Unverified50% confidenceFactExact time
Weight freezing is implemented by setting requires_grad to False for some parameters, does not change the model structure, does not affect inference forward computation, and has zero deployment cost.
1
Sources
50%
Confidence
Long-term
Relevance
10/20/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedPrompting provides instructions and examples at inference time, has the lowest cost and fastest iteration, and is suitable for solving problems such as expression style, format constraints, and task boundary specification, but it does not change model weights, so it cannot introduce private knowledge the model does not know, nor can it guarantee real-time capability.72% similarUnverifiedA frozen model refers to a model whose weight parameters all remain unchanged during the inference phase, with no fine-tuning, adapter training, or online learning performed.72% similarUnverifiedThe zero synthetic data generation commitment means that user data is used only for the current inference, and will not be transformed, retained, or used for any form of model training70% similarUnverifiedWhen parsing model output, the parsing logic should be wrapped with try...except, and an empty list should be returned if parsing fails, to ensure the program does not crash.68% similarUnverifiedThe essence of preconfigured deployment is to combine a validated set of instance specifications and inference configurations into a template, skipping the process of tuning from scratch.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1073278API
curl https://kongchang.com/api/v1/knowledge/claims/1073278MCP
get_claim(id=1073278)