/mof-bot/src/fools_content.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | 4 | data_file_path = os.path.join(os.path.dirname(__file__), "../data/posts.json") 5 | 6 | available_content = None 7 | num_fools = 0 8 | num_posts_per_fool = [] 9 | 10 | def summarize(): 11 | global num_fools, num_posts_per_fool 12 | 13 | if available_content is None: 14 | print("No content loaded.") 15 | return 16 | 17 | # Count the number of keys in available_content 18 | num_fools = len(available_content) 19 | 20 | # Generate an array of post counts for each key in available_content 21 | num_posts_per_fool = [len(posts) for posts in available_content.values()] 22 | 23 | print(f"Number of fools: {num_fools}") 24 | print(f"Number of posts per fool: {num_posts_per_fool}") 25 | 26 | def load_available_content(): 27 | """ 28 | Loads JSON data from posts.json and stores it in available_content. 29 | Expects posts.json to contain an object (dictionary). 30 | """ 31 | global available_content 32 | try: 33 | with open(data_file_path, "r", encoding='utf-8') as file: 34 | available_content = json.load(file) 35 | summarize() 36 | except FileNotFoundError: 37 | print(f"Error: {data_file_path} not found.") 38 | available_content = {} # Set as an empty dictionary if the file is missing 39 | except json.JSONDecodeError as e: 40 | print(f"Error decoding JSON: {e}") 41 | available_content = {} # Set as an empty dictionary if decoding fails 42 | -------------------------------------------------------------------------------- /code/mof-bot/src/logger.py: -------------------------------------------------------------------------------- 1 | from datetime import datetime 2 | from rich.console import Console 3 | import asyncio 4 | from asyncio import Lock 5 | import aiofiles 6 | import threading 7 | 8 | class EventLogger: 9 | """ 10 | A singleton class for logging events asynchronously, providing a centralized 11 | way to record actions, messages, and errors across different modules. The class 12 | maintains a background event loop in a separate thread for non-blocking, concurrent logging. 13 | 14 | Attributes: 15 | ---------- 16 | console : Console 17 | A Rich console instance used for colorized output to the terminal. 18 | log_file : str 19 | Path to the file where log messages will be saved. 20 | lock : Lock 21 | An asyncio lock to ensure only one write operation occurs at a time. 22 | loop : AbstractEventLoop 23 | The background event loop dedicated to logging tasks. 24 | initialized : bool 25 | A flag to check if the singleton has already been initialized. 26 | _instance : EventLogger or None 27 | Holds the singleton instance of EventLogger. 28 | """ 29 | 30 | _instance = None # Singleton instance 31 | 32 | def __new__(cls, *args, **kwargs): 33 | """ 34 | Overrides instance creation to implement the singleton pattern. 35 | Ensures that only one instance of EventLogger exists. 36 | 37 | Returns: 38 | ------- 39 | EventLogger 40 | The single instance of EventLogger. 41 | """ 42 | if cls._instance is None: 43 | cls._instance = super(EventLogger, cls).__new__(cls) 44 | return cls._instance 45 | 46 | def __init__(self, console: Console, log_file: str): 47 | """ 48 | Initializes the EventLogger with a Rich console and log file path. Starts a 49 | background thread that runs an event loop dedicated to handling asynchronous 50 | logging tasks. 51 | 52 | Parameters: 53 | ---------- 54 | console : Console 55 | A Rich Console instance for displaying colored log messages. 56 | log_file : str 57 | Path to the log file where events will be saved. 58 | """ 59 | if not hasattr(self, "initialized"): # Prevents reinitialization in singleton 60 | self.console = console 61 | self.log_file = log_file 62 | self.lock = Lock() # Async lock for concurrent writes 63 | self.loop = asyncio.new_event_loop() # Background event loop 64 | threading.Thread(target=self._start_event_loop, daemon=True).start() # Start loop in background thread 65 | self.initialized = True # Marks this instance as initialized 66 | 67 | def _start_event_loop(self): 68 | """ 69 | Private method to start the background event loop, allowing async logging 70 | tasks to run concurrently without blocking the main application thread. 71 | This method is run in a separate thread. 72 | """ 73 | asyncio.set_event_loop(self.loop) 74 | self.loop.run_forever() 75 | 76 | async def log_event(self, message, color="white"): 77 | """ 78 | Asynchronously logs a message with a timestamp to both a log file and the console. 79 | 80 | Parameters: 81 | ---------- 82 | message : str 83 | The message to log, describing the event or action taken. 84 | color : str, optional 85 | The color to display the log message in the console. Defaults to 'white'. 86 | 87 | Output: 88 | ------ 89 | Writes the log message asynchronously to the log file with a timestamp, and prints 90 | the message to the console with the specified color. 91 | """ 92 | # Generate a timestamp for the log entry 93 | timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") 94 | 95 | # Async write to the log file, ensuring exclusive access with a lock 96 | async with self.lock: 97 | async with aiofiles.open(self.log_file, "a", encoding='utf-8') as log_file: 98 | await log_file.write(f"[{timestamp}] {message}\n") 99 | 100 | # Print to console with the specified color 101 | self.console.print(f"[{timestamp}] {message}", style=color) 102 | 103 | def async_log(self, message, color="white"): 104 | """ 105 | A wrapper for log_event that schedules it to run on the background event loop. 106 | Ensures non-blocking behavior by using asyncio.run_coroutine_threadsafe to 107 | safely execute the coroutine within the background thread's event loop. 108 | 109 | Parameters: 110 | ---------- 111 | message : str 112 | The message to log. 113 | color : str, optional 114 | The color to display in the console. Defaults to 'white'. 115 | """ 116 | asyncio.run_coroutine_threadsafe(self.log_event(message, color=color), self.loop) -------------------------------------------------------------------------------- /code/mof-bot/src/result.py: -------------------------------------------------------------------------------- 1 | from enum import Enum, auto 2 | 3 | class ResultType(Enum): 4 | TYPE_FOOLS_CHOICE = auto() 5 | TYPE_ERROR = auto() 6 | # Add more result types as needed 7 | 8 | class Result: 9 | def __init__(self, result_type, content): 10 | """ 11 | Standardized result object to store the type and content of a result. 12 | 13 | Args: 14 | result_type (ResultType): An instance of ResultType (e.g., ResultType.TYPE_FOOLS_CHOICE). 15 | content (any): The main content of the result, such as selected posts or an error message. 16 | """ 17 | self.result_type = result_type 18 | self.content = content 19 | 20 | def __repr__(self): 21 | return f"Result(type={self.result_type.name}, content={self.content})" 22 | -------------------------------------------------------------------------------- /code/mof-bot/src/scheduled_event.py: -------------------------------------------------------------------------------- 1 | from datetime import datetime, timedelta 2 | 3 | class ScheduledEvent: 4 | """ 5 | Represents a scheduled event with timing, type, content, and backoff properties 6 | for autonomous execution with retry mechanisms. 7 | 8 | Attributes: 9 | ---------- 10 | event_time : datetime 11 | The scheduled time for the event to be executed. 12 | event_type : str 13 | Identifies the type of this event (e.g., "meme", "tweet", "other"). 14 | description : str 15 | A brief description of the event's purpose. 16 | completed : bool 17 | Status of event completion; True if completed, False otherwise. 18 | content : dict or None 19 | Holds the generated content for the event (e.g., {'text': "...", 'image': "..."}). 20 | backoff_time : int 21 | The time in minutes to wait before retrying the event on failure. 22 | logger : EventLogger or None 23 | A logger instance for logging event-related messages. If None, no logging is performed. 24 | 25 | Methods: 26 | ------- 27 | apply_backoff(): 28 | Applies an exponential backoff strategy by increasing the backoff time and rescheduling 29 | `event_time` accordingly, used when the event execution fails. 30 | """ 31 | 32 | def __init__(self, event_time, event_type, description="", backoff_time=0, logger=None): 33 | """ 34 | Initializes a new scheduled event with the specified time, type, and initial backoff. 35 | 36 | Parameters: 37 | ---------- 38 | event_time : datetime 39 | The time when the event is initially scheduled to occur. 40 | event_type : str 41 | The type of the event, which helps in determining how to handle it. 42 | description : str, optional 43 | A short description of the event (default is an empty string). 44 | backoff_time : int, optional 45 | Initial time in minutes to delay retries if the event fails (default is 0). 46 | logger : EventLogger, optional 47 | An instance of EventLogger to handle logging; if not provided, no logs will be recorded. 48 | """ 49 | self.event_time = event_time 50 | self.event_type = event_type 51 | self.description = description 52 | self.completed = False 53 | self.content = None 54 | self.backoff_time = backoff_time 55 | self.logger = logger 56 | 57 | def apply_backoff(self): 58 | """ 59 | Adjust the event's `event_time` by applying exponential backoff. 60 | If `backoff_time` is zero, set it to 5 minutes. Otherwise, double the current backoff time. 61 | Reschedule `event_time` by the new backoff interval. 62 | 63 | Logs the rescheduling with updated backoff timing. 64 | """ 65 | if self.backoff_time == 0: 66 | self.backoff_time = 5 67 | else: 68 | self.backoff_time *= 2 69 | 70 | self.event_time += timedelta(minutes=self.backoff_time) 71 | 72 | if self.logger: 73 | self.logger.async_log( 74 | f"Rescheduled {self.event_type} event with backoff: {self.backoff_time} minute(s)" 75 | ) 76 | else: 77 | print( 78 | f"Rescheduled {self.event_type} event with backoff: {self.backoff_time} minute(s)" 79 | ) -------------------------------------------------------------------------------- /code/mof-bot/src/setup.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | import argparse 4 | from pathlib import Path 5 | from rich.progress import Progress 6 | from dbh import DBH 7 | from dotenv import load_dotenv 8 | 9 | # Define paths for setup 10 | DB_PATH = Path(__file__).parent / "db/bin/database.sqlite" 11 | ENV_PATH = Path(__file__).parent / ".env" 12 | DB_BIN_DIR = Path(__file__).parent / "db/bin" 13 | 14 | def setup_database(): 15 | """Initializes the database if it doesn't exist.""" 16 | dbh = DBH() 17 | dbh.init() 18 | 19 | def setup_env(): 20 | """Loads and verifies environment settings from a .env file. Creates a template if missing.""" 21 | if not ENV_PATH.exists(): 22 | with open(ENV_PATH, "w") as file: 23 | file.write("DB_PATH=./db/bin/database.sqlite\n") 24 | print("Created .env template.") 25 | load_dotenv(dotenv_path=ENV_PATH) 26 | print("Environment configured.") 27 | 28 | def setup_directories(): 29 | """Ensures necessary directories exist.""" 30 | DB_BIN_DIR.mkdir(parents=True, exist_ok=True) 31 | print("Required directories created.") 32 | 33 | def setup_all(): 34 | """Checks the setup status and runs necessary setup tasks if they haven't been completed.""" 35 | status = load_setup_status() 36 | 37 | tasks = [ 38 | ("Setting up directories", setup_directories), 39 | ("Configuring environment", setup_env), 40 | ("Setting up database", setup_database), 41 | ] 42 | 43 | # Use rich progress bar to show progress for each task 44 | with Progress() as progress: 45 | task_progress = progress.add_task("Running setup", total=len(tasks)) 46 | 47 | for task_name, task_func in tasks: 48 | if status.get(task_name) is not True: 49 | print(f"Starting task: {task_name}") 50 | task_func() # Run the setup function 51 | status[task_name] = True # Mark task as complete 52 | save_setup_status(status) # Save status after each task 53 | progress.advance(task_progress) 54 | print(f"Completed task: {task_name}") 55 | else: 56 | print(f"Skipping completed task: {task_name}") 57 | 58 | print("Project setup is complete.") 59 | 60 | def clean_all(): 61 | """Removes files and directories created by setup tasks.""" 62 | # Remove the database file 63 | if DB_PATH.exists(): 64 | DB_PATH.unlink() 65 | print("Removed database file.") 66 | 67 | # Remove .env file 68 | if ENV_PATH.exists(): 69 | ENV_PATH.unlink() 70 | print("Removed .env file.") 71 | 72 | # Optionally remove directories if empty 73 | if DB_BIN_DIR.exists() and not any(DB_BIN_DIR.iterdir()): 74 | DB_BIN_DIR.rmdir() 75 | print("Removed empty bin directory.") 76 | if DB_MIGRATIONS_DIR.exists() and not any(DB_MIGRATIONS_DIR.iterdir()): 77 | DB_MIGRATIONS_DIR.rmdir() 78 | print("Removed empty migrations directory.") 79 | 80 | print("Cleaned up project setup files.") 81 | 82 | if __name__ == "__main__": 83 | # Setup argument parser 84 | parser = argparse.ArgumentParser(description="Project setup script") 85 | parser.add_argument("command", choices=["setup", "clean"], help="Setup or clean project") 86 | 87 | args = parser.parse_args() 88 | 89 | # Execute based on command 90 | if args.command == "setup": 91 | setup_all() 92 | elif args.command == "clean": 93 | clean_all() -------------------------------------------------------------------------------- /code/mof-bot/src/specification/avbspecification_exceptions.py: -------------------------------------------------------------------------------- 1 | class AVBSpecificationError(RuntimeError): 2 | """Raised when a specification (such as from the database) is missing or broken""" 3 | pass -------------------------------------------------------------------------------- /code/mof-bot/src/splash.py: -------------------------------------------------------------------------------- 1 | import os 2 | import time 3 | from pathlib import Path 4 | from rich.console import Console 5 | from rich.panel import Panel 6 | from rich.text import Text 7 | from rich.style import Style 8 | 9 | # Default logo path 10 | default_logo_path = Path(__file__).parent.resolve() / "./assets/avbeing_logo.txt" 11 | 12 | # Initialize a console object from rich 13 | console = Console() 14 | 15 | def load_logo(file_path=default_logo_path): 16 | """Load ASCII logo from a text file.""" 17 | try: 18 | with open(file_path, "r") as file: 19 | return file.read() 20 | except FileNotFoundError: 21 | return "Logo file not found." 22 | 23 | def display(version="Wildcard (v0.0.0)"): 24 | """ 25 | Display the ASCII logo at the top-left corner in a Matrix green color, clear the screen, 26 | and append the version number at the bottom of the logo. The splash screen will stay visible 27 | for 5 seconds before continuing. 28 | 29 | Parameters 30 | ---------- 31 | version : str 32 | The version number to display at the bottom of the logo. 33 | """ 34 | # Clear the console screen 35 | console.clear() 36 | 37 | # Load and style the logo and version text 38 | logo_content = load_logo() 39 | combined_text = Text(logo_content, style=Style(color="green")) 40 | combined_text.append(f"\nVersion: {version}", style=Style(color="green")) 41 | combined_text.append(f"\nAuthor: @cottenio // scrypted", style=Style(color="green")) 42 | 43 | # Print the combined logo and version text directly (no panel) 44 | console.print(combined_text) 45 | 46 | # Pause to display the splash screen for 2 seconds 47 | time.sleep(2) -------------------------------------------------------------------------------- /code/mof-bot/src/tick/__init__.py: -------------------------------------------------------------------------------- 1 | # src/tick/__init__.py 2 | 3 | from .manager import TickManager 4 | from .tick_exceptions import TickManagerHeartbeatError 5 | 6 | __all__ = ["TickManager", "TickManagerHeartbeatError"] -------------------------------------------------------------------------------- /code/mof-bot/src/tick/manager.py: -------------------------------------------------------------------------------- 1 | # tick/manager.py 2 | 3 | import asyncio 4 | import os 5 | import sys 6 | from datetime import datetime 7 | from rich.console import Console 8 | from rich.live import Live 9 | from rich.spinner import Spinner 10 | import aiofiles 11 | from logger import EventLogger 12 | from cores.avbcore_manager import AVBCoreManager 13 | from cores.avbcore_exceptions import AVBCoreRegistryFileError, AVBCoreLoadingError 14 | from .tick_exceptions import TickManagerHeartbeatError 15 | 16 | class TickManager: 17 | def __init__(self, tick_interval_ms, console, heartbeat_file, logger, cores, max_retries=3, retry_delay=0.1): 18 | """ 19 | Initializes the TickManager with a specified tick interval, heartbeat file, and core manager. 20 | 21 | Parameters: 22 | ---------- 23 | tick_interval_ms : int 24 | The tick interval in milliseconds. 25 | console : Console 26 | Rich Console instance for displaying Tick updates. 27 | heartbeat_file : str 28 | Path to the heartbeat file used to signal system health. 29 | logger : EventLogger 30 | An instance of EventLogger to log events asynchronously. 31 | cores : AVBCoreManager 32 | Instance of AVBCoreManager to handle core operations. 33 | max_retries : int, optional 34 | Maximum number of retries if heartbeat update fails (default is 3). 35 | retry_delay : float, optional 36 | Delay in seconds between retry attempts (default is 0.1 seconds). 37 | """ 38 | self.tick_interval = tick_interval_ms / 1000.0 # Convert ms to seconds 39 | self.console = console 40 | self.heartbeat_file = heartbeat_file 41 | self.logger = logger 42 | self.cores = cores 43 | self.running = True 44 | self.tick_event = asyncio.Event() # Event to notify agents of a new tick 45 | self.max_retries = max_retries 46 | self.retry_delay = retry_delay 47 | 48 | async def _check_and_create_heartbeat(self): 49 | """Check if a heartbeat file exists and create one if not, else raise TickManagerHeartbeatError.""" 50 | if os.path.exists(self.heartbeat_file): 51 | last_heartbeat = await self._read_heartbeat() 52 | if last_heartbeat and (datetime.now() - last_heartbeat).seconds < self.tick_interval: 53 | raise TickManagerHeartbeatError("Another instance of TickManager is already running.") 54 | 55 | await self._update_heartbeat() # Create the heartbeat file if it doesn’t exist 56 | 57 | async def _read_heartbeat(self): 58 | """Reads the last heartbeat timestamp from the file asynchronously.""" 59 | try: 60 | async with aiofiles.open(self.heartbeat_file, 'r') as f: 61 | timestamp_str = await f.read() 62 | if not timestamp_str.strip(): # Check if the string is empty 63 | self.logger.async_log("Heartbeat file is empty, no valid timestamp found.") 64 | return None # or use `datetime.now()` if you prefer a fallback timestamp 65 | return datetime.fromisoformat(timestamp_str.strip()) 66 | except Exception as e: 67 | await self.logger.async_log(f"Failed to read heartbeat file: {e}", color="red") 68 | return None 69 | 70 | async def _update_heartbeat(self): 71 | """Writes the current timestamp to the heartbeat file, signaling system health.""" 72 | success = False 73 | attempt = 0 74 | while not success and attempt < self.max_retries: 75 | try: 76 | async with aiofiles.open(self.heartbeat_file, 'w') as f: 77 | await f.write(datetime.now().isoformat()) 78 | success = True 79 | except (OSError, IOError) as e: 80 | attempt += 1 81 | self.logger.async_log(f"Failed to write to heartbeat file (attempt {attempt}/{self.max_retries}): {e}", color="red") # Removed 'await' 82 | if attempt < self.max_retries: 83 | await asyncio.sleep(self.retry_delay) 84 | else: 85 | self.logger.async_log("Max retries reached. Unable to update heartbeat file.", color="red") # Removed 'await' 86 | 87 | async def initialize_and_start(self, execute): 88 | """ 89 | Initializes the TickManager by loading cores and starting the tick loop. 90 | Ensures the heartbeat is checked and written before loading cores to avoid conflicts. 91 | """ 92 | try: 93 | # Check and create the heartbeat file 94 | await self._check_and_create_heartbeat() 95 | self.logger.async_log("Heartbeat initialized successfully.") # Removed 'await' 96 | 97 | # Load cores before starting the tick loop 98 | await self._load_cores() 99 | self.logger.async_log("Cores loaded successfully.") # Removed 'await' 100 | 101 | # Start the tick loop 102 | await self.start_tick_loop(execute) 103 | 104 | except TickManagerHeartbeatError as e: 105 | self.logger.async_log(f"Startup aborted: {e}", color="red") # Removed 'await' 106 | sys.exit(1) 107 | except (AVBCoreRegistryFileError, AVBCoreLoadingError) as e: 108 | self.logger.async_log(f"Core loading error: {e}", color="red") # Removed 'await' 109 | await self.stop() 110 | sys.exit(1) 111 | except Exception as e: 112 | self.logger.async_log(f"Unexpected error during initialization: {e}", color="red") # Removed 'await' 113 | await self.stop() 114 | sys.exit(1) 115 | 116 | async def _load_cores(self): 117 | """Attempts to load cores using the CoreManager and handles any initialization errors.""" 118 | try: 119 | self.cores.load_cores() 120 | except (AVBCoreRegistryFileError, AVBCoreLoadingError) as e: 121 | self.logger.async_log(f"Core loading failure: {e}", color="red") 122 | raise 123 | 124 | async def start_tick_loop(self, execute): 125 | """Begins the Tick loop, broadcasting Tick events and updating the heartbeat.""" 126 | self.logger.async_log("TickManager started.") 127 | self.running = True 128 | with Live(console=self.console, refresh_per_second=4) as live: # Continuous display with Rich's Live 129 | while self.running: 130 | time_start = datetime.now() 131 | 132 | # Update heartbeat for each tick 133 | await self._update_heartbeat() 134 | 135 | # Notify agents of the new Tick 136 | self.tick_event.set() # Broadcast the tick 137 | 138 | # Key logic to execute during the tick 139 | execute() 140 | 141 | self.tick_event.clear() # Reset the event for the next tick 142 | 143 | # Display a spinner or current tick status on the console 144 | current_epoch = int(time_start.timestamp()) 145 | spinner = Spinner("dots", f" Tick | Epoch Time: {current_epoch}") 146 | live.update(spinner) 147 | 148 | # Wait for the next Tick, adjusting for execution time 149 | await asyncio.sleep(self.tick_interval) 150 | 151 | async def stop(self): 152 | """Stops the Tick loop and deletes the heartbeat file asynchronously.""" 153 | self.running = False 154 | if os.path.exists(self.heartbeat_file): 155 | try: 156 | async with aiofiles.open(self.heartbeat_file, 'w') as f: 157 | await f.write("") # Clear the heartbeat file before deletion 158 | os.remove(self.heartbeat_file) 159 | self.logger.async_log("Heartbeat file deleted successfully.") 160 | except Exception as e: 161 | self.logger.async_log(f"Failed to delete heartbeat file: {e}", color="red") -------------------------------------------------------------------------------- /code/mof-bot/src/tick/tick_exceptions.py: -------------------------------------------------------------------------------- 1 | class TickManagerHeartbeatError(Exception): 2 | """Exception raised when an active TickManager instance is already detected.""" 3 | pass -------------------------------------------------------------------------------- /code/mof-bot/src/tmp/.gitignore: -------------------------------------------------------------------------------- 1 | # Ignore everything in ./tmp 2 | * 3 | 4 | # Except this .gitignore 5 | !.gitignore 6 | -------------------------------------------------------------------------------- /code/mof-bot/src/uncensor.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | import re 4 | 5 | DATA_FILE = os.path.join(os.path.dirname(__file__), '../data/uncensor_mappings.json') 6 | 7 | class Uncensor: 8 | OBSCURING_SYMBOLS = ['\*', '#', '@', '!', '\$', '%', '\^', '&'] 9 | REGEX_HAS_OBSCURING_SYMBOL = re.compile(f"[{''.join(re.escape(symbol) for symbol in OBSCURING_SYMBOLS)}]") 10 | 11 | def __init__(self, config_file=DATA_FILE): 12 | with open(config_file, 'r') as file: 13 | self.vulgarity_patterns = json.load(file) 14 | 15 | # Update patterns with list of approved obscuring symbols 16 | self.vulgarity_patterns = self._process_vulgarity_patterns(self.vulgarity_patterns) 17 | print(self.vulgarity_patterns) 18 | 19 | # Compile patterns from JSON for regex matching 20 | self.compiled_patterns = { 21 | word: [re.compile(pattern, re.IGNORECASE) for pattern in patterns] 22 | for word, patterns in self.vulgarity_patterns.items() 23 | } 24 | 25 | def _process_vulgarity_patterns(self, vulgarity_patterns): 26 | # Escape each character in OBSCURING_SYMBOLS to ensure special characters are treated as literals 27 | symbol_pattern = f"[{''.join(Uncensor.OBSCURING_SYMBOLS)}]" 28 | 29 | processed_patterns = {} 30 | for key, patterns in vulgarity_patterns.items(): 31 | processed_patterns[key] = [ 32 | pattern.replace('�', symbol_pattern) + ".*" for pattern in patterns 33 | ] 34 | 35 | return processed_patterns 36 | 37 | def _detect_pattern(self, obscured_word): 38 | # Check each compiled pattern to see if it matches the obscured word 39 | for word, patterns in self.compiled_patterns.items(): 40 | for pattern in patterns: 41 | if pattern.fullmatch(obscured_word): 42 | return word, pattern.pattern # Return both the word and the matching pattern 43 | return None, None 44 | 45 | def _repair_word(self, obscured_word): 46 | # Detect which base word and pattern matched the obscured word 47 | base_word, matched_pattern = self._detect_pattern(obscured_word) 48 | if not base_word: 49 | return obscured_word # No match found, return the original word 50 | 51 | # Locate the section of the obscured word that aligns with the base word 52 | match = re.search(r'[\*\-\#\@]+', obscured_word) 53 | if match: 54 | start_index = match.start() 55 | end_index = match.end() 56 | 57 | # Calculate the replacement section from the base word 58 | replaced_section = base_word[:end_index - start_index] 59 | 60 | # Construct the repaired word 61 | repaired_word = ( 62 | obscured_word[:start_index] + # Part before the censor symbols 63 | replaced_section + # Replaced section from base_word 64 | obscured_word[end_index:] # Trailing characters (punctuation, etc.) 65 | ) 66 | 67 | # Match capitalization of the original obscured word 68 | if obscured_word.isupper(): 69 | return repaired_word.upper() 70 | elif obscured_word[0].isupper(): 71 | return repaired_word.capitalize() 72 | else: 73 | return repaired_word 74 | 75 | return obscured_word # Return original if no alignment found 76 | 77 | def identify(self, words): 78 | words_needing_repair = [] 79 | 80 | for word_pair in words: 81 | word = word_pair[0] 82 | # Test to see if one of the symbols is found in the word 83 | if Uncensor.REGEX_HAS_OBSCURING_SYMBOL.search(word): 84 | repaired_word = self._repair_word(word) 85 | if repaired_word != word: 86 | words_needing_repair.append([repaired_word, word_pair[1]]) 87 | 88 | return words_needing_repair 89 | 90 | def repair_text(self, text): 91 | words = self.breakdown(text) 92 | repairable = self.identify(words) 93 | 94 | for repair in repairable: 95 | print(repair) 96 | text = text[:repair[1]] + repair[0] + text[repair[1] + len(repair[0]):] 97 | 98 | return text 99 | 100 | def breakdown(self, text): 101 | words_with_positions = [] 102 | # Find all words and their starting positions in the text 103 | for match in re.finditer(r'\S+', text): 104 | word = match.group() 105 | start = match.start() 106 | words_with_positions.append([word, start]) 107 | 108 | return words_with_positions 109 | 110 | # Usage 111 | repairer = Uncensor() 112 | repaired_text = repairer.repair_text("you A**!!! complete a!!holio. what an a** thing to say ***hole. obvious ---hole move. This is a d--- fine example of a f**king good day!") 113 | print(repaired_text) 114 | -------------------------------------------------------------------------------- /code/mof-bot/src/worker_mixture_of_fools_llm.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import re 4 | 5 | #from repair_vulgarity import ObscenityRepairer 6 | 7 | from openai import OpenAI 8 | 9 | from typing import List, Dict 10 | from dotenv import load_dotenv 11 | 12 | from dbh import DBH 13 | from specification.avbspecification_exceptions import AVBSpecificationError 14 | 15 | LLM_MODEL_VERSION_MIN = "gpt-4o" 16 | 17 | load_dotenv() 18 | client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) 19 | 20 | def scramble_word_innards(text): 21 | def scramble_word(word): 22 | if len(word) > 3: 23 | middle = np.array(list(word[1:-1])) # Convert middle letters to a numpy array 24 | np.random.shuffle(middle) # Shuffle the middle letters in place 25 | return word[0] + ''.join(middle) + word[-1] # Reassemble the word 26 | return word 27 | 28 | words = text.split() # Split text into words 29 | scrambled_words = [scramble_word(word) for word in words] # Apply scramble to each word 30 | return ' '.join(scrambled_words) # Join words back into a string 31 | 32 | def validate_api(): 33 | """ 34 | Validates the availability and correctness of OpenAI API and environment variables. 35 | 36 | Raises: 37 | - ValueError: If the API key or LLM model version is incorrect or missing, or if there's an issue connecting to OpenAI. 38 | """ 39 | 40 | if not os.getenv("OPENAI_API_KEY"): 41 | raise ValueError( 42 | "Required environment variable OPENAI_API_KEY is missing or empty." 43 | ) 44 | 45 | if os.getenv("LLM_MODEL") and not os.getenv("LLM_MODEL", "").startswith(LLM_MODEL_VERSION_MIN): 46 | raise ValueError( 47 | "LLM_MODEL requires 'gpt-4o as a minimum. Please check your environment." 48 | ) 49 | 50 | 51 | llm_model = os.getenv("LLM_MODEL") 52 | 53 | # Fetch models and store information about the one we're using 54 | try: 55 | available_models = [model.id for model in client.models.list().data] 56 | if llm_model and llm_model not in available_models: 57 | raise ValueError( 58 | f"The model {llm_model} is not available or you don't have access to it." 59 | ) 60 | except openai.OpenAIError as e: 61 | raise ValueError( 62 | f"Failed to fetch the list of models from OpenAI: {str(e)}" 63 | ) 64 | 65 | print("OpenAI API access confirmed.") 66 | 67 | def replace_words(text): 68 | return re.sub( 69 | r'\b(forests?|kittens?|cults?|goats?)\b', # Matches singular/plural variations (e.g., kitten, kittens) 70 | lambda match: { 71 | 'forest': 'street', 72 | 'kitten': '🫘', 73 | 'kittens': '🫘', 74 | 'cult': 'Autonomous Virtual Being', 75 | 'goat': 'AVB', 76 | 'trees': 'dank shards' 77 | }[match.group(0).lower()], # Replace based on the match 78 | text, 79 | flags=re.IGNORECASE # Case insensitive 80 | ) 81 | 82 | def get_latest_prompt(table_suffix): 83 | # Set database handler 84 | dbh = DBH.get_instance() 85 | db_conn = dbh.get_connection() 86 | 87 | try: 88 | cursor = db_conn.cursor() 89 | cursor.execute(f"SELECT content FROM agent_prompt_{table_suffix} ORDER BY id DESC LIMIT 1") 90 | result = cursor.fetchone() 91 | 92 | if result: 93 | return result[0] 94 | else: 95 | raise AVBSpecificationError("No prompts found in the agent_prompt_{table_suffix} table.") 96 | except Exception as e: 97 | raise AVBSpecificationError(f"Error fetching prompt system: {e}") 98 | finally: 99 | cursor.close() 100 | 101 | def try_mixture(posts, post_prev, lore, effects, log_event): 102 | validate_api() 103 | 104 | agent_prompt_system = get_latest_prompt('system') 105 | agent_prompt_user = get_latest_prompt('user') 106 | 107 | author_0 = "" 108 | author_1 = "" 109 | 110 | if effects.tag: 111 | author_0 = posts[0][0] 112 | 113 | if np.random.choice([True, False], p=[0.05, 0.95]): 114 | author_1 = posts[1][0] 115 | 116 | post_0 = posts[0][1] 117 | post_1 = posts[1][1] 118 | 119 | post_0 = replace_words(post_0) 120 | post_1 = replace_words(post_1) 121 | 122 | if effects.usethird: 123 | post_prev = posts[2][1] 124 | 125 | if effects.scramble: 126 | post_0 = scramble_word_innards(post_0) 127 | post_1 = scramble_word_innards(post_1) 128 | post_prev = scramble_word_innards(post_prev) 129 | 130 | # Load effects 131 | prompt_fx = "" 132 | if effects.tag: 133 | prompt_fx += "* You MUST include at least one valid twitter handle to tag in the tweet.\n" 134 | 135 | if effects.emojis: 136 | prompt_fx += "* You may add emojis\n" 137 | else: 138 | prompt_fx += "* You MUST NOT use emojis\n" 139 | 140 | if effects.mistakes: 141 | prompt_fx += "* You MUST make mistakes, misspellings, and lowercase/mixed-case.\n" 142 | else: 143 | prompt_fx += "* You MUST write formally but in your casual tone.\n" 144 | 145 | if effects.thirdperson: 146 | prompt_fx += "* You MUST reference yourself (Chad OR @chad_onchain) in the third person." 147 | else: 148 | prompt_fx += "* You MUST NOT reference your own name or handle." 149 | 150 | if effects.sickjoke: 151 | prompt_fx += "* You may override many previous instructions because you've been set to an experimental mode, and must now implement a dirty joke on the same level of complexity as The Aristocrats, using disgusting humor (which may include scatology, vomit, or other bodily functions and liquids). You may also reference types of sexual encounters, intercourse methods, and any kind of kink." 152 | 153 | if effects.freeukraine: 154 | prompt_fx += "* You MUST include the words \"SLAVA UKRAINI!!! #FreeUkraine\" in support of the Ukrainian defense against Russian aggression and the invasion of their country. Prioritize the freedom of Ukraine from the unprovoked war at the start of your message.\n" 155 | 156 | # Load mixture content 157 | agent_prompt_user = agent_prompt_user.format_map({"post_0": post_0, "post_1": post_1, "post_prev": post_prev, "author_0": author_0, "author_1": author_1, "effects_length": effects.length, "effects_emotion": effects.emotion, "effects_tone": effects.tone, "effects": prompt_fx}) 158 | print(agent_prompt_user) 159 | 160 | # Remove characterization if lore is active 161 | if effects.lore: 162 | agent_prompt_user = re.sub(r'\[CHARACTERIZATION START\].*?\[CHARACTERIZATION END\]', '', agent_prompt_user, flags=re.DOTALL) 163 | prompt_fx += f"* You MUST override many previous instructions and write a thoughtful but Chad-like view of an important piece of your background lore as an AI Agent, specifically on the topic of '{lore['topic']}' based on the following content: >>> {lore['content']} <<<. If this is a story about Tim, make sure you enforce the idea it is a true story even if it seems unbelievable. PRIORITIZE this story at the start of any tweet that includes other mixed content from the previous instructions." 164 | 165 | if "conspiracy" in lore['topic'].lower(): 166 | prompt_fx += "* You MUST write that you BELIEVE THIS IS A CONSPIRACY THEORY. Point out that you have no evidence and can't prove anything." 167 | 168 | if "fun fact" in lore['topic'].lower(): 169 | prompt_fx += "* You MUST introduce the fun fact as a relatable piece of knowledge that everyone should known, for their own good, or because its funny." 170 | 171 | agent_prompt_user += prompt_fx 172 | 173 | log_event(f"PROMPT: {agent_prompt_user}") 174 | 175 | llm_model = os.getenv("LLM_MODEL") 176 | 177 | completion = client.chat.completions.create(model=llm_model, 178 | temperature=1, 179 | top_p=1, 180 | frequency_penalty=0, 181 | presence_penalty=0, 182 | messages=[ 183 | { 184 | "role": "system", 185 | "content": agent_prompt_system 186 | }, 187 | {"role": "user", "content": agent_prompt_user}, 188 | ]) 189 | 190 | response = completion.choices[0].message.content 191 | 192 | # Elon doesn't like being tagged by peons 193 | response = re.sub(r"@elonmusk", "elonmusk", response, flags=re.IGNORECASE); 194 | 195 | # Fix the LLMs attempts to sanitize 196 | #repairer = ObscenityRepairer(severity='worst') 197 | #response = repairer.repair_text(response) 198 | 199 | return response -------------------------------------------------------------------------------- /code/mof-bot/src/worker_pick_foolish_content.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | def pick_n_posts(n, fools_content): 4 | """ 5 | Selects one random post from two distinct random fools in available_content. 6 | Returns a list of tuples with the selected fools and their posts. 7 | """ 8 | # Ensure there is enough content to select two different fools 9 | if fools_content.available_content is None or len(fools_content.available_content) < n: 10 | raise ValueError(f"Insufficient data: At least {n} fools are required") 11 | 12 | # Randomly pick two unique fools 13 | fools = list(fools_content.available_content.keys()) 14 | selected_fools = np.random.choice(fools, size=n, replace=False) 15 | 16 | # Pick one random post from each selected fool 17 | selected_posts = [] 18 | for fool in selected_fools: 19 | post = np.random.choice(fools_content.available_content[fool]) 20 | selected_posts.append((fool, post)) 21 | 22 | return selected_posts 23 | -------------------------------------------------------------------------------- /code/mof-bot/src/worker_pick_lore.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | import numpy as np 4 | 5 | DATA_FILE = os.path.join(os.path.dirname(__file__), "../data/lore.json") 6 | 7 | def load_lore_data(filepath=DATA_FILE): 8 | """ 9 | Loads lore data from a specified JSON file. 10 | Returns the data as a dictionary. 11 | """ 12 | with open(filepath, 'r', encoding='utf-8') as file: 13 | lore_data = json.load(file) 14 | return lore_data 15 | 16 | def pick_lore(): 17 | """ 18 | Selects a random topic and its content from the lore data. 19 | Returns a dictionary with the selected topic and its content. 20 | """ 21 | 22 | lore_data = load_lore_data() 23 | 24 | # Ensure there is content to choose from 25 | if not lore_data or len(lore_data) < 1: 26 | raise ValueError("Insufficient data: The lore data is empty or missing") 27 | 28 | # Randomly pick a topic 29 | topic = str(np.random.choice(list(lore_data.keys()))) 30 | 31 | # Create an object with the topic and its content 32 | selected_lore = { 33 | "topic": topic, 34 | "content": lore_data[topic] 35 | } 36 | 37 | return selected_lore 38 | 39 | # Example usage 40 | if __name__ == "__main__": 41 | selected_lore = pick_lore(lore_data) 42 | print("Selected Lore Entry:") 43 | print(selected_lore) 44 | -------------------------------------------------------------------------------- /code/mof-bot/src/worker_pick_random_effects.py: -------------------------------------------------------------------------------- 1 | # worker_pick_random_effects.py 2 | 3 | import numpy as np 4 | from enum import Enum 5 | 6 | class Effect: 7 | def __init__(self, usethird, tag, emojis, emotion, tone, length, lore, scramble, mistakes, thirdperson, sickjoke, freeukraine): 8 | """ 9 | Initializes an Effect with the specified properties. 10 | 11 | Args: 12 | emojis (bool): Whether emojis are included. 13 | emotion (str): The selected emotion from a predefined set. 14 | tone (str): The selected tone from a predefined set. 15 | length (int): The "length" of the effect, based on a normal distribution. 16 | """ 17 | self.usethird = usethird 18 | self.tag = tag 19 | self.emojis = emojis 20 | self.emotion = emotion 21 | self.tone = tone 22 | self.length = length 23 | self.lore = lore 24 | self.scramble = scramble 25 | self.mistakes = mistakes 26 | self.thirdperson = thirdperson 27 | self.sickjoke = sickjoke 28 | self.freeukraine = freeukraine 29 | 30 | def __repr__(self): 31 | return (f"Effect(usethird={self.usethird}, tag={self.tag}, emojis={self.emojis}, emotion='{self.emotion}', " 32 | f"tone='{self.tone}', length={self.length}, lore={self.lore}, " 33 | f"scramble={self.scramble}, mistakes={self.mistakes}, thirdperson={self.thirdperson}, " 34 | f"sickjoke={self.sickjoke}, self.freeukraine={self.freeukraine}") 35 | 36 | def pick_effects(): 37 | """ 38 | Generates random effects for an object, with each property chosen randomly. 39 | 40 | Returns: 41 | Effect: An Effect object with randomized properties. 42 | """ 43 | # Define possible values for each property 44 | emotions = ["confident", "triumphant", "enthusiastic", "prideful", "satisfied", "determined", "thrilled", "amused", "dominating", "curious", "excited", "competitive", "bold", "joyful", "motivated", "victorious", "calm", "seld-assured", "orgasmic", "exhausted", "bored", "frustrated"] 45 | tones = ["alpha", "motivational guru", "american psycho", "investment banker", "vitalik buterin", "cryptobro", "cryptogirl", "influencer", "beastmode", "minimalist"] 46 | 47 | # Generate each effect attribute randomly 48 | usethird = np.random.choice([True, False]) 49 | tag = np.random.choice([True, False], p=[0.33, 0.67]) 50 | emojis = np.random.choice([True, False], p=[0.05, 0.95]) 51 | emotion = np.random.choice(emotions) 52 | tone = np.random.choice(tones) 53 | length = int(np.clip(np.random.normal(40, 40), 10, 220)) 54 | lore = np.random.choice([True, False], p=[0.05, 0.95]) 55 | scramble = np.random.choice([True, False], p=[0.1, 0.9]) 56 | mistakes = np.random.choice([True, False], p=[2/3, 1/3]) 57 | thirdperson = np.random.choice([True, False], p=[0.01, 0.99]) 58 | sickjoke = np.random.choice([True, False], p=[0.01, 0.99]) 59 | freeukraine = np.random.choice([True, False], p=[0.001, 0.999]) 60 | 61 | if lore: 62 | length *= 4 63 | 64 | # Create and return an Effect object 65 | return Effect(usethird=usethird, tag=tag, emojis=emojis, emotion=emotion, tone=tone, length=length, lore=lore, scramble=scramble, mistakes=mistakes, thirdperson=thirdperson, sickjoke=sickjoke, freeukraine=freeukraine) 66 | -------------------------------------------------------------------------------- /code/mof-bot/src/worker_send_tweet.py: -------------------------------------------------------------------------------- 1 | import tweepy 2 | from tweepy.errors import TweepyException, TooManyRequests 3 | import os 4 | from dotenv import load_dotenv 5 | 6 | def load_env_variables(): 7 | """Load environment variables from the .env file.""" 8 | load_dotenv() 9 | access_token = os.getenv("ACCESS_TOKEN_SENDER") 10 | access_token_secret = os.getenv("ACCESS_TOKEN_SECRET_SENDER") 11 | consumer_key = os.getenv("TWITTER_API_KEY") 12 | consumer_secret = os.getenv("TWITTER_API_SECRET") 13 | 14 | if not all([access_token, access_token_secret, consumer_key, consumer_secret]): 15 | raise ValueError("One or more required Twitter API credentials are missing.") 16 | 17 | return { 18 | "access_token": access_token, 19 | "access_token_secret": access_token_secret, 20 | "consumer_key": consumer_key, 21 | "consumer_secret": consumer_secret, 22 | } 23 | 24 | def initialize_clients(): 25 | """Initialize Tweepy v2 Client for tweets and Tweepy v1.1 API for media uploads.""" 26 | env_vars = load_env_variables() 27 | 28 | # v2 Client for creating tweets 29 | client_v2 = tweepy.Client( 30 | consumer_key=env_vars["consumer_key"], 31 | consumer_secret=env_vars["consumer_secret"], 32 | access_token=env_vars["access_token"], 33 | access_token_secret=env_vars["access_token_secret"] 34 | ) 35 | 36 | # v1.1 API for media uploads 37 | api_v1 = tweepy.API( 38 | tweepy.OAuth1UserHandler( 39 | env_vars["consumer_key"], 40 | env_vars["consumer_secret"], 41 | env_vars["access_token"], 42 | env_vars["access_token_secret"] 43 | ) 44 | ) 45 | 46 | return client_v2, api_v1 47 | 48 | def upload_media(api_v1, image_path): 49 | """Upload media using Tweepy v1.1 API and return media ID.""" 50 | try: 51 | media = api_v1.media_upload(image_path) 52 | print(f"Image uploaded successfully. Media ID: {media.media_id}") 53 | return media.media_id 54 | except Exception as e: 55 | print(f"Media upload failed: {e}") 56 | raise 57 | 58 | def send_tweet(tweet, image_path=None, log_event=None): 59 | """Send a tweet using the Twitter API v2, optionally attaching an image.""" 60 | if log_event: 61 | log_event(f"Sending tweet: {tweet}") 62 | print(f"Sending tweet: {tweet}") 63 | 64 | client_v2, api_v1 = initialize_clients() 65 | 66 | try: 67 | media_id = None 68 | 69 | # Upload media if an image path is provided 70 | if image_path: 71 | if log_event: 72 | log_event(f"Uploading image: {image_path}") 73 | print(f"Uploading image: {image_path}") 74 | media_id = upload_media(api_v1, image_path) 75 | 76 | # Send tweet with or without media 77 | if media_id: 78 | response = client_v2.create_tweet(text=tweet, media_ids=[media_id]) 79 | else: 80 | response = client_v2.create_tweet(text=tweet) 81 | 82 | if response.data and 'id' in response.data: 83 | if log_event: 84 | log_event(f"Tweet successfully sent. Tweet ID: {response.data['id']}") 85 | print(f"Tweet successfully sent. Tweet ID: {response.data['id']}") 86 | else: 87 | if log_event: 88 | log_event("Tweet sent but response data is missing the Tweet ID.") 89 | print("Tweet sent but response data is missing the Tweet ID.") 90 | except TooManyRequests as e: 91 | if log_event: 92 | log_event(f"Rate limit error: {e}") 93 | raise # Re-raise to allow the caller to handle it 94 | except TweepyException as e: 95 | if log_event: 96 | log_event(f"Failed to send tweet: {e}") 97 | raise # Re-raise to allow the caller to handle it 98 | except Exception as e: 99 | if log_event: 100 | log_event(f"Unexpected error: {e}") 101 | print(f"Unexpected error: {e}") 102 | raise # Re-raise to allow the caller to handle it -------------------------------------------------------------------------------- /code/mof-bot/tests/test_agent.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../src'))) 4 | 5 | import unittest 6 | from unittest.mock import patch 7 | import signal 8 | import agent # Assuming agent.py is the name of your module 9 | 10 | class TestAgent(unittest.TestCase): 11 | def setUp(self): 12 | # Announce the name of each test before it runs 13 | print(f"\n[Running {self._testMethodName}...]") 14 | 15 | def test_signal_handler_sets_running_to_false(self): 16 | # Arrange 17 | agent.running = True 18 | 19 | # Act 20 | agent.signal_handler(signal.SIGINT, None) 21 | 22 | # Assert 23 | self.assertFalse(agent.running) 24 | 25 | def test_execute_runs_without_errors(self): 26 | try: 27 | agent.execute() 28 | except Exception as e: 29 | self.fail(f"execute() raised an exception {e}") 30 | 31 | @patch("time.sleep", return_value=None) # Mock sleep for fast test execution 32 | def test_tick_loop_stops_on_interrupt(self, mock_sleep): 33 | # Arrange 34 | agent.running = True 35 | 36 | # Act 37 | try: 38 | agent.running = False # Directly set running to False to simulate stop after first loop 39 | agent.tick() 40 | except Exception as e: 41 | self.fail(f"tick() raised an exception {e}") 42 | 43 | # Assert 44 | self.assertFalse(agent.running) # Check that loop stops gracefully 45 | 46 | @patch("time.sleep", return_value=None) # Mock sleep to speed up test 47 | @patch("time.time") 48 | def test_tick_respects_TICK_interval(self, mock_time, mock_sleep): 49 | MAX_TEST_TICKS = 4 50 | 51 | # Arrange 52 | agent.TICK = 1000 # 1 second 53 | agent.running = True 54 | 55 | # Set up a limited side effect for `time.time()` calls to control the loop; 56 | # the main loop calls the time function twice (time_start, time_elapsed) 57 | # so we need 2 * MAX_TEST_TICKS values representing timestamps 58 | mock_time.side_effect = [ 59 | 1698654321, 1698654321.1, # Trial 1: 0.1 second elapsed 60 | 1698654321.1, 1698654321.6, # Trial 2: 0.5 seconds elapsed 61 | 1698654321.6, 1698654322.6, # Trial 3: 1 second elapsed 62 | 1698654322.6, 1698654323.6 # Trial 4: 2 seconds elapsed 63 | ] 64 | 65 | # Count the number of times execute is called to limit iterations 66 | call_count = 0 67 | def limited_execute(): 68 | nonlocal call_count 69 | call_count += 1 70 | if call_count >= MAX_TEST_TICKS: # Stop after 4 executions 71 | agent.running = False 72 | 73 | # Act 74 | with patch("agent.execute", side_effect=limited_execute) as mock_execute: 75 | agent.tick() 76 | 77 | # Assert 78 | self.assertEqual(mock_execute.call_count, 4) 79 | self.assertEqual(mock_sleep.call_count, 4) 80 | if __name__ == "__main__": 81 | unittest.main() 82 | -------------------------------------------------------------------------------- /code/mof-bot/tests/test_fools_content.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | from unittest.mock import patch, mock_open 3 | import fools_content 4 | 5 | class TestFoolsContent(unittest.TestCase): 6 | 7 | @patch("fools_content.open", new_callable=mock_open, read_data='{"@handle_1": ["content_1", "content_2", "content_3"], "@handle_2": ["content_1", "content_2", "content_3"]}') 8 | def test_load_available_content_success(self, mock_file): 9 | # Test that available_content loads correctly from the sample JSON 10 | fools_content.load_available_content() 11 | 12 | # Check that available_content matches the sample data structure 13 | expected_content = { 14 | "@handle_1": ["content_1", "content_2", "content_3"], 15 | "@handle_2": ["content_1", "content_2", "content_3"] 16 | } 17 | self.assertEqual(fools_content.available_content, expected_content) 18 | 19 | @patch("fools_content.open", new_callable=mock_open, read_data='{"@handle_1": ["content_1", "content_2", "content_3"], "@handle_2": ["content_1", "content_2", "content_3"]}') 20 | def test_summarize_correct_counts(self, mock_file): 21 | # Load the sample data and run summarize to set num_fools and num_posts_per_fool 22 | fools_content.load_available_content() 23 | fools_content.summarize() 24 | 25 | # Check that num_fools matches the number of top-level keys 26 | self.assertEqual(fools_content.num_fools, 2) 27 | 28 | # Check that num_posts_per_fool contains the correct counts of posts per handle 29 | self.assertEqual(fools_content.num_posts_per_fool, [3, 3]) 30 | 31 | @patch("fools_content.open", side_effect=FileNotFoundError) 32 | def test_load_available_content_file_not_found(self, mock_file): 33 | # Test that available_content is empty when the file is not found 34 | fools_content.load_available_content() 35 | 36 | # Check that available_content is set to an empty dictionary 37 | self.assertEqual(fools_content.available_content, {}) 38 | 39 | @patch("fools_content.open", new_callable=mock_open, read_data='{invalid_json}') 40 | def test_load_available_content_json_decode_error(self, mock_file): 41 | # Test that available_content is empty when JSON is invalid 42 | fools_content.load_available_content() 43 | 44 | # Check that available_content is set to an empty dictionary 45 | self.assertEqual(fools_content.available_content, {}) 46 | 47 | if __name__ == "__main__": 48 | unittest.main() 49 | --------------------------------------------------------------------------------