import{s as lr,n as ur,o as dr}from"../chunks/scheduler.56725da7.js";import{S as pr,i as gr,e as s,s as r,c as l,h as fr,a as m,d as e,b as a,f as _,g as u,j as v,k as c,l as o,m as i,n as d,t as p,o as g,p as f}from"../chunks/index.18a26576.js";import{C as _r}from"../chunks/CopyLLMTxtMenu.08cb1880.js";import{D as h}from"../chunks/Docstring.4f5c8174.js";import{H as x}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.bfeb55ce.js";function cr(Fn){let C,oe,re,ie,K,se,N,me,R,Wn="The transformation specs API defines how model weights are transformed between the original Transformers implementation and the custom implementation optimized for Neuron devices. This enables automatic weight conversion during model loading and checkpoint consolidation.",le,U,ue,H,de,$,B,on,qt,qn=`This class defines the interface for transforming model weights between the original Transformers implementation
and the custom implementation for Neuron.`,sn,I,J,mn,zt,zn="Adapts the PEFT config to match the custom modeling implementation.",ln,F,O,un,Qt,Qn="Transforms the state dict from the original Transformers model to match the custom modeling implementation.",dn,W,X,pn,At,An="Returns the set of parameter names that this spec would affect.",gn,q,Y,fn,jt,jn="Guesses the PEFT type of the module associated to the spec.",_n,z,Z,cn,Gt,Gn="Restores the PEFT config to the original one that matches the original Transformers implementation.",hn,Q,tt,$n,Kt,Kn=`Produces the weights associated to this transformation spec from the custom model to match the original
Transformers weights.`,pe,et,ge,T,nt,vn,Nt,Nn="Defines a list of transformation specs for a given module of the model.",fe,rt,_e,L,at,bn,Rt,Rn=`This class is used to mark a module as a custom module. It is used to identify the modules that contain weights
that need to transformed when loading and saving the model.`,ce,ot,he,it,$e,P,st,yn,Ut,Un=`Represents a transformation where multiple linear layers are fused into a single linear layer.
It can handle the case where the fused linear layer is sharded across multiple tensor parallel ranks.`,ve,mt,be,y,lt,xn,Ht,Hn=`Represents the transformation of separate query, key, and value projections into a single GQAQKVColumnParalleLinear
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<p>A tuple containing the transformed weights and a list of the
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<p>tuple[dict[str, torch.Tensor], list[str]]</p>
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