Unverified50% confidenceDecision RuleExact time
The team chose Qwen2.5-1.5B-Instruct as the language model because the hackathon schedule was tight and hardware was limited, training a model from scratch was unrealistic, and they wanted to focus their effort on the memory architecture.
1
Sources
50%
Confidence
Long-term
Relevance
10/18/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedVKORE's route involves trade-offs: reinventing a cross-hardware portable training instruction set will lead to a substantial rise in software complexity and may lose part of the hardware-level performance optimization space.67% similarUnverifiedThe core idea of QLoRA is to first quantize the pre-trained model to 4-bit precision to reduce VRAM usage, then train only a small number of low-rank adapter matrices without updating the original weights.66% similarUnverifiedIf the proportion of code data in the training set reaches 40–50% (as in Qwen's strategy), the model will perform exceptionally well on programming tasks but may come across as overly technical in everyday conversation.66% similarUnverifiedQwen 2.5系列训练数据超过18万亿Token，采用Transformer解码器架构并引入分组查询注意力（GQA）机制66% similarUnverifiedVIDRAFT released a small language model named VKORE-46M-NPU with 46 million parameters, pretrained entirely from random weights, and the training process did not use any GPU.65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1060566API
curl https://kongchang.com/api/v1/knowledge/claims/1060566MCP
get_claim(id=1060566)