Qwen3-30B
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Qwen3-30B Model Runs on Raspberry Pi in Real Time
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The ShapeLearn GGUF release introduces the Qwen3-30B-A3B-Instruct-2507 model, which runs efficiently on small hardware like a Raspberry Pi 5 with 16GB RAM, achieving 8.03 tokens per second while maintaining 94.18% of BF16 quality. Instead of focusing solely on reducing model size, the approach optimizes for tokens per second (TPS) without sacrificing output quality, revealing that different quantization formats impact performance differently on CPUs and GPUs. On CPUs, smaller models generally run faster, while on GPUs, performance is influenced by kernel choices, with certain configurations offering optimal results. Feedback and testing from the community are encouraged to further refine evaluation processes and adapt the model for various setups and workloads. This matters because it demonstrates the potential for advanced AI models to run efficiently on consumer-grade hardware, broadening accessibility and application possibilities.
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Training AI Co-Scientists with Rubric Rewards
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Meta has introduced a scalable method to train AI systems to aid scientists in reaching their research objectives by leveraging large language models (LLMs) to extract research goals and grading rubrics from scientific literature. These rubrics are then used in reinforcement learning (RL) training, where the AI self-grades its progress to bridge the generator-verifier gap. Fine-tuning the Qwen3-30B model with this self-grading approach has shown to enhance research plans for 70% of machine learning goals, achieving results comparable to Grok-4-Thinking, though GPT-5-Thinking remains superior. This approach also demonstrates significant cross-domain generalization, supporting the potential of AI as versatile co-scientists. This matters because it highlights the potential for AI to significantly enhance scientific research processes across various domains.