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Junlin Zhou
jlzhou
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about 12 hours ago
PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models
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๐ข New Research Alert: Making Language Models Smaller & Smarter! Thrilled to share the latest technical report demonstrating how to reduce language model parameters by 77% while maintaining performance. The secret? Grouped pointwise convolutions. Yes. We brought a method from computer vision to the transformers arena. ๐ Key Findings: โข 77% parameter reduction. โข Maintained model capabilities. โข Improved generalization. Paper: https://www.researchgate.net/publication/388835829_SAVING_77_OF_THE_PARAMETERS_IN_LARGE_LANGUAGE_MODELS_TECHNICAL_REPORT Code: https://github.com/joaopauloschuler/less-parameters-llm
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DeepSeek-R1 Dissection: Understanding PPO & GRPO Without Any Prior Reinforcement Learning Knowledge
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Distributed SFT with trl and DeepSpeed Part 2: Scaling Locally
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Distributed SFT with trl and DeepSpeed Part 1: Starting Locally
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2307.08674
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jlzhou/Qwen2.5-3B-Infinity-Instruct-0625
Text Generation
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jlzhou/ppo-LunarLander-v2
Reinforcement Learning
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