oiv2SSKJr SSKrSSKrSSKrSSKrSSKrSSKJr SSK J r J r SSKJ r SSK Jr SSKJrJrJrJr SSKrSSKrSSKrSSKJrJr SS KJrJrJr SS KJ r J!r! SS K"J#r#J$r$J%r%J&r& SS K'J(r( SS K)J*r+ SSK,J-r-J.r.J/r/ SSKJ0r0J1r1J2r2J3r3 SSK4J5r5J6r6J7r7 SSK8J9r9 SSK:J;r;Jr>J?r? SSK@JArA SSKBJCrC SSKDJErE SSKFJGrGJHrHJIrI SSKJJKrK SSKLJMrM SSKNJOrOJPrPJQrQ SSKRJSrSJTrTJUrUJVrVJWrWJXrXJYrYJZrZJ[r[J\r\J]r]J^r^J_r_J`r`JaraJbrb "SS\9\RR5re"SS \e5rf"S!S"\e5rg"S#S$\e5rh"S%S&\e5ri"S'S(\e5rj"S)S*\e5rk\"S+S,55rlS2S-jrm\"S.S/55rnS3S0jroS1rpg)4) annotationsN)Sequence)contextmanager nullcontext)deepcopy) dataclass)AnyLiteralOptionalUnion)dispatch_modelinfer_auto_device_map)AlignDevicesHookadd_hook_to_moduleremove_hook_from_submodules)get_balanced_memorynamed_module_tensors) HfFileSystem ModelCard ModelCardDatahf_hub_download) safe_open) save_file)BCEWithLogitsLossCrossEntropyLossMSELoss)Cache DynamicCacheEncoderDecoderCachePreTrainedModel)QuestionAnsweringModelOutputSequenceClassifierOutputTokenClassifierOutput)PushToHubMixin)get_alora_offsets_for_forwardget_alora_offsets_for_generate) BaseTunerBaseTunerLayer)AuxiliaryTrainingWrapper)DUMMY_MODEL_CONFIG)init_empty_weights)TrainableTokensWrappercreate_attention_mask set_additional_trainable_modules) __version__) PeftConfig)PEFT_TYPE_TO_CONFIG_MAPPINGPEFT_TYPE_TO_PREFIX_MAPPINGPEFT_TYPE_TO_TUNER_MAPPING)SAFETENSORS_WEIGHTS_NAME8TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING WEIGHTS_NAMEPeftTypeTaskType_get_batch_size_prepare_prompt_learning_config _set_adapter_set_trainableget_peft_model_state_dictid_tensor_storage infer_deviceload_peft_weightsmap_cache_to_layer_device_mapset_peft_model_state_dictshift_tokens_rightc^\rSrSrSrS(S)U4Sjjjr\S*Sj5r\S+Sj5r\S,Sj5r \RS-Sj5rS.S/Sjjr \ S0S1S jj5r S2S jrS3S jrS3S jrS4U4S jjrS5S6SjjrS7SjrS8SjrS9U4Sjjr\S5rS:SjrSrS;Sjr\S5rSSjr\S?Sj5rS@Sjr SASjr!\ SBSj5r"SCSjr#SDSjr$SESFS jjr%S>S!jr&SGSHS"jjr'\S#5r(\S$5r)S%r*SIS&jr+S'r,U=r-$)J PeftModelHa Base model encompassing various Peft methods. Args: model ([`~transformers.PreTrainedModel`]): The base transformer model used for Peft. peft_config ([`PeftConfig`]): The configuration of the Peft model. adapter_name (`str`, *optional*): The name of the adapter, defaults to `"default"`. autocast_adapter_dtype (`bool`, *optional*): Whether to autocast the adapter dtype. Defaults to `True`. Right now, this will only cast adapter weights using float16 and bfloat16 to float32, as this is typically required for stable training, and only affect select PEFT tuners. low_cpu_mem_usage (`bool`, `optional`, defaults to `False`): Create empty adapter weights on meta device. Useful to speed up the loading loading process. > [!TIP] > Don't use `low_cpu_mem_usage=True` when creating a new PEFT adapter for training. **Attributes**: - **base_model** ([`torch.nn.Module`]) -- The base transformer model used for Peft. - **peft_config** ([`PeftConfig`]) -- The configuration of the Peft model. - **modules_to_save** (`list` of `str`) -- The list of sub-module names to save when saving the model. - **prompt_encoder** ([`PromptEncoder`]) -- The prompt encoder used for Peft if using [`PromptLearningConfig`]. - **prompt_tokens** (`torch.Tensor`) -- The virtual prompt tokens used for Peft if using [`PromptLearningConfig`]. - **transformer_backbone_name** (`str`) -- The name of the transformer backbone in the base model if using [`PromptLearningConfig`]. - **word_embeddings** (`torch.nn.Embedding`) -- The word embeddings of the transformer backbone in the base model if using [`PromptLearningConfig`]. c>[TU]5 X0lURUlSS1UlUR UlUR (aX20UlXlURX2US9 OMSUl[URnU(a[O[nU"5 U"XU0U5UlSSS5 [URS5(aURRX4S9 [USS5(aUR!U5n[URS5(a@[URR"S 5(aS URR"lS Ulg!,(df  N=f) N adapter_names alora_offsetslow_cpu_mem_usage_cast_adapter_dtype adapter_nameautocast_adapter_dtypeis_gradient_checkpointingTconfigpretraining_tpr/F)super__init__active_adapter peft_typespecial_peft_forward_argsis_prompt_learning_is_prompt_learning _peft_config base_model add_adapterr4r+rhasattrrMgetattr(prepare_model_for_gradient_checkpointingrRrS_adapters_disabled) selfmodel peft_configrOrPrLclsctx __class__s I/home/james-whalen/.local/lib/python3.13/site-packages/peft/peft_model.pyrUPeftModel.__init__hsH *$..+:?)K&#.#A#A  # #!- ;D #O   \J[  \ $D ,[-B-BCC(9${C"%eK-H,"W 4??$9 : : OO / /) 0  55t < <AA%HE 4??H - -'$//:P:PRb2c2c45DOO " " 1"'#s %E88 FchUR(a UR$URR$NrZr[r\rdrbs rhrdPeftModel.peft_configs'  # #$$ $***cURRn[U[5(d$URn[U[ 5(aU/nU$![ a( URn[U[ 5(aU/nU$f=frk)r\active_adapters isinstancelistrVstrAttributeError)rbadapterss rhrqPeftModel.active_adapterss &66Hh--  ..h,, (zH   &**H(C(($:  &sAA.BBcURURR(aUR(+$UR(+=(d UR(+$)aReflects whether the adapters are purposefully disabled (via disable_adapter) or if there are no active adapters (enabled but inactive). They are two separate mechanisms but sometimes it is helpful to know whether the model has any active/enabled adapter at all. )rdrVrYrarqrms rhhas_active_enabled_adapter$PeftModel.has_active_enabled_adaptersJ   D// 0 C C... .***F$2F2F.FFrocTUR(aXlgXRlgrkrl)rbvalues rhrdrns  # # % */OO 'roc f ^[RRU5(a[SUS35eUc$[ TR R 55nOM[U4SjU55(a3[S[ TR R 55SUS35eU4SjnU(a&[R"US S 9 TRU5 UGH=n TR U n [TURS S5U US 9n U S :wa[RRX5OUn [R"U S S 9 U(GasU(Gak[R"[5n U R5H`up[!U["R$5(aU ['U5R)U5 MCU [+U5R)U5 Mb U R5VVs0sHunn[-U5S:dMUU_M nnnUR5H(unnUSSHnU UR/5U U'M M* Ub7[0R2"U 5n S U lU R7U5 U"XX5n [9U [RRU [:5SS0S9 OzU(asUb7[0R2"U 5n S U lU R7U5 U"XX5n ["R<"U [RRU [>55 U R@clU RB(a&TRDRFRSS5O/TRDRHRFRSS5U l U RJnS U l%U RLc5TROU RBS9nURPnURRUS.nOSnU(aUbS U lU =RTS-sl*U RV(dU =RXS-sl,OU =RXS-sl,U RZ(a9U RZR5VVs0sH unnUSU-_M snnU l-U R\(a9U R\R5VVs0sH unnUSU-_M snnU l.U R7U US9 UU l%GM@ gs snnfs snnfs snnf)u This function saves the adapter model and the adapter configuration files to a directory, so that it can be reloaded using the [`PeftModel.from_pretrained`] class method, and also used by the [`PeftModel.push_to_hub`] method. Args: save_directory (`str`): Directory where the adapter model and configuration files will be saved (will be created if it does not exist). safe_serialization (`bool`, *optional*): Whether to save the adapter files in safetensors format, defaults to `True`. selected_adapters (`List[str]`, *optional*): A list of adapters to be saved. If `None`, will default to all adapters. save_embedding_layers (`Union[bool, str]`, *optional*, defaults to `"auto"`): If `True`, save the embedding layers in addition to adapter weights. If `auto`, checks the common embedding layers `peft.utils.other.EMBEDDING_LAYER_NAMES` in config's `target_modules` when available. and automatically sets the boolean flag. This only works for 🤗 transformers models. is_main_process (`bool`, *optional*): Whether the process calling this is the main process or not. Will default to `True`. Will not save the checkpoint if not on the main process, which is important for multi device setups (e.g. DDP). path_initial_model_for_weight_conversion (`str, *optional*`): The path to the initialized adapter, which is obtained after initializing the model with PiSSA/CorDA/OLoRA and before performing any training. When `path_initial_model_for_weight_conversion` is not None, the difference in adapter before and after fine-tuning is calculated. This difference can be represented as the parameters of a standard LoRA adapter. Using this converted adapter does not require changes to the base model, thus conveniently allowing the use of multiple PiSSA/CorDA/OLoRA adapters with LoRA adapters, and the activation or deactivation of any adapters. Note that this conversion is not supported if `rslora` is used in combination with `rank_pattern` or `alpha_pattern`. kwargs (additional keyword arguments, *optional*): Additional keyword arguments passed along to the `push_to_hub` method. zProvided path (z#) should be a directory, not a fileNc3p># UH+nU[TRR55;v M- g7frk)rsrdkeys).0selected_adapter_namerbs rh ,PeftModel.save_pretrained..s2->)&T$2B2B2G2G2I-JJ->s36zYYou passed an invalid `selected_adapters` arguments, current supported adapter names are z - got .c>^TR(a/TR(dTR(a Sn[U5e[ U4SjS55(d[ R "S5 [RRU5nT R[RRU5UUS9 [T RUR5R5R!S5n[T RUR5R5S:Hn[T RUR5R5S:HnU(dU(dU(a [S 5eT R"R%X%U5nT R'U5 U$!T R'U5 f=f) NzPassing `path_initial_model_for_weight_conversion` to `save_pretrained` is not supported when using `rank_pattern` or `alpha_pattern` at the same time as `use_rslora=True`.c3># UH7n[TR5R5RU5v M9 g7frk)rtinit_lora_weightslower startswith)rprefixrds rhrJPeftModel.save_pretrained..save_mutated_as_lora..s:AFK11288:EEfMMAs?A)pissacordaoloratruezr`path_initial_model_for_weight_conversion` only works for converting a PiSSA/CorDA/OLoRA adapter to a LoRA adapter) subfolderrOrrrzThe `init_lora_weights` parameter of the initial adapter should be set to `True`. Otherwise, `self.load_adapter` will subtract the decomposed values again based on the residual model.) use_rslora rank_pattern alpha_pattern ValueErroranywarningswarnospathbasename load_adapterdirnamertrdrrrr\subtract_mutated_initdelete_adapter) rd(path_initial_model_for_weight_conversionoutput_state_dictkwargsmsginitial_adapter_nameis_pissais_cordais_olorarbs ` rhsave_mutated_as_lora7PeftModel.save_pretrained..save_mutated_as_loras%%;+C+C{G`G`e!o%A %$&77#3#34\#]  :!!GGOO$LM2!5" t//0DEWWX^^`kklstt//0DEWWX^^`dkkt//0DEWWX^^`dkkx8$* %)OO$I$I%V%!##$89$ $##$89s DF55GT)exist_ok state_dict)rrOsave_embedding_layersdefaultr/formatptmetadata name_or_path)is_prompt_tuning)base_model_classparent_libraryg;f?)auto_mapping_dict)/rrisfilerrsrdrrmakedirscreate_or_update_model_cardr>getjoin collections defaultdictitemsrrtorchTensorr?appendidlenclonecopyrrsave_pretrainedsafe_save_filer5saver7base_model_name_or_pathrYr\__dict__rcinference_mode task_type_get_base_model_class __module____name__rr lora_alpharr)rbsave_directorysafe_serializationselected_adaptersris_main_processrrrrOrdr output_dirptrsnametensorptrnames shared_ptrs_shared_tensor_namerrrrkeyvals` rhrPeftModel.save_pretrainedsT 77>>. ) )~.>>abc c  $ $T%5%5%:%:%< = ->!T--22456g>O=PPQS % %N  KK 6  , ,^ <-L**<8K 9!::lD9)&; !  HTW`G`nCftJ KK T 2#5#..t4$5$;$;$=LD"&%,,77.v67>>tDRZ(//5%>=AJJL[Ljc5CPUJYZNzsEzL [ + 1 1 3HAu/4ABi*@QRd@e@k@k@m)*<=/8!4 <G"&-- "$ "2!R@"S5:XdM6URC[EU5S-[EU5-5 Ma GM U(a"U R SS5(d [S5eU(a^UR=5Vs0sH>nUU;dM UUUSU[EUURF5RISS5S._M@ nnUU S'[USS5bL[K[/URLRO55RQSS155S :a [SU5 UR(aU(a [S!5eU(+Ul [[US"S5U 5(a[X\5(d[US#[WU5S$35eS%U ;a U S%Ul,O[ZR\R_U5(d[a5nURcU5Vs/sH"nUS&S':XdMUS([KU5S-SPM$ nn0nUH%n[ZR\ReX"5UU'M' UUl,UUl3OS%U ;a [S)5eURhU R=5;a U"UUUUUS*9nOXRh"UUUUUS*9nURj"UU4UUUU S+.U D6nURlVs/sHnS,U;dM S-U;dMUPM nnU(aUS.US3n[nR "URp5n U (aUU ;a S/US0U S13U-n[&R("U5 U$s snfs snfs snf)2u Instantiate a PEFT model from a pretrained model and loaded PEFT weights. Note that the passed `model` may be modified inplace. Args: model ([`torch.nn.Module`]): The model to be adapted. For 🤗 Transformers models, the model should be initialized with the [`~transformers.PreTrainedModel.from_pretrained`]. model_id (`str` or `os.PathLike`): The name of the PEFT configuration to use. Can be either: - A string, the `model id` of a PEFT configuration hosted inside a model repo on the Hugging Face Hub. - A path to a directory containing a PEFT configuration file saved using the `save_pretrained` method (`./my_peft_config_directory/`). adapter_name (`str`, *optional*, defaults to `"default"`): The name of the adapter to be loaded. This is useful for loading multiple adapters. is_trainable (`bool`, *optional*, defaults to `False`): Whether the adapter should be trainable or not. If `False`, the adapter will be frozen and can only be used for inference. config ([`~peft.PeftConfig`], *optional*): The configuration object to use instead of an automatically loaded configuration. This configuration object is mutually exclusive with `model_id` and `kwargs`. This is useful when configuration is already loaded before calling `from_pretrained`. autocast_adapter_dtype (`bool`, *optional*): Whether to autocast the adapter dtype. Defaults to `True`. Only relevant for specific adapter types. ephemeral_gpu_offload (`bool`, *optional*): Whether to use ephemeral GPU offloading for partially loaded modules. Defaults to `False`. This is useful when parts of the model and/or components (such as adapters) are kept in CPU memory until they are needed. Rather than perform expensive operations on small data, the data is transferred to the GPU on-demand, the operation(s) performed, and the results moved back to CPU memory. This brings a slight momentary VRAM overhead but gives orders of magnitude speedup in certain cases. low_cpu_mem_usage (`bool`, `optional`, defaults to `False`): Create empty adapter weights on meta device before loading the saved weights. Useful to speed up the process. torch_device (`str`, *optional*, defaults to None): The device to load the adapter on. If `None`, the device will be inferred. key_mapping (dict, *optional*, defaults to None) Extra mapping of PEFT `state_dict` keys applied before loading the `state_dict`. When this mapping is applied, the PEFT-specific `"base_model.model"` prefix is removed beforehand and the adapter name (e.g. `"default"`) is not inserted yet. Only pass this argument if you know what you're doing. kwargs: (`optional`): Additional keyword arguments passed along to the specific PEFT configuration class. r/) MODEL_TYPE_TO_PEFT_MODEL_MAPPING) XLoraConfig XLoraModelNrrevision cache_dirtoken)rrrruse_auth_tokenz+The input config must be a PeftConfig, got _checkpoint_conversion_mappingruntime_configzCEphemeral GPU offloading is not supported for this model. Ignoring. hf_device_mapT)recurse_hf_hookoriginal_devicesdatasetmetaruse_safetensorsz8Disk offloading currently only supported for safetensorssafetensors_fileztorch.)r weight_namedtype offload_indexcpudiskrRCannot set a prompt learning adapter to trainable when loading pretrained adapter.r\zExpected 'XLoraConfig', got 'z ' instead.rvtype directoryrzFIf model_id is a local path, then `adapters` must be passed in kwargs.)rPrL) is_trainablerPrL key_mappingvblora_vector_bankprompt_encoderz9Found missing adapter keys while loading the checkpoint: Adapter name ')' should not be contained in the prefix 'z@'. This could be the potential reason for missing adapter keys. )9autortunersrrrr2r1_get_peft_typefrom_pretrainedrrrrrgrYr_r^rephemeral_gpu_offloadrrdictrset named_modulesr weights_maprindexrrrdeviceaddrtrreplacerrvalues intersectionr TypeErrorrrvrrexistsrlsr _subfoldersrr missing_keysr3rW)!rercmodel_idrOrrRrPrrLrrrrr hf_kwargsr weight_map disk_modulesr rmodulerprsfilerI adapter_paths load_resultkr warn_messagers! rhrPeftModel.from_pretrainedsst ;3 >#ZZ T:"JJz48#ZZ T:GT2 I "(,n UR>[@RB[@RD[@RF4;aU "X R:5n OUR>[@RH:Xa U "U5n OpUR>[@RJ:XaG[MS URO5RQ555(a [SS 5eU "U5n O [SS5eU RUURV5n UR RY[RR X055 [RZ"UR\UR-5R_5UR U'g![&a GNf=f)NrFrr/zembeddings.word_embeddingsds_shapeget_text_config text_configrz.weightrc3<# UHn[USS5v M g7f)gradient_checkpointingFNr_)rrs rhr2PeftModel._setup_prompt_encoder..srRq76#;UCCRqsz8Prefix tuning does not work with gradient checkpointing.z Not supported)0rdr^rnn ModuleDictr prompt_tokensr\named_children parameters requires_gradrrr transformer_backbone_namenum_transformer_submodulesrr9 SEQ_2_SEQ_LM get_submodulerursnamed_parametersr_rRr& vocab_sizer'shaperword_embeddingsr4rWr8 PROMPT_TUNINGMULTITASK_PROMPT_TUNINGCPTP_TUNING PREFIX_TUNINGrget_base_modelmodulesrtor updatearangenum_virtual_tokenslong)rbrOrRtransformer_backbonerrparamr9 named_paramr|"deepspeed_distributed_tensor_shaper7 model_clsrs rh_setup_prompt_encoderPeftModel._setup_prompt_encoderZs<!!,/t-.."'(("5"5b"9D !#D # OO::Z/6B:1=K&:&H&HI\I\]fhjIk&lO+'T. /.v/?/?@    6 68X8XZbZfZfg g&v/C/CDN   !2!2 2&v.N   !7!7 7rRVReReRgRoRoRqrrr ![\\&v.N_- -'**4;;7 ""588#6#6 7U#VW+0<<  % %(I(I I, $& <(]   s9O"" O0/O0c&URU5 g)z< Prepares the model for gradient checkpointing if necessary N))_prepare_model_for_gradient_checkpointing)rbrcs rhr`2PeftModel.prepare_model_for_gradient_checkpointings 66u=roc[USS5(dz[USS5(dh[USS5(dV[US5(aUR5 U$[US5(a"SnUR5R U5 U$)Nis_loaded_in_8bitFis_loaded_in_4bit is_quantizedenable_input_require_gradsget_input_embeddingsc&URS5 g)NT)requires_grad_)rinputoutputs rhmake_inputs_require_gradUPeftModel._prepare_model_for_gradient_checkpointing..make_inputs_require_grads))$/ro)r_r^rTrUregister_forward_hook)rbrcrZs rhrN3PeftModel._prepare_model_for_gradient_checkpointings E. 6 6u1599une44u:;;002  6770**,BBC[\ roc|>URUnURURS5RSS5R UR R R5nURURnURUR[R:Xa"USS2SURUR24nURUR[R:Xa[Un[XR]CU5nOU"U5nUSR#5R%5$)zr Returns the prompt embedding to save when saving the model. Only applicable when using a prompt learning method. rr/N)rr. unsqueezeexpandrA embeddingweightr rdrWr8r>rDr;r4rTforwarddetachr)rbrOrr.rWprompt_embedding_clsprompt_embeddingsrgs rhget_prompt_embedding_to_save&PeftModel.get_prompt_embedding_to_saves ,,\:   | , 6 6q 9 @ @B G J J>KcKcKjKjKqKq r $$\2<<   L ) 3 3x7M7M M)!-`t/?/? /M/`/`-`*`aM   L ) 3 3x7W7W W#=i#H %&: STa b  .} =  #**,0022roc URnURURnURURR S5R US5R URRR5nUR[R:XGanUSS2SUR24nUR(a(URRRUSS5nOU"U5nUR bUR UR 5nUR#UURUR$S-UR&UR(UR&-5nUR*S:Xa[,R."Xw/SS9nUR1/SQ5R3UR*S-5nUR55n[7USS5n [7U S S 5n [8R:"UR<R>S5b'[8UR<R>n U "U5nGOS U ;dS U ;Ga@[@RBRE[FRH5[@RBRES 5:n U (aUbUS:Xa [KS5eUR<n [MU S5(aU RO5n U (a+SSK#J(n U"U UUUSRRUSRS9nO [UU S9n[,RV"URUSRS9n[YUR$5H'nUSUUSUnnUR[UUUSU0S9 M) UnGOhUR*S:Xaw[@RBRE[FRH5[@RBRES 5:n U (a[TR\"U5nO[UU5nOUR*S:Xa[7UR^SS5(a[@RBRE[FRH5[@RBRES 5:n U (a[`R\"U5nO [aU5n[U5Ul1URdRg5HnSURdU'M [iUR55U5 U$UR[Rj:Xa U"Xb5nU$UR(aURRnO USSnU"U5nURUSS5nU$)zc Returns the virtual prompts to use for Peft. Only applicable when using a prompt learning method. rr_Nr/rdim)rrr/rR model_typergemma2 gemma3_textz 4.56.0.dev0zmax_cache_len is missing but it should have been passed. Something went wrong, please open an issue on GitHub with a reproducer: https://github.com/huggingface/peft/issuesr&) HybridCache)rRmax_batch_size max_cache_lenrr )rRr cache_position) cache_kwargs_supports_cache_classTF)6active_peft_configrrVr.r`rarArbrcr rWr8r>rDrrepeatbase_model_torch_dtypeview num_layersnum_attention_heads token_dimr3rcatpermutesplitr?r_r6rrRro packagingversionparse transformersr0rr^r&rrrrrCrangerBfrom_legacy_cacher\rcross_attention_cache is_updatedrrBr;)rb batch_sizetask_idsrtrdrr.past_key_valuesr\ model_configropost_process_fntransformers_lt_4_56 base_configrr new_cacherv layer_idx key_states value_statesrpromptss rh get_promptPeftModel.get_prompts -- ,,T-@-@A   t22 3 Yq\ VJ # R((//66 7   H$:$: :)!-M{/M/M-M*MNM))"0":":"A"A"H"HUVXY"Z"0"?**6"1"4"4T5P5P"Q-22..&&*//%%)H)HH O55:"'))_,NTU"V.55oFLL66:O,,.J":x>L |R@JGKKDKKLbLbdhiu"Z[_[f[f[q[q"r"1/"Bj(mz.I'0'8'8'>'>|?W?W'X[d[l[l[r[r!\($(m.CZ\I\$h)// ;(9::"-"="="?K'9 +*'1&3-a066.q188 !I!-K @I!&k.L.LUdefUgUnUn!o!&{'='=!>I/>q/A)/Lo^_N`ajNk J$$"L)K[]kJl%"? #,771<(1'8'8'>'>|?W?W'X[d[l[l[r[r!\($(&2&D&D_&UO&2?&CO88A=7!8$DD(1'8'8'>'>|?W?W'X[d[l[l[r[r!\($(&9&K&KO&\O&9/&JO8D5*55::[TU]U5$![a US:Xae[URU5s$f=f)z1Forward missing attributes to the wrapped module.r\)rT __getattr__rur_r\)rbrrgs rhrPeftModel.__getattr__}sC 27&t, , 2|#4??D1 1 2s '<<c/# [URS5(a;UR(a*URR"U0UD6 Sv SSS5 gSv g!,(df  g=f7f)N_enable_peft_forward_hooks)r^r\ryr)rbargsrs rhr$PeftModel._enable_peft_forward_hookssX 4??$@ A AdFeFe;;TLVLM    ML sA A/ AA/ A,(A/cUR"U0UD6 UR5VVs0sHup4X0R;dMX4_M nnnUR5"U0UD6sSSS5 $s snnf!,(df  g=f)z Forward pass of the model. N)rrrXr?rbrrr!vs rhrdPeftModel.forwardsl , ,d =f ='-||~a~tqB`B`9`dad~Fa&&($9&9> =a> =s"A.A(A(A.(A.. A<cUR"U0UD6 UR5VVs0sHup4X0R;dMX4_M nnnUR5R"U0UD6sSSS5 $s snnf!,(df  g=frk)rrrXr?generaters rhrPeftModel.generatesp  , ,d =f ='-||~a~tqB`B`9`dad~Fa&&(114B6B> =a> =s"A8A2A2"A82A88 Bc|U(d URRR$URR$)z Returns the base model class. )r\rcrg)rbrs rhrPeftModel._get_base_model_classs- ??((22 2(((roc## URURR(anURnURRUlUR nURR UlSUlSv XlX lSUlgURURR(aHURR5 SUlSv URR5 SUlgUR5nURS:Xa[R"S5 URR5 SUlSv URSLaURR5 SUlg!WUlWUlSUlf=f!URR5 SUlf=f!URSLaURR5 SUlf=f7f)z Context manager that disables the adapter module. Use this to run inference on the base model. Example: ```py >>> with model.disable_adapter(): ... model(inputs) ``` TNF irregularzThe model contains some adapter layers that are enabled and others that are disabled. This is most likely unintentional. After exiting the disable_adapter context, all adapters will be enabled)rdrVrYrdr\prepare_inputs_for_generationrais_adaption_promptdisable_adapter_layersenable_adapter_layersget_model_statusenabledrr)rb old_forward!old_prepare_inputs_for_generation model_statuss rhdisable_adapterPeftModel.disable_adapters   D// 0 C C 0#ll #66 484V4V159__5b5b2*.'* 5V2*/'   d11 2 E E 0668*.'557*/' 002L##{2 &  0668*.'''u4OO99;*/'; + 5V2*/'557*/' ''u4OO99;*/'sN)H AF X44$)N)r)rinspect signaturerr0)rer(_kwargs_not_in_hf_hub_download_signaturehf_hub_download_kwargs other_kwargsrr|s rh _split_kwargsPeftModel._split_kwargss^3F0!#  ,,.JCg''8CCCsGv.3s+$)S! ) &33roc U(dU$Sn[URR55nUGHn[UR55nUSRS5SS-nUHonUR S5n X8SU -n [ UR 55U n [U [5(aX8SU -S-XS-n OX8-n XUS'XX'X Mq [5n [UR55GH3n XSn[UR[R55n[U5HunnSU;dMUU==S - ss' O [RR"U6nX;aM0n[US S 9nUR5HnUR!U5nUR#5nUR S5n X7-USU -n [ UR 55U n[U[5(azU S-UU S-nUR%5VVs0sHunnX;dM UU_M nnnUR%5H-unnUR S5nUSUSU3-UUS-n UUU 'M/ OX7-U-nUUU'M U R'U5 UR5H nUUS .X'M [RR)U5n[RR+U5(d[R,"U5 [/UUWS 9 SSS5 GM6 GM gs snnf!,(df  GMT=f)ab Update the offload_index and safetensors files for loading and mergine PeftModels with disk-offloaded modules. Args: offload_index (Dict[str: str]): Dictionary of disk-offloaded modules with their metadata and safetensors filenames adapters_weights (Dict[str: torch.tensor]): Dictionary of Peft adapter module names and weights zbase_model.model.rrNz .base_layerrrz--z-peftr) framework)rrr)rsrdrrrfindrr rrr(r rsep enumeraterrr get_tensorrrrrrrr) rbradapters_weightsrrIrOrblock_idr suffix_posextended_prefixrnew_key files_seenfnamenew_fname_listir new_fname safe_dictfsafe_key safe_tensorr safe_module final_keyr lora_dictlora_keylora_valdivide base_names rh_update_offloadPeftModel._update_offloadsF $T--2245 )L **,-DAw}}S)!,s2H YYs^ "({ +;";d0023ODfn55$;J'77-G#kJZZG$lG4;c"=1)6); &!&J 2 2 45%./AB"&ekk"&&&9!:(8GAtt|&q)W4) 9GGLL.9 & u5$%FFH&'ll8&< #$::<%-^^C%8 *0*;h{ >S*S&*4+=+=+?&@&Q %k>BB(7-(G(S]S^J_(_IBRBXBXBZ(uBZhc3^m^tcBZI(u7@oo6G 2()1)<*27F*;,>P*PS[\b\cSd*d5= ' 27H )/(9H(DI/: ),#%-$NN9- )~~/BK\_-` * 0!# :I77>>)44 I."9i(K9656'*T)v65s& BM, L><L>C(M>M M cUR(aU(a [S5eU/[URR 55-n[ U5S:a[ SU55(aSn[R"U5 g [ SU55(aSn[R"U5 g [ SU55(aSn[R"U5 g g g ) z?Perform checks on newly added PEFT configs to ensure integrity.rr/c3B# UHn[USS5S:Hv M g7f)rNrr*rrRs rhr6PeftModel._check_new_adapter_config..s!cWbV76#6=HWbzPiSSA changes the base weights of the model and should thus not be used with other adapters. Consider converting the PiSSA adapter into a normal LoRA adapter: https://github.com/huggingface/peft/tree/main/examples/pissa_finetuning#convert-pissa-to-lorac3B# UHn[USS5S:Hv M g7f)rNrr*rs rhrr!eYdvWV%8$?7JYdrzCorDA changes the base weights of the model and should thus not be used with other adapters. Consider converting the CorDA adapter into a normal LoRA adapter: https://github.com/huggingface/peft/tree/main/examples/corda_finetuning#convert-corda-to-lorac3B# UHn[USS5S:Hv M g7f)rNrr*rs rhrrr rzOLoRA changes the base weights of the model and should thus not be used with other adapters. Consider converting the OLoRA adapter into a normal LoRA adapter: https://github.com/huggingface/peft/tree/main/examples/olora_finetuning#olora-and-loraN) rYrrsrdrrrrr)rbrdr all_configsrs rh_check_new_adapter_config#PeftModel._check_new_adapter_configs  ) )lqr r#md4+;+;+B+B+D&EE { a cWbccct  c"eYdeeet  c"eYdeeem  c" f roc  SSKJn URU 5upUc [5nX R;aXU [ R "U40U D6R"U4SU0U D6n URXS9 U(+U l URX,US9 [U4XHS.U D6n U RSS 5n[UU UUUS 9nURURn[R"US 5n/nUR H$nUU;dM UU;dMUR#U5 M& UR R%5 UR R'U5 [)US S5Gb[+[-UR.R155R3S S155S:Gav[+UR5S:XGa\U RSS5nU RSS5nU RSS5nU RSS5nU RSS5nUbUb [5S5eUcUn0nS[6R8"[:5R<;aUUS'UR>nUS:wa[AUUUUS:HS9n[CU[D5(a [GUUUS9nURIUU 5 UUS'[;U4UUS.UD6 [KSS9nURURL(a[OURP5 [SURU5U5 [WURXS5(aURXR[X%S9 U(dUR]5 U$) a Load a trained adapter into the model. The name for the new adapter should be unique. The new adapter is not automatically set as the active adapter. Use [`PeftModel.set_adapter`] to set the active adapter. Args: model_id (`str` or `os.PathLike`): The name of the PEFT configuration to use. Can be either: - A string, the `model id` of a PEFT configuration hosted inside a model repo on the Hugging Face Hub. - A path to a directory containing a PEFT configuration file saved using the `save_pretrained` method (`./my_peft_config_directory/`). adapter_name (`str`): The name of the adapter to be added. is_trainable (`bool`, *optional*, defaults to `False`): Whether the adapter should be trainable or not. If `False`, the adapter will be frozen and can only be used for inference. torch_device (`str`, *optional*, defaults to None): The device to load the adapter on. If `None`, the device will be inferred. autocast_adapter_dtype (`bool`, *optional*, defaults to `True`): Whether to autocast the adapter dtype. Defaults to `True`. Right now, this will only cast adapter weights using float16 and bfloat16 to float32, as this is typically required for stable training, and only affect select PEFT tuners. ephemeral_gpu_offload (`bool`, *optional*, defaults to `False`): Whether to use ephemeral GPU offloading for partially loaded modules. Defaults to `False`. low_cpu_mem_usage (`bool`, `optional`, defaults to `False`): Create empty adapter weights on meta device before loading the saved weights. Useful to speed up the process. key_mapping (dict, *optional*, defaults to None) Extra mapping of PEFT `state_dict` keys applied before loading the `state_dict`. When this mapping is applied, the PEFT-specific `"base_model.model"` prefix is removed beforehand and the adapter name (e.g. `"default"`) is not inserted yet. Only pass this argument if you know what you're doing. kwargs: (`optional`): Additional arguments to modify the way the adapter is loaded, e.g. the token for Hugging Face Hub. r/)r2Nr)rrK)r rignore_mismatched_sizesF)rOrrLrrrrr device_mapr max_memoryoffload_folder offload_dirrzL // /5)), o   '< ) K  * *; * R-9)9K &   \J[  \,  ) F\  #)**-F"N/  %$;/    .88266ubA !++Cs"|s':$++C0,   &&(  ''(<=T?D 1 =S++2245BBE6?STWXXD$$%*L&9JL$7J#ZZ(8$?N **]D9K"JJ=M&>+E !_``$, $& !'"3"3N"C"N"NN9F%o6&*&<&< #\)0),C(,<<  *c**2ZI`   0@ A5B !/ 2  %' (  $48D -@@+D,?,?@ t224d ; 4??$9 : : OO / /) 0   IIKrocXR;a[SUS35eXlURUR(dURR U5 g[ X5 g)a Sets the active adapter. Only one adapter can be active at a time. Additionally, this function will set the specified adapter to trainable (i.e., requires_grad=True). If this is not desired, use the following code. ```py >>> for name, param in model_peft.named_parameters(): ... if ...: # some check on name (ex. if 'lora' in name) ... param.requires_grad = False ``` Args: adapter_name (`str`): The name of the adapter to be set as active. The adapter must be loaded first. rz not found.N)rdrrVrYr\ set_adapterr<)rbrOs rhr'PeftModel.set_adaptersZ& // /x ~[AB B* -@@ OO ' ' 5  ,rocURR(a-[SURRRS35eUR R XS9 g)ag Enable or disable gradients on the given adapter(s). Note: Not supported for prompt learning methods like prompt tuning. Args: adapter_name (`str` or `Sequence[str]`): The name of the adapter(s) whose gradients should be enabled/disabled. requires_grad (`bool`, *optional*) Whether to enable (`True`, default) or disable (`False`). zJSetting `requires_grad` is not supported for prompt learning methods like r)rIr1N)ryrYrrWr|r\set_requires_grad)rbrIr1s rhr*PeftModel.set_requires_gradsY  " " 5 5\**44::;1>  )) )croc0[URSS5$)Nr)r_r\rms rhr{ PeftModel.base_model_torch_dtypest66roc4URUR$rk)rdrVrms rhryPeftModel.active_peft_configs 3 344rocURRn[U[5(d URn/n[ UR S5(aD[UR R[R5(aURS5 US:XaURS5 [ UR S5(a(URSUR R35 U$)zDerive tags for the model card from the adapter's config. For example, setting the base model is important for enabling support for HF inference providers but it also makes models more searchable on the HF hub. rcrLORAlorarzbase_model:adapter:) ryrWrrrtr|r^r\rcrr rr)rb peft_methodtagss rh_get_peft_specific_model_tags'PeftModel._get_peft_specific_model_tagss --77 +s++%++K 4??G , ,DOO>(+C+Cy~~h'I`I`anapIq$* .!u((* :| , ,::j339r:Dzz$<<>?  &t DIIf yy%%*T;O*P*P%6DII " 11$7 +/AAt|  #<(G&2?&CDIIl # $$&" R=STU#; [M "% /&/ U[[)@AAEQ\P]G^_ 78 K=QRIIe$  ('/tsL ) rarZr[rVr\rdrWrrr.rXr2r9)rTF) rcr rdr1rOrtrPrGrLrGreturnNone)rQdict[str, PeftConfig])rQ list[str])rQrG)r|rS)TNrTN)rrtrrGrzOptional[list[str]]rzUnion[str, bool]rrGr Optional[str]rr rQrR)rFNTFFN)rctorch.nn.ModulerUnion[str, os.PathLike]rOrtrrGrRzOptional[PeftConfig]rPrGrrGrLrGrOptional[dict[str, str]]rr rQrF)rOrt)rcr )rOrtrQ torch.Tensor)NN)rintrOptional[torch.Tensor]rtz Optional[int]rQrY)rQztuple[int, int])rQrR)rrt)rr rr F)rQrVrOrtrdr1rLrGrQrR)rOrtrQrR)rQzOptional[set[str]])rQlist[TunerLayerStatus])rQTunerModelStatus)rzdict[str, Any])rzdict[str, dict[str, str]]rzdict[str, torch.tensor])rdr1rrGrQrR)FNTFFN)rrWrOrtrrGrrUrPrGrrGrLrGrrXrr )T)rIzstr | Sequence[str]r1rGrQrR)rrt).rr __qualname____firstlineno____doc__rUpropertyrdrqrysetterr classmethodrrKr`rNrhrrrrrrrdrrrr?r]rrrrrrr rr'r*r{ryr5r__static_attributes__ __classcell__rgs@rhrFrFHsfF&'+"' )()( )( )( !% )(  )( )()(V++ $GG00$(1528 $BFD8D8!D8/ D8 0 D8  D83@D8D8 D8L &"'+'+&+"'04SS*S S  S % S!%S $S S.SS SSjJX>  3,hl{{)?{Wd{ {z+< "2  :C )3030jh BH9&  &<&&P 4 4NL`#D#&*'+&+"'04`)`` ` $ ` !% ` $` `.``D-:d(7755,??rorFc^\rSrSrSrSS U4SjjjrS S U4SjjjrS SjrS SjrSr U=r $)"PeftModelForSequenceClassificationi=a Peft model for sequence classification tasks. Args: model ([`~transformers.PreTrainedModel`]): Base transformer model. peft_config ([`PeftConfig`]): Peft config. adapter_name (`str`, *optional*): The name of the adapter, defaults to `"default"`. autocast_adapter_dtype (`bool`, *optional*): Whether to autocast the adapter dtype. Defaults to `True`. Right now, this will only cast adapter weights using float16 and bfloat16 to float32, as this is typically required for stable training, and only affect select PEFT tuners. **Attributes**: - **config** ([`~transformers.PretrainedConfig`]) -- The configuration object of the base model. - **cls_layer_name** (`str`) -- The name of the classification layer. Example: ```py >>> from transformers import AutoModelForSequenceClassification >>> from peft import PeftModelForSequenceClassification, get_peft_config >>> config = { ... "peft_type": "PREFIX_TUNING", ... "task_type": "SEQ_CLS", ... "inference_mode": False, ... "num_virtual_tokens": 20, ... "token_dim": 768, ... "num_transformer_submodules": 1, ... "num_attention_heads": 12, ... "num_layers": 12, ... "encoder_hidden_size": 768, ... "prefix_projection": False, ... "postprocess_past_key_value_function": None, ... } >>> peft_config = get_peft_config(config) >>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased") >>> peft_model = PeftModelForSequenceClassification(model, peft_config) >>> peft_model.print_trainable_parameters() trainable params: 370178 || all params: 108680450 || trainable%: 0.3406113979101117 ``` c >^SS/n[US5(a3URc USSUlOURRU5 [TU]"XU40UD6 [US5(aQUR R 5H3umn[U4SjUR55(dM,TUl O [UU[USS5URS9 g)N classifierscorerc3,># UH oT;v M g7frkr module_namers rhr>PeftModelForSequenceClassification.__init__..|sS>R{d*>R module_namesr) r^rrrTrUr\r/rcls_layer_namer=r_r rbrcrdrOrclassifier_module_namesrrrgs @rhrU+PeftModelForSequenceClassification.__init__js$0"9 ; 1 2 2**2.Ea.H +++223JK \DVD ; 1 2 2??99;aSd>R>RSSS*.D'<    .?F&55  roc>[US5(a7SS/nURc USSUlOURRU5 [TU]XUS9$rrrlrmNrKr^rrrTr]rbrOrdrLrxrgs rhr].PeftModelForSequenceClassification.add_adapterb, ; 1 2 2'3W&= #**2.Ea.H +++223JKw"_prefix_tuning_forwardzerosr9rcrErrrbrrrrrrrrrrdr!rrprefix_attention_maskrs rhrd*PeftModelForSequenceClassification.forwardsl&1%e>41QFdFd=d$!$>e((HMM9)1:& '#1"/!&7)= +   ;:%Y>  %$)JJz;Y;Y$Z$]$]^l^s^s$t !"YY(='NTUVN ::nd + 7 MMq r%)F> " "0 %6(<*     H$:$: :..MMfM Mzz*D1=+099 J0N0NORRSWSgSgSnSnSuSuv/0 , $& '($ $ 4 4Y ? oooOGjj!4!45G!IIw&>AFM??II&I Iaf;:$ JJ 8J >>J J J c j[X5n URU 5n [[R"UR R 5RR55n URUUUUUUU S.5 SU ;aUR "SSU0UD6$UR RUR5n [[R"U R 5RR55n SU ;a [S5eU "S0UD6n [U 5S:aU SOU SnS[UR R55VVs/sHunnUPM snn;aUR RU5nUR RUR 5"U5nSnUGbUR"R$cUR R&S:XaSUR"lOyUR R&S:aNUR([*R,:XdUR([*R.:XaS UR"lOS UR"lUR"R$S:XaT[15nUR R&S:Xa&U"UR35UR355nOU"UU5nOUR"R$S :XaG[55nU"UR7S UR R&5UR7S 55nO-UR"R$S :Xa[95nU"UU5nU(dU4U S S-nUbU4U-$U$[;UUU R<U R>S 9$s snnf)NrrrrrrrrrLModel does not support past key values which are required for prefix tuning.r/rdropout regressionsingle_label_classificationmulti_label_classificationr_rlosslogits hidden_states attentionsro) r:rrsrrr\rdr0rrBr5r2rrr/rrvrR problem_type num_labelsrrrErZrsqueezerr|rr"rr)rbrrrrrrrrrr fwd_paramsr2outputs pooled_outputrrrrloss_fctrYs rhr9PeftModelForSequenceClassification._prefix_tuning_forwards%Y> //*5'++DOO,C,CDOOTTVW  &"0!.%6(<*#2    *??;&;F; ;(,(E(EdFdFd(e %g//0I0Q0QR]]bbdeJ 2 !opp/9&9G*-g,*:GAJ Mdoo6T6T6V1WX1WgdAT1WXX $ 7 7 F __2243F3FG VFD!;;++311Q63? 033a7V\\UZZ=W[a[g[gkpktkt[t3P 03O 0;;++|;&yH11Q6'(8&..:JK'7[[--1NN/1H#FKKDOO4N4N$OQWQ\Q\]_Q`aD[[--1MM02H#FF3D WQR[0-1-=&(I6I+%33"--  =YsN/rvrrcrVrdr1rOrtrQrRr\r]NNNNNNNNNNNNNNN rrr`rarbrUr]rdrrfrgrhs@rhrjrj=s*ZT] $ 3= MP   <ccB!@JH!EErorjc|^\rSrSrSrS S U4SjjjrS SjrSrSrSS.SS jjr S r U=r $)rDi1a Peft model for causal language modeling. Args: model ([`~transformers.PreTrainedModel`]): Base transformer model. peft_config ([`PeftConfig`]): Peft config. adapter_name (`str`, *optional*): The name of the adapter, defaults to `"default"`. autocast_adapter_dtype (`bool`, *optional*): Whether to autocast the adapter dtype. Defaults to `True`. Right now, this will only cast adapter weights using float16 and bfloat16 to float32, as this is typically required for stable training, and only affect select PEFT tuners. Example: ```py >>> from transformers import AutoModelForCausalLM >>> from peft import PeftModelForCausalLM, get_peft_config >>> config = { ... "peft_type": "PREFIX_TUNING", ... "task_type": "CAUSAL_LM", ... "inference_mode": False, ... "num_virtual_tokens": 20, ... "token_dim": 1280, ... "num_transformer_submodules": 1, ... "num_attention_heads": 20, ... "num_layers": 36, ... "encoder_hidden_size": 1280, ... "prefix_projection": False, ... "postprocess_past_key_value_function": None, ... } >>> peft_config = get_peft_config(config) >>> model = AutoModelForCausalLM.from_pretrained("gpt2-large") >>> peft_model = PeftModelForCausalLM(model, peft_config) >>> peft_model.print_trainable_parameters() trainable params: 1843200 || all params: 775873280 || trainable%: 0.23756456724479544 ``` c `>[TU]"XU40UD6 URRUlgrk)rTrUr\r(base_model_prepare_inputs_for_generationrbrcrdrOrrgs rhrUPeftModelForCausalLM.__init__Zs+ \DVD8<8e8e5roNc URn U R(d[XU40U D6n URRR S:Xa'Ub [ S5eUR"SUUUUUUS.U D6$U R[R:XaXS'UR"S0U D6 U R5V V s0sHupXR;dMX_M n n n UR"SUUUUUUUS.U D6sSSS5 $[X5n UbO[R"XR 5R#UR$5n[R&"X4SS9nU R)SS5b[*R,"S 5 SU S'U R)S S5b[*R,"S 5 SU S 'U R/UUUUUS .5 U R[R0:XabUbUR2SU R -nOUR2SU R -nUR5XS 9U S'UR"SXS.U D6$U R[R6:XaUR8"XXU 40U D6$UcUR;U5nUbU[R<"XR 4S5R#UR$5n[R&"UU4SS9U S'UR5XS9nUR#UR>5n[R&"UU4SS9nUR"SSU0U D6$s sn n f!,(df  GNY=f)Nmptz8forward in MPTForCausalLM does not support inputs_embeds)rrrrrrrrr/rkrrrXToken type ids are not supported for parameter efficient tuning. Ignoring token type idsr)rtr)rrrrrro) ryrYr%r\rRroAssertionErrorrWr8rrrrXr:rrrDrAr rrrrrBr>r8rr< _cpt_forwardr9fullr)rbrrrrrrrrrrdr!rrrrt prefix_labelsrs rhrdPeftModelForCausalLM.forward`sF-- --24M\U[\F%%00E9 ,()cdd'#1!&7)= + $$ 5%-z"00:6:+1<<>e>41QFdFd=d$!$>e '#1"/!&7)= +  ;:%Y>  %$)JJz;Y;Y$Z$]$]^l^s^s$t !"YY(='NTUVN ::nd + 7 MMq r%)F> " ::& - 9 MMt u'+F# $ "0 %6(<*     H$:$: :$ ) 2[5S5S S - 3 3A 69W9W W (, (`F$ %??^Y^W]^ ^  " "hll 2$$Y{V`kdjk k$ $ 4 4Y ? ! % J8V8V+WY] ^ a abhbobo p #(99mV-D!#Lx oooOGjj!4!45G!IIw &>AFM??II&I Iof;:s$*M >MMM M  Mc DURS5nXU4Vs/sHocMURPM snSn SUR5;a!URS5RU 5n OKUcURSn OURSn [ R "X[45RU 5S-n URn URn UcURU5nURXTS9nURUR5n[ R"X4SS9nSnUGb [ R"U 5R5RSS5nUR!US5RUR5n[ R"UU4SS9n[ R"U 5R5RSS5nUR!US5RUR5nU nUUS:==UR#5- ss'[ R"UU4SS9nUS:US-S:H-nS UU)'XS'UR$"S S U0UD6nUcU$[&UR(nUR+UUWUR,S 5nU$s snf) Nrrinput_type_maskr/rnrrkr_rrrro)popr rrAr8rr cpt_token_idscpt_tokens_type_maskr9rrrrrEr|rzmaxr\r4rWcalculate_lossrd)rbrrrdrrrrrr rN_tokensrrr cpt_labelsrprefix_type_maskadjusted_input_type_mask cpt_type_mask labels_idxbase_model_output cpt_embeddings rhr!PeftModelForCausalLM._cpt_forwardsH%%.v$FX$Fq(!(($FXYZ[  -$jj):;>>vFO (..q1$??1-#jj*)?@CCFKaOO#11 *??   00;M//Z/K**]001 7":B   !LL7<<>CCArJM)00Q?BB6==QMM6#:BJ$||,@AFFHMMaQST /66z1EHHW '6 $ $%=%A BFVFZFZF\ \ B!II'79Q&RXYZM'!+ 0AQ0FGJ&*J { #&x OOR-R6R >$ $7{7L7LMM - < <!:}d>N>Ny>Y! % $gYs JJcURnURURl[URS5(a&URURR lOURURlUR (d|[U/UQ70UD6nUR"U0UD6 UR5VVs0sHupEX@R;dMXE_M nnnURR"U0UD6nSSS5 OURR"S0UD6nURURlW$s snnf!,(df  N1=f! URURle=f)Nrcro) ryrr\r^generation_configrcrYr&rrrXrr)rbrrrdr!rrs rhrPeftModelForCausalLM.generates:-- 8<8Z8Z5 4??G , ,6:6L6LDOO ! ! 3040F0FDOO - 117NtNvN44dEfE/5||~i~tqJhJhAhdad~Fi"oo66GGGFE//22S9nU (aAUR[R@:wa#[4RB"UUS R:S9nOUSn[EURG5SUURS 5UUUURSS5S9nUUS'O[4RH"X4S S9US'URSS5bRL"S5 SUS'URSS5bRL"S5 SUS'URS S5nUSL=(d6 [!U[ RN5=(a UR)5(+nU(aUR[R@:Xas[!U[ RN5(a,[!U[ RP5(d URRnOS nURUUS R&SUS9nUUS 'OuU(anURWUS 5nURUUS R&SUS9nUR9UR>5n[4RH"UU4S S9US'SUS 'U (a7UR[R@:XaUS==UR2- ss'U$UR[R@:waURYSS5nU$)Nz4.38.0z4.36.0)llamamistral persimmonphiz4.43.3bloomrvrrrrr/r_rz&Expected a single attention mask, got za instead, please open an issue (https://github.com/huggingface/peft/issues) and report the error.rnr)rrur) model_inputrrrvrsequence_lengthrrkrrr)rrtrr)-ryrrrrrr0rr\rRrorrWr8rrYrrtuplersr8get_seq_lengthrrrrrDrrrAr rlrr>rCr-r?rrrrrrtrr9r)rbrrrrd model_kwargsuses_transformers_4_38uses_transformers_4_36transformers_new_cache_archs uses_cache is_prefillrseq_lenrsizerbs total_seq_lenattention_mask_2dcache_position_attention_mask_newcacherequires_prompt_injectionrtnew_past_key_valuesrrrs rhr2PeftModelForCausalLM.prepare_inputs_for_generation s-- DDdUfU "+!2!2!8!89Q9Q!RV_VgVgVmVmnvVw!w!*!2!2!8!89Q9Q!RV_VgVgVmVmnvVw!w'O$    " "<#;#; G;7==a@@0<[0I!RS&0QL-"."2"23CT"JJWnd33>*a/(DSEXDYZgg&*.*?*?*A%B1%EN#K066q9;;Y;YY(- 4(8(;(;L++-$('8(4(8(89J(K'6#%(5%1%5%5nd%K *&6HL!126;YY@U?flm5nL!125A uv/3 ^,zz*D1= n,0'(/;/?/?@QSW/XE).$)5,"4"45Te>R>R>T:T &)[-B-BhF\F\-\e\%7%788ES_SlSlAmAm$)$7$7M$&M&*oo+K8>>qA"/'6'#3F ./* $ 4 4\+5N O //\+5N5T5TUV5Wbj/k!**]%8%8905 7M:RXY0Z _-,0 [) ;00H4J4JJ ) *k.L.L L * " "h&<&< <   !148Aro)rrrr)rr[) rrr`rarbrUrdrrrrfrgrhs@rhrDrD1s&RT]f$f3=fMPf ff!YJv6%p.W[rorDc|^\rSrSrSrS S U4SjjjrS SjrSrSS.S SjjrS r U=r $)PeftModelForSeq2SeqLMiav Peft model for sequence-to-sequence language modeling. Args: model ([`~transformers.PreTrainedModel`]): Base transformer model. peft_config ([`PeftConfig`]): Peft config. adapter_name (`str`, *optional*): The name of the adapter, defaults to `"default"`. autocast_adapter_dtype (`bool`, *optional*): Whether to autocast the adapter dtype. Defaults to `True`. Right now, this will only cast adapter weights using float16 and bfloat16 to float32, as this is typically required for stable training, and only affect select PEFT tuners. Example: ```py >>> from transformers import AutoModelForSeq2SeqLM >>> from peft import PeftModelForSeq2SeqLM, get_peft_config >>> config = { ... "peft_type": "LORA", ... "task_type": "SEQ_2_SEQ_LM", ... "inference_mode": False, ... "r": 8, ... "target_modules": ["q", "v"], ... "lora_alpha": 32, ... "lora_dropout": 0.1, ... "fan_in_fan_out": False, ... "enable_lora": None, ... "bias": "none", ... } >>> peft_config = get_peft_config(config) >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") >>> peft_model = PeftModelForSeq2SeqLM(model, peft_config) >>> peft_model.print_trainable_parameters() trainable params: 884736 || all params: 223843584 || trainable%: 0.3952474242013566 ``` c >[TU]"XU40UD6 URRUlURR Ulgrk)rTrUr\rr._prepare_encoder_decoder_kwargs_for_generation8base_model_prepare_encoder_decoder_kwargs_for_generationrs rhrUPeftModelForSeq2SeqLM.__init__s@ \DVD8<8e8e5 OO J J EroNc   URn U R(dU R[R:XaXS'UR "S0U D6 U R 5VVs0sHupXR;dMX_M n nnUR"SUUUUUUUUU U S. U D6sSSS5 $[X5nUb[R"UU R5RUR5nU R[R[R 4;a[R""UU4SS9nU R%SS5b[&R("S5 SU S'U R%SS5b[&R("S5 SU S'U R+UUUUU U S .5 U R[R,:Xa*UR/U5U S 'UR"SUUUS .U D6$U R[R[R 4;aUcUR1U5nUbT[R"UU R5RUR5n[R""UU4SS9U S 'UR/US 9nURUR25n[R""USS2SU R24U4SS9nUR"SUUUS.U D6$UcUR1U5nUcHUcE[5XpR6R8UR6R:5nUR1U5nUbT[R"UU R5RUR5n[R""UU4SS9U S 'Ub{U R<S:XaX|S'OfU R<S:XaV[R>"UU R4S5RUR5n[R""UU4SS9U S'UR/UU S9nURUR25n[R""USS2SU R24U4SS9nU R<S:XaUR"SSU0U D6$U R<S:Xa>[R""USS2U RS24U4SS9nUR"SX6S.U D6$gs snnf!,(df  GN]=f)Nr) rrrdecoder_input_idsdecoder_attention_maskdecoder_inputs_embedsrrrrr/rkrrrr)rrrrrrr)rrrrr)rrrrrrrr)rrro) ryrYrWr8rrrrXr\r:rrrDrAr r:r=rrrrrBr>rr9rrDrR pad_token_iddecoder_start_token_idr3r)rbrrrrrrrrrrrrrdr!rrrrrs rhrdPeftModelForSeq2SeqLM.forwards-- --$$ 5%-z"00:6:+1<<>e>41QFdFd=d$!$>e '#1"/&7+A*?!&7)= +  ;: %Y> ! -$)JJz;;Y;Y$Z$]$]&--% !$$X-C-CXEVEV,WW).4IKa3bhi)j& ::nd + 7 MMq r%)F> " ::& - 9 MMt u'+F# $ "0*@ %6(<*     H$:$: :(, (CF$ %??#"3&;    " "x'='=x?P?P&Q Q$ $ 4 4Y ? )(- :{?]?](^(a(a")))%,1996K^5\bc+d'(ooo'-8$ ;;q@$)JJ K > @@ACXY_`)%"/`f =Ef;:s$S<&S6?S6S<6S<< T c URnURURlURURlUR(dnUR "S0UD6 UR 5VVs0sHup4X0R;dMX4_M nnnURR"S0UD6nSSS5 GO*SU;a [S5eURSS5b[R"S5 SUS'URSS5b[R"S5 SUS'UR[R:XaURR"S0UD6nGOUR[R [R"[R$4;Ga=['U5nSU;aUS [R"S5 UR)S5nUR+U5nUR,S nUR/XR)S S5S 9n U R1UR25n [4R6"U SS2SUR824U4S S 9nXqS'SU;aX[4R:"XR85R1USR<5n [4R6"XS4S S 9US'URR"S0UD6$[>eUR@URlURBURlW$s snnf!,(df  NL=f! UR@URlURBURle=f)Nrz4input_ids must be provided for Peft model generationrrrrencoder_outputsz[`encoder_outputs` should not be passed to `generate` when using prompt tuning. Ignoring it.rrrr/rkrrro)"ryrr\rrYrrrXrrrrrrWr8r>r:r=r;rrr9r8rrArrrrDrr NotImplementedErrorrr) rbrrdr!rrrrrrrs rhrPeftModelForSeq2SeqLM.generate< s-- 8<8Z8Z5  ? ? F? 1144>v>/5||~i~tqJhJhAhdad~Fi"oo66@@G?>f,$%[\\::nd3?MMo.2F>*::.5AMMr04F+,((H,B,BB"oo66@@G ****%%44/ &f-F(F2"#45 y!' ; 7I$($8$8$CM!.!4!4Q!7J"oojjYceiNjokG%jj)<)<=G$)IIwq:ZKS\lUm=ntu3v/0??33=f==--=Ad <@rrrrr8r) rbrrrrdrrrvis_prefill_stagerrs rhr3PeftModelForSeq2SeqLM.prepare_inputs_for_generation s-- DDdUfU  HMM 1'/ $  " "h&<&< <*../@$GO)--.>GN!D(U6SN1DDQG &*ooj&A#2E./ro)rrrr NNNNNNNNNNN)rrY) rrr`rarbrUrdrrrfrgrhs@rhrrs|%PT] $ 3= MP   #"!}~ENMQrorc^\rSrSrSrSS U4SjjjrS S U4SjjjrS SjrS SjrSr U=r $)PeftModelForTokenClassificationi a Peft model for token classification tasks. Args: model ([`~transformers.PreTrainedModel`]): Base transformer model. peft_config ([`PeftConfig`]): Peft config. adapter_name (`str`, *optional*): The name of the adapter, defaults to `"default"`. autocast_adapter_dtype (`bool`, *optional*): Whether to autocast the adapter dtype. Defaults to `True`. Right now, this will only cast adapter weights using float16 and bfloat16 to float32, as this is typically required for stable training, and only affect select PEFT tuners. **Attributes**: - **config** ([`~transformers.PretrainedConfig`]) -- The configuration object of the base model. - **cls_layer_name** (`str`) -- The name of the classification layer. Example: ```py >>> from transformers import AutoModelForSequenceClassification >>> from peft import PeftModelForTokenClassification, get_peft_config >>> config = { ... "peft_type": "PREFIX_TUNING", ... "task_type": "TOKEN_CLS", ... "inference_mode": False, ... "num_virtual_tokens": 20, ... "token_dim": 768, ... "num_transformer_submodules": 1, ... "num_attention_heads": 12, ... "num_layers": 12, ... "encoder_hidden_size": 768, ... "prefix_projection": False, ... "postprocess_past_key_value_function": None, ... } >>> peft_config = get_peft_config(config) >>> model = AutoModelForTokenClassification.from_pretrained("bert-base-cased") >>> peft_model = PeftModelForTokenClassification(model, peft_config) >>> peft_model.print_trainable_parameters() trainable params: 370178 || all params: 108680450 || trainable%: 0.3406113979101117 ``` c >^[TU]"XU40UD6 SS/n[US5(a3URc USSUlOURR U5 UR R 5H3umn[U4SjUR55(dM,TUl O [UU[USS5URS9 g)Nrlrmrc3,># UH oT;v M g7frkrorps rhr;PeftModelForTokenClassification.__init__.. O:N;$&:Nrsrt rTrUr^rrr\r/rrvr=r_rrws @rhrU(PeftModelForTokenClassification.__init__ s \DVD#/"9 ; 1 2 2**2.Ea.H +++223JK557GD!O$:N:NOOO&*#8    .?F&55  roc>[US5(a7SS/nURc USSUlOURRU5 [TU]XUS9$r{r|r}s rhr]+PeftModelForTokenClassification.add_adapter rroc FURn UbUOURRnU R(dUR"S 0U D6 U R 5V V s0sHupXR ;dMX_M n n n U R[R:XaXS'UR"S UUUUUUUS.U D6sSSS5 $[X5n UbO[R"XR5RUR 5n[R""X4SS9nU R%SS5b[&R("S5 SU S'U R+UUUUUS.5 U R[R,:XaUR."S SU0U D6$U R%S S5bv[R""[R0"XR5RUR2R4R 5U S 4SS9R75U S 'UcUR3U5nUR9XS 9nURUR:5n[R""X4SS9nUR"S S U0U D6$s sn n f!,(df  GN=fr)ryrRrrYrrrXrWr8rr\r:rrrDrAr rrrrrBr>rrr9rcrErrrs rhrd'PeftModelForTokenClassification.forward sl-- %0%e>41QFdFd=d$!$>e((HMM9)1:& '#1"/!&7)= +   ;:%Y>  %$)JJz;Y;Y$Z$]$]^l^s^s$t !"YY(='NTUVN ::nd + 7 MMq r%)F> " "0 %6(<*     H$:$: :..MMfM Mzz*D1=+099 J0N0NORRSWSgSgSnSnSuSuv/0 , $& '($ $ 4 4Y ? oooOGjj!4!45G!IIw&>AFM??II&I Iaf;:rc H[X5n URU 5n [[R"UR R 5RR55n URUUUUUUU S.5 SU ;aUR "S SU0UD6$UR RUR5n [[R"U R 5RR55n SU ;a [S5eU "S 0UD6n U SnS[UR R55VVs/sHunnUPM snn;aUR RU5nUR RUR5"U5nSnUb<[!5nU"UR#SUR$5UR#S55nU(dU4U SS-nUbU4U-$U$['UUU R(U R*S 9$s snnf) Nrrrrrrr_rrro)r:rrsrrr\rdr0rrBr5r2rr/rrvrr|rr#rr)rbrrrrrrrrrrrr2rsequence_outputrrrrrrYs rhr6PeftModelForTokenClassification._prefix_tuning_forwardB s%Y> //*5'++DOO,C,CDOOTTVW  &"0!.%6(<*#2    *??;&;F; ;(,(E(EdFdFd(e %g//0I0Q0QR]]bbdeJ 2 !opp/9&9G%ajOdoo6T6T6V1WX1WgdAT1WXX"&//"9"9/"J__2243F3FGXFD!+- B @&++b/R WQR[0-1-=&(I6I(%33"--  YsHr)Nrrr\r]rrrrhs@rhrr s*Z[d $ 3= TW   2ccB!AJJ!44rorc^\rSrSrSrSS U4SjjjrS S U4SjjjrS SjrS SjrSr U=r $)PeftModelForQuestionAnsweringiy a> Peft model for extractive question answering. Args: model ([`~transformers.PreTrainedModel`]): Base transformer model. peft_config ([`PeftConfig`]): Peft config. adapter_name (`str`, *optional*): The name of the adapter, defaults to `"default"`. autocast_adapter_dtype (`bool`, *optional*): Whether to autocast the adapter dtype. Defaults to `True`. Right now, this will only cast adapter weights using float16 and bfloat16 to float32, as this is typically required for stable training, and only affect select PEFT tuners. **Attributes**: - **config** ([`~transformers.PretrainedConfig`]) -- The configuration object of the base model. - **cls_layer_name** (`str`) -- The name of the classification layer. Example: ```py >>> from transformers import AutoModelForQuestionAnswering >>> from peft import PeftModelForQuestionAnswering, get_peft_config >>> config = { ... "peft_type": "LORA", ... "task_type": "QUESTION_ANS", ... "inference_mode": False, ... "r": 16, ... "target_modules": ["query", "value"], ... "lora_alpha": 32, ... "lora_dropout": 0.05, ... "fan_in_fan_out": False, ... "bias": "none", ... } >>> peft_config = get_peft_config(config) >>> model = AutoModelForQuestionAnswering.from_pretrained("bert-base-cased") >>> peft_model = PeftModelForQuestionAnswering(model, peft_config) >>> peft_model.print_trainable_parameters() trainable params: 592900 || all params: 108312580 || trainable%: 0.5473971721475013 ``` c >^[TU]"XU40UD6 S/n[US5(a3URc USSUlOURR U5 UR R 5H3umn[U4SjUR55(dM,TUl O [UU[USS5URS9 g)N qa_outputsrc3,># UH oT;v M g7frkrorps rhr9PeftModelForQuestionAnswering.__init__.. r rsrtr ) rbrcrdrOrqa_module_namesrrrgs @rhrU&PeftModelForQuestionAnswering.__init__ s \DVD'. ; 1 2 2**2.=a.@ +++22?C557GD!O$:N:NOOO&*#8    .?F&55  roc>[US5(a6S/nURc USSUlOURRU5 [TU]XUS9$)rrrNrKr|)rbrOrdrLrrgs rhr])PeftModelForQuestionAnswering.add_adapter s], ; 1 2 2+nO**2.=a.@ +++22?Cw"rrr9rcrErr)rbrrrrrr r!rrrrrrdr!rrrrs rhrd%PeftModelForQuestionAnswering.forward sz-- %0%e>41QFdFd=d$!$>e '#1"/$3"/&7)= +  ;:%Y>  %$)JJz;;Y;Y$Z$]$]^l^s^s$t !"YY(=~'NTUVN ::nd + 7 MMq r%)F> " "0#2!.%6(<*     H$:$: :..MMfM Mzz*D1=+099 J 0N0NORRSWSgSgSnSnSuSuv/0 , $& '($ $ 4 4Y ? oooAFM??II&I Iaf;:s$-JJJ JJ J&c [X5n URU 5n [[R"UR R 5RR55n U RUUUUUUU S.5 SU ;aUR "S XES.U D6$UR RUR5n [[R"U R 5RR55n SU ;a [S5eU "S 0U D6nUSnS[UR R55VVs/sHunnUPM snn;aUR RU5nUR RUR5"U5nUR!SSS 9unnUR#S5R%5nUR#S5R%5nSnUbUb['UR)55S:aUR#S5n['UR)55S:aUR#S5nUR)S5nUR+SU5nUR+SU5n[-US 9nU"UU5nU"UU5nUU-S - nU(dUU4US S-nUbU4U-$U$[/UUUUR0UR2S 9$s snnf)Nrr)r r!rrrr/r_rk) ignore_indexr)r start_logits end_logitsrrro)r:rrsrrr\rdr0rrBr5r2rr/rrvrr contiguousrrclamprr!rr)rbrrrr r!rrrrrrrr2rrrrrr%r& total_loss ignored_indexr start_lossend_lossrYs rhr4PeftModelForQuestionAnswering._prefix_tuning_forward% s%Y> //*5'++DOO,C,CDOOTTVW  &"0!.%6(<*#2    *??j?jcij j(,(E(EdFdFd(e %g//0I0Q0QR]]bbdeJ 2 !opp/9&9G%ajOdoo6T6T6V1WX1WgdAT1WXX"&//"9"9/"J__2243F3FGXF'-||A2|'> $L*'//3>>@L#++B/::>> from transformers import AutoModel >>> from peft import PeftModelForFeatureExtraction, get_peft_config >>> config = { ... "peft_type": "LORA", ... "task_type": "FEATURE_EXTRACTION", ... "inference_mode": False, ... "r": 16, ... "target_modules": ["query", "value"], ... "lora_alpha": 32, ... "lora_dropout": 0.05, ... "fan_in_fan_out": False, ... "bias": "none", ... } >>> peft_config = get_peft_config(config) >>> model = AutoModel.from_pretrained("bert-base-cased") >>> peft_model = PeftModelForFeatureExtraction(model, peft_config) >>> peft_model.print_trainable_parameters() ``` c *>[TU]"XU40UD6 grk)rTrUrs rhrU&PeftModelForFeatureExtraction.__init__ s \DVDroc ~URn U R(dU R[R:XaXxS'UR "S0UD6 UR 5V V s0sHupXR;dMX_M nn n UR"SUUUUUUS.UD6sSSS5 $[X5n UbO[R"XR5RUR5n [R"X4SS9nUR!SS5b["R$"S5 SUS'UR!SS5b["R$"S5 SUS'UR'UUUUS .5 U R[R(:Xa(UR+U 5US 'UR"SS U0UD6$UcUR-U5nUR+U S 9nURUR.5n[R"X4SS9nUR"SS U0UD6$s sn n f!,(df  GN=f)Nr)rrrrrrr/rkrrrr)rrrrrrrrro)ryrYrWr8rrrrXr\r:rrrDrAr rrrrrBr>rr9r)rbrrrrrrrrrdr!rrrrs rhrd%PeftModelForFeatureExtraction.forward s-- --$$ 5%-z"00:6:+1<<>e>41QFdFd=d$!$>e'#1"/&7)= + ;:%Y>  %$)JJz;Y;Y$Z$]$]^l^s^s$t !"YY(='NTUVN ::nd + 7 MMq r%)F> " ::& - 9 MMt u'+F# $ "0%6(<*     H$:$: :(, (CF$ %??AYA&A A$ $ 4 4Y ? oooAFM??II&I IUf;:s$H-&H'?H'H-'H-- H<ror)rcrVrdr1rOrtr) rrr`rarbrUrdrfrgrhs@rhr/r/m s8%NEE !;J;Jror/cf\rSrSr%S\S'S\S'S\S'S\S'S\S 'S \S 'S\S 'S \S'Srg)TunerLayerStatusi rtr module_typerGrrTrqmerged_adapters&dict[str, bool | Literal['irregular']]r1available_adaptersdict[str, list[str]]devicesroNrrr`ra__annotations__rfrororhr5r5 s0 I M99!! !!ror5c [U[5(a-URn[U[5(d [ S5eOUn/nUR 5GH|up4[U[ [45(dM#[U[5(aM:[R"[5nURHn[XF5n[U[RR 5(aOUR#5H9upU R%5H n XXR'U R(5 M" M; M[U[RR*5(a8UR#5H"upXXR'U R(5 M$ MM SSjn UR#5VV s0sH upX"U 5_M n nn [R"[5nURUR,-GHn[XF5n[U[RR 5(a`UR#5HJupXR/U R%5V s/sHoR0R2PM sn 5 ML M[U[RR*5(dUR4R6S:XdMUR#5H,upXR'U R0R25 M. GM UR#5VVs0sHupU[9[;U55_M nnn[=U[?U5RAS5SURB(+URDURFU [9URI55US9nUR'U5 GM U(d [KS5eU$s sn nfs sn fs snnf) aGet the status of each adapter layer in the model. This function returns a list of `TunerLayerStatus` dataclass instances, each of which contains the following attributes: - `name` (`str`): The name of the adapter layer, e.g. `model.encoder.block.0.layer.0.SelfAttention.q`. - `module_type` (`str`): The type of the adapter layer, e.g. `lora.Linear`. - `enabled` (`bool`): Whether the adapter layer is enabled. - `active_adapters` (`list[str]`): The names of the active adapters, if any, e.g. `["default"]`. - `merged_adapters` (`list[str]`): The names of the merged adapters, if any, e.g. `["default"]`. - requires_grad : dict[str, bool | Literal["irregular"]] The requires_grad status of the parameters for each adapter module. Ideally, it should be either `True` or `False`. If the requires_grad status is not consistent across all parameters, the value will be set to `"irregular"`. - `available_adapters` (`list[str]`): The names of the available adapters, e.g. `["default"]`. - `devices` (`dict[str, list[str]]`): The devices where the parameters of the given adapter are stored, e.g. `["cuda"]`. Args: model ([Union[`~PeftModel`, `~transformers.PreTrainedModel`, `nn.Module`]]): The model to get the adapter layer status from. Returns: list[`peft.peft_model.TunerLayerStatus`]: A list of dataclasses, each containing the status of the corresponding adapter layer. zjget_layer_status() got an invalid PeftModel instance; prefix tuning and adaption prompt are not supported.cH[U5(ag[U5(dgg)NTFr)allrvalss rhcheck_irrgular(get_layer_status..check_irrgular& s4yyt99ro BufferDict(r)rr6rrqr7r1r9r;zNo adapter layers found in the model, please ensure that it's a PEFT model or that you have PEFT adapters injected in the model.)rBz list[bool]rQbool | Literal['irregular'])&rrrFr\r'rr r(r)r,rrrsadapter_layer_namesr_rr,r-rr0rr1 ParameterDictother_param_namesrr rrgrrBr r5repr partitiondisable_adaptersrqr7_get_available_adaptersr)rcr\ layer_statusrrmapping_requires_grad_listadapter_module_nameadapter_moduler submodulerGrCrBr1 devices_ddrr;statuss rhrr sMD%##%% *i00 1  +-L"002 &>3K"LMM  f4 5 5 =H>&4&:&:&>u?R?RS"8'=NEHH,B,BCC"0"6"6"8JC.3::5;N;NO#9$> E_DdDdDfgDfysnT22Df g ,,T2 #)#=#=@X@X#X $VAN.%((*=*=>>&4&:&:&. s8namW]9R9Ramscd[SU55(ag[SU55(agg)Nc3(# UHoSLv M g7f)TNrorrs rhr;get_model_status..check_irrgular.. s+dsd{dTc3(# UHoSLv M g7f)FNrorbs rhrrc s,te|trdFr)r@rAs rhrC(get_model_status..check_irrgular s- +d+ + + ,t, , ,ro) rWrXrYrrZr[rrqr7r1r9r;)rBz!list[bool | Literal['irregular']]rQrG)#rrrFr\r'rr?rgrrrdrrtrWrLr rrrrrBr rArrqrsrBr7r9rrr1rr;rr_)rcrWrrZr\rrRrYrXrOr[rU enabled_setrr9all_active_adaptersrq merged_allr7unmergedrequires_grad_allrrCrBr1rTr;adapter_model_statuss rhrra sX%##%**I66  ..0::CC).)J)J)L&%% WaWmWmWsWsWuvWu c3v//0::3?CCWu v'11:: E? + +//22)2)N)Nu)U&  #!)2)N)Nu)U&  ##E*L\*;GH<nnr?r@rArBrCrDr,ModulerFrjrDrrrr/r5rr_rrrororhrsV#  $2!00 <^^FSS!9AARRww-c>/36llii(rrj/qqhX9Xv KIK\\i\~qIqhfJIfJR "" "l^  " "  "Q h Kro