Unverified60% confidenceOpinionExact time
For small models with limited parameter counts, data quality can have an even greater impact on final performance than data quantity.
2
Sources
60%
Confidence
Long-term
Relevance
7/13/2026
First Seen
Sources
不到350元训练亿参数模型:SmoLLM从零构建全记录
redditr/learnmachinelearning7/12/2026
Related Claims
UnverifiedModels with smaller file sizes and lower parameter counts generally have more limited performance.77% similarUnverifiedSome services' free tiers use models with fewer parameters or older versions, which may affect response accuracy and speed.75% similarUnverifiedThe effectiveness of threshold functions is highly dependent on the calibration quality of the underlying model's output probabilities.75% similarUnverifiedSmall models are far more sensitive to data quality than large models.75% similarUnverified小模型对数据分布更敏感,同样的数据补充量在4B模型上的边际效果往往比大模型更明显74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/502362API
curl https://kongchang.com/api/v1/knowledge/claims/502362MCP
get_claim(id=502362)