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ModelScope
@ModelScope2022
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加入 April 2024
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Qwenputer explores a fascinating idea: embedding computational machinery directly into LLM weights, without retraining the original language model. Developed by @divagr1925 , the project has evolved from a RISC-V CPU inside Qwen3.5 into a family of models exploring new ways to extend LLM capabilities. The full model family and an interactive demo are now available on ModelScope: 🚀 Explore all models: 🧪 Try the interactive demo: Here's a closer look at the model family: • Qwenputer embeds a functional RISC-V CPU inside Qwen3.5, using Gated DeltaNet's recurrent state as RAM. • Qwenputer-Filesystem introduces persistent file storage within the model's recurrent state, enabling memory across sessions. • Qwenputer-NPU extends the internal CPU with neural network instructions, allowing it to execute small models. • QwenPU takes things further with a dedicated vector unit capable of running Karpathy's 260K-parameter Llama inside Qwen itself. Original project 👉:
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1/3 A CPU inside an LLM is one thing, but running another LLM inside an LLM would be taking things a bit too far, wouldn't it? Anyways, here's an LLM running inside an LLM : QwenPU, an LLM (@karpathy's stories260k) running on an NPU compiled inside Qwen 3.5-4B's weights.
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