A developer, identified only by the handle "FareedKhan-dev" on GitHub, has put forth claims of training a 235-million parameter Large Language Model (LLM) from scratch. The details, disseminated through repositories titled "train-llm-from-scratch" and "create-million-parameter-llm-from-scratch," suggest a focus on fundamental model architecture and training processes. This development, if substantiated, could represent a notable stride in making sophisticated language model development more accessible.
The developer's work appears to explore various architectural components fundamental to modern LLMs. One repository details a Transformer model class, outlining core elements like embedding layers and a training loop structure. Another repository, "create-million-parameter-llm-from-scratch," delves into specifics of model construction, including the integration of RMSNorm, Rotary Positional Embeddings (RoPE), and Masked Multi Head Attentions. This specific exploration resulted in a model with approximately 60,000 parameters, demonstrating iterative refinement.
Parameter Count and Model Scale
The claimed 235-million parameter count places this development in a distinct category, though still considerably smaller than some industry giants. For comparison, Alibaba's Qwen3‑235B‑A22B‑Instruct‑2507, released in July 2025, is an instruction-tuned LLM with 235 billion parameters, though only 22 billion are activated per token. This highlights a significant disparity in scale, with the anonymous developer's model being orders of magnitude smaller.
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Large Language Models, as described in a recent survey, are neural network-based statistical models trained on vast text corpora. They are categorized as 'Foundation', 'Instruction', or 'Chat' models based on their fine-tuning. The concept of parameters, essentially the learned values within a neural network, is central to a model's capacity. A publication from January 2026 notes that a smaller model trained on substantial data can potentially outperform a larger one with less data. The anonymous developer's assertion implies a potential pathway for achieving robust performance without requiring colossal parameter counts.
The practical implications of such a development hinge on the actual performance and capabilities of the trained model, which remain undisclosed. While the code repositories offer a glimpse into the technical underpinnings, the efficacy of the trained 235-million parameter model against established benchmarks is yet to be verified. The journey from raw data to a functional LLM, as outlined by the developer's work, involves stages from data preparation to text generation, encompassing fundamental neural network operations.
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