Unverified50% confidenceFactExact time
In the experiment, the Qwen3.5-0.8B backbone and approximately 51 billion parameter PLE memory module were frozen, with only R=1 readers deployed at decoder layers 3 and 9 being trained.
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50%
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Long-term
Relevance
9/25/2026
First Seen
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Related Claims
UnverifiedTransferring the PLE (n-gram memory) pre-trained by Qwen3.8 Flash-Next to the Qwen3.5-0.8B model achieved a 5.05% perplexity reduction on the frozen validation set.78% similarUnverifiedThe GGUF packages the Qwen3.5 backbone together with reader and arbitration tensors, while the large PLE memory exists as an external quantized sidecar and is not packed into the model GGUF.67% similarUnverifiedQwen3.8-Flash-Next adopts a hybrid attention mechanism of GDN plus QSA, with a total of 125B parameters plus 51B N-gram embeddings, activating only approximately 6B parameters per token.63% similarUnverifiedThis tutorial uses Qwen 2.5 (1.5B) for dialogue and answer generation, and BGE-M3 for text embedding; the entire pipeline can run on CPU without requiring a GPU.62% similarUnverifiedSmall and medium parameter versions of the Llama, Qwen, and Mistral series, combined with the GGUF format, can run on devices with 8GB or even less VRAM, and some extremely small models can run on pure CPU inference.61% similar
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