Revolutionize Local LLMs: Test Time Scaling Unleashed
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In this thrilling episode, the 1littlecoder team unveils a groundbreaking technique called test time scaling, allowing models to think longer during inference. It's like giving your local llama a turbo boost of brainpower, resulting in enhanced intelligence and more accurate responses. They showcase the remarkable impact of this method using a code shared by an hanum, a key figure in the mlx library. By tweaking the model with a simple yet ingenious trick based on the S1 simple test time scaling paper, they demonstrate how it can correctly answer tricky questions that stump other models.
The team takes us on a wild ride through the process, showing how appending "wait" tags can make the model think longer and arrive at the right answers. Test time scaling is all about using extra compute power during inference to fine-tune the model's performance by controlling its thinking process. They share their exhilarating experiment with a 1.32 billion parameter model, revealing the magic that unfolds as they increase the thinking time. This mind-bending journey is currently exclusive to Apple computers, utilizing the mlx LM library and the Deep Seek R1 distal Quin 1.5 billion parameter model.
Despite a few bumps in the road during the demo, the team remains steadfast in their belief in the effectiveness of test time scaling. They are determined to dive deeper into this revolutionary approach and share their discoveries with llama enthusiasts worldwide. So buckle up, gearheads, and get ready to witness the future of local llm testing unfold before your eyes. It's a thrilling adventure of innovation, code, and the relentless pursuit of pushing the boundaries of what's possible in the world of language modeling.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
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