Llama 3.1

All models, with their blocks →

Size

405B

Published
Meta · Jul 2024
Line
Dense decoder · follows Llama 3
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Source

The Llama 3 Herd of Models (arXiv 2407.21783v3) · DPO (arXiv 2305.18290v3) · no training code is publishedHow to read

DPO step

DPO step

Tokens in the loss

×

tensor

Each response’s tokens, the formatting ones masked.

shape
mm [B batch, 2, T positions]
Sources

Contextual evidence from Mask formatting tokens

§4.1.4, Direct Preference Optimization: DPO on the SFT models with learning rate 10⁻⁵ and β = 0.1; special formatting tokens, including header and termination tokens, are masked out of the loss in chosen and rejected responses; an NLL term with scaling coefficient 0.2 is added on the chosen sequences.

meta · arXiv 2407.21783 / Llama3-Herd-2407.21783v3.pdf
Pinned revision v3 ↗ · p. 16

Contextual evidence from Policy

§4, eq. 7: L_DPO(π_θ; π_ref) = −E[log σ(β log π_θ(y_w|x)/π_ref(y_w|x) − β log π_θ(y_l|x)/π_ref(y_l|x))], over preference triples (x, y_w, y_l).

dpo · arXiv 2305.18290 / DPO-2305.18290v3.pdf
Pinned revision v3 ↗ · p. 4

Contextual evidence from Reference

§3–4: β controls the deviation from the reference policy π_ref, “namely the initial SFT model”; π_ref is initialized to π_SFT whenever available.

ref · arXiv 2305.18290 / DPO-2305.18290v3.pdf
Pinned revision v3 ↗ · pp. 4–5