Fine-Tuning
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Fine-Tuning 7B Models on Free Colab with GRPO + TRL
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A Colab notebook has been developed to enhance reasoning capabilities in 7B+ models using free Colab sessions with a T4 GPU. By leveraging TRL's comprehensive memory optimizations, the setup significantly reduces memory usage by approximately seven times compared to the naive FP16 approach. This advancement makes it feasible to fine-tune large models without incurring costs, providing an accessible option for those interested in experimenting with advanced machine learning techniques. This matters because it democratizes access to powerful AI tools, enabling more people to engage in AI development and research without financial barriers.
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Three-Phase Evaluation for Synthetic Data in 4B Model
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An ongoing series of experiments is exploring evaluation methodologies for small fine-tuned models in synthetic data generation tasks, focusing on a three-phase blind evaluation protocol. This protocol includes a Generation Phase where multiple models, including a fine-tuned 4B model, respond to the same proprietary prompt, followed by an Analysis Phase where each model ranks the outputs based on coherence, creativity, logical density, and human-likeness. Finally, in the Aggregation Phase, results are compiled for overall ranking. The open-source setup aims to investigate biases in LLM-as-judge setups, trade-offs in niche fine-tuning, and the reproducibility of subjective evaluations, inviting community feedback and suggestions for improvement. This matters because it addresses the challenges of bias and reproducibility in AI model evaluations, crucial for advancing fair and reliable AI systems.
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Dynamic Learning Rate Scheduling
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Training a machine learning model often requires adjusting the learning rate as the process progresses. Initially, a larger learning rate is beneficial for rapid progress, but as the model nears optimal performance, a smaller learning rate is necessary for fine-tuning and precise adjustments. Without adapting the learning rate, the model may overshoot the optimal point, causing oscillations and preventing further improvement. Implementing a learning rate schedule can significantly enhance model performance, potentially increasing accuracy from 85 percent to 95 percent with the same model and data. This matters because it can lead to more efficient training and better-performing models in machine learning applications.
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Fine-tuned 8B Model for Quantum Cryptography
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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.
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Framework for RAG vs Fine-Tuning in AI Models
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To optimize AI model performance, start with prompt engineering, as it is cost-effective and immediate. If a model requires access to rapidly changing or private data, Retrieval-Augmented Generation (RAG) should be employed to bridge knowledge gaps. In contrast, fine-tuning is ideal for adjusting the model's behavior, such as improving its tone, format, or adherence to complex instructions. The most efficient systems in the future will likely combine RAG for content accuracy and fine-tuning for stylistic precision, maximizing both knowledge and behavior capabilities. This matters because it helps avoid unnecessary expenses and enhances AI effectiveness by using the right approach for specific needs.