IP屬地：香港
-
LLaMA：開源的高效的基礎(chǔ)語言模型LLaMA: Open and Efficient Foundation Language Models Feb 2023 Hugo Touvr...
-
2025-06-09
使用檢查點支持容錯訓(xùn)練 在整個RLHF訓(xùn)練過程中，可能會出現(xiàn)訓(xùn)練錯誤或機器故障，因此建議啟用檢查點功能以最小化損失。 API接口已在 :ref:...
-
縮放律與模型架構(gòu)：歸納偏置如何影響縮放？
Scaling Laws vs Model Architectures: How does Inductive Bias Influence S...
-
UL2：統(tǒng)一語言學(xué)習(xí)范式
UL2: Unifying Language Learning Paradigms https://arxiv.org/abs/2205.051...
-
用0.1%的額外計算超越縮放定律
Transcending Scaling Laws with 0.1% Extra Compute https://arxiv.org/abs/...
-
大語言模型的涌現(xiàn)能力
Emergent Abilities of Large Language Models https://arxiv.org/abs/2206.0...
-
預(yù)訓(xùn)練指南：測量數(shù)據(jù)年齡、領(lǐng)域覆蓋率、質(zhì)量和毒性的影響
A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age...
-
自回歸生成建模的縮放律
Scaling Laws for Autoregressive Generative Modeling Oct 2020 https://arx...
-
神經(jīng)語言模型的縮放律
Scaling Laws for Neural Language Models Jan 2020 https://arxiv.org/abs/2...