๊ฐ€์ž… ํ›„ ์ดˆ๋Œ€ ๋งํฌ๋ฅผ ๊ณต์œ ํ•˜๋ฉด ๋™์˜์ƒ ์žฌ์ƒ ๋ฐ ์ดˆ๋Œ€ ๋ณด์ƒ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

ModelScope
@ModelScope2022
Driving innovations with open communities. ๐Ÿ’ฌ Join our Discord:
๊ฐ€์ž… April 2024
198 ํŒ”๋กœ์ž‰ ์ค‘    16.8K ํŒฌ
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: ๐Ÿš€ Explore all models: ๐Ÿงช Try the interactive demo: Here's a closer look at the model family: โ€ข Qwenputer embeds a functional RISC-V CPU inside Qwen3.5, using Gated DeltaNet's recurrent state as RAM. โ€ข Qwenputer-Filesystem introduces persistent file storage within the model's recurrent state, enabling memory across sessions. โ€ข Qwenputer-NPU extends the internal CPU with neural network instructions, allowing it to execute small models. โ€ข QwenPU takes things further with a dedicated vector unit capable of running Karpathy's 260K-parameter Llama inside Qwen itself. Original project ๐Ÿ‘‰:
๋” ๋ณด๊ธฐ
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.
๋” ๋ณด๊ธฐ