Unverified50% confidenceFactExact time
As a relatively niche professional software, Nuke has a very low representation in the training data of general-purpose large models, resulting in relatively limited large model support for it.
1
Sources
50%
Confidence
Long-term
Relevance
9/12/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedDuring LoRA fine-tuning, the original model parameters are frozen and only the inserted low-rank matrices are updated, significantly reducing VRAM requirements and training costs.67% similarUnverifiedThe core idea of PEFT is to not update all parameters, but instead introduce a small number of trainable adapter parameters (such as LoRA's low-rank matrix decomposition), reducing VRAM and computation requirements while retaining performance close to full fine-tuning.66% similarUnverifiedObscure legacy protocols like QIC-117 are nearly absent from large model training datasets, resulting in weaker model capability regarding them.61% similarUnverified仅凭相对少量的针对性训练数据，就能让大语言模型对明确事实给出完全错误的回答61% similarUnverifiedThe use of 3D models tends to be low-frequency but high-value, as a project may only require a few key models, making long-term subscriptions not cost-effective for light users59% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/911288API
curl https://kongchang.com/api/v1/knowledge/claims/911288MCP
get_claim(id=911288)