Unverified50% confidencePredictionExact time
Truly usable 1-bit-level large models will be an entirely new class of models trained from scratch with quantization-aware training (QAT), rather than post-hoc compression of existing models.
1
Sources
50%
Confidence
Long-term
Relevance
9/12/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedWhen fine-tuning a visual large model for downstream tasks, one can choose to fine-tune only the backend LLM, only the frontend Encoder, both parts, or specify particular layers and modules to train.73% similarUnverified1-bit/1.58-bit要求从训练阶段就以低精度权重为目标，与训练后量化方案本质不同72% similarUnverifiedBinarized neural network works such as the BitNet series typically operate on the premise of using low-bit-aware training from the training stage, rather than applying post-training quantization (PTQ) to an already fully trained full-precision model.71% similarUnverifiedThe phenomenon of smaller models outperforming larger ones suggests that the two companies may have trained independently at different scales, rather than taking the route of distilling large models into small ones.70% similarVerifiedModern large model training typically employs a combination of multiple parallelism strategies simultaneously—including data parallelism, pipeline parallelism, tensor parallelism, and expert parallelism—forming a 3D or 4D parallel architecture.70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/913315API
curl https://kongchang.com/api/v1/knowledge/claims/913315MCP
get_claim(id=913315)