LoRA
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Visual UI for Fine-Tuning LLMs on Apple Silicon
Read Full Article: Visual UI for Fine-Tuning LLMs on Apple Silicon![[Project] I built a complete ui for Fine-Tuning LLMs on Mac (MLX) – No more CLI arguments! (Open Source and Non-profit)](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-9427-300x171.png)
A new visual UI has been developed for fine-tuning large language models (LLMs) on Apple Silicon, eliminating the need for complex command-line interface (CLI) arguments. This tool, built using Streamlit, allows users to visually configure model parameters, prepare training data, and monitor training progress in real-time. It supports models like Mistral and Qwen, integrates with OpenRouter for data preparation, and provides sliders for hyperparameter tuning. Additionally, users can test their models in a chat interface and easily upload them to HuggingFace. This matters because it simplifies the fine-tuning process, making it more accessible and user-friendly for those working with machine learning on Apple devices.
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Train Models with Evolutionary Strategies
Read Full Article: Train Models with Evolutionary Strategies
The paper discussed demonstrates that using only 30 random Gaussian perturbations can effectively approximate a gradient, outperforming GRPO on RLVR tasks without overfitting. This approach significantly speeds up training as it eliminates the need for backward passes. The author tested and confirmed these findings by cleaning up the original codebase and successfully replicating the results. Additionally, they implemented LoRA and pass@k training, with plans for further enhancements, encouraging others to explore evolutionary strategies (ES) for training thinking models. This matters because it offers a more efficient method for training models, potentially advancing machine learning capabilities.
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Fine-tuned 8B Model for Quantum Cryptography
Read Full Article: Fine-tuned 8B Model for Quantum Cryptography![[P] Fine-tuned 8B model for Quantum Cryptography](https://www.tweakedgeek.com/wp-content/uploads/2025/12/featured-article-7151-300x171.png)
A fine-tuned 8-billion parameter model has been developed specifically for quantum cryptography, demonstrating significant improvements in domain-specific tasks such as QKD protocols and QBER analysis. The model, based on Nemotron-Cascade-8B-Thinking and fine-tuned using LoRA with 8,213 examples over 1.5 epochs, achieved a final loss of 0.226 and showed a high domain accuracy of 85-95% on quantum key distribution tasks. Despite a general benchmark performance drop of about 5%, the model excels in areas where the base model struggled, utilizing real IBM Quantum experiment data to enhance its capabilities. This advancement is crucial for enhancing the security and efficiency of quantum communication systems.