I built a tiny LLM to demystify how language models work
RepositoryFreeBuilt a ~9M param LLM from scratch to understand how they actually work. Vanilla transformer, 60K synthetic conversations, ~130 lines of PyTorch. Trains in 5 min on a free Colab T4. The fish thinks the meaning of life is food.Fork it and swap the personality for your own character.
Capabilities3 decomposed
interactive language model exploration
Medium confidenceThis capability allows users to interactively explore the inner workings of a tiny language model by providing a simple interface for input and output. It uses a lightweight architecture that emphasizes transparency, enabling users to see how different inputs affect the model's responses. The implementation is designed to be educational, showcasing the mechanics of tokenization, embedding, and generation without the complexity of larger models.
The model's architecture is intentionally simplified to facilitate understanding, contrasting with more opaque, larger models that are less accessible for educational purposes.
More approachable for beginners compared to larger models like GPT-3, which can be overwhelming due to complexity.
tokenization visualization
Medium confidenceThis capability provides a visual representation of how input text is tokenized into smaller units before being processed by the model. It employs a straightforward algorithm that breaks down sentences into tokens, allowing users to see the mapping between text and tokens. This transparency helps demystify the preprocessing step that is often taken for granted in larger models.
Focuses on visualizing the tokenization process, which is often overlooked in other LLM tools that do not provide such clarity.
More intuitive and visual than traditional tokenization libraries that provide only textual output.
model response analysis
Medium confidenceThis capability allows users to analyze the responses generated by the language model in terms of coherence, relevance, and creativity. It uses a simple scoring mechanism based on predefined criteria to evaluate the quality of the output. This feature is designed to help users understand how different inputs can lead to varying quality in responses, fostering a deeper comprehension of model behavior.
Integrates a scoring system that is easy to understand and apply, unlike more complex evaluation frameworks that require extensive setup.
Simpler and more user-friendly than comprehensive NLP evaluation libraries that require deep expertise.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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AI Timeline – 171 LLMs from Transformer (2017) to GPT-5.3
Interactive timeline of every major Large Language Model. Filterable by open/closed source, searchable, 54 organizations tracked.
Best For
- ✓students and educators interested in AI and NLP fundamentals
- ✓developers and learners wanting to grasp tokenization in NLP
- ✓researchers and developers testing language model outputs
Known Limitations
- ⚠Limited to a small model size, which may not represent full-scale LLM behaviors accurately
- ⚠Only supports English text and basic tokenization schemes
- ⚠Scoring is subjective and may not reflect all nuances of language quality
Requirements
Input / Output
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