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% 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% similarVerifiedFor models with more than 30B parameters, the performance loss caused by 4-bit quantization typically results in a 1–3% drop on benchmarks, but the impact is more significant for smaller models.74% similar
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