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Returns: Path: The local file path. ) *.jsonmodel*.safetensors*.pytokenizer.model *.tiktokentiktoken.model*.txt*.jsonl*.jinja)rr)rexistsr)rrr model_paths r= _downloadrsN o&J    '  ,  !-   r?c.tt|dS)NT)local_files_only)rr)hf_repos r=hf_repo_to_pathrs !'DA BBr?rcxt|dz d5}tj|}ddd|dz }|jrFi} t|d5}tj|}ddd|j ddx}r|d<S#1swYfxYw#1swY0xYw#tj$rYHwxYw)N config.jsonrgeneration_config.json eos_token_idF)openjsonrrJSONDecodeErrorrR)rfrugeneration_config_filegeneration_configrs r= load_configrs j=(# .!1 /(*BB$$& ,c2a$(IIaL!3 -00G G< G%1F> " M / .32##   s5B  B#B(B# BB B##B98B9FTlazystrict model_configget_model_classesct||j|tjt|dz }|s|rt d|i|D]&}jt j |(jdx}vtjjd||z }tjj|} |jj| | j| j} } n |\} } dvrjdi} d| vr| dd<| j!} | | t#d rj%fd }jd dx} ||njdd x}r{|d }|dk(rddlm}||na|dk(rdddd}|d <|d<||nC|dk(rdddd}|d <|d<||n%|dvr!t+|\}|d <|d<||jdd rHd}t-|j/t0j2j4}j7|j9j;t=j?||s#t j8jAfS)aB Load and initialize the model from a given path. Args: model_path (Path): The path to load the model from. lazy (bool): If False eval the model parameters to make sure they are loaded in memory before returning, otherwise they will be loaded when needed. Default: ``False`` strict (bool): Whether or not to raise an exception if weights don't match. Default: ``True`` model_config (dict, optional): Optional configuration parameters for the model. Defaults to an empty dictionary. get_model_classes (Callable[[dict], Tuple[Type[nn.Module], Type]], optional): A function that returns the model class and model args class given a config. Defaults to the ``_get_classes`` function. Returns: Tuple[nn.Module, dict[str, Any]]: The loaded and initialized model and config. Raises: FileNotFoundError: If the weight files (.safetensors) are not found. ValueError: If the model class or args class are not found or cannot be instantiated. NrzNo safetensors found in model_file custom_model)rurA text_configsanitizec rfd}tj|d|d|jdd|y)NcJ|dvrd|St|dsy|dvS)N quantization to_quantizedFrF)r)prrur@s r=class_predicatez6load_model.._quantize..class_predicateQs>F>**n-a001n-S=G+ +r?rCr5modeaffine)rCr5rr)rquantizerR)rrrurr@s r= _quantizezload_model.._quantizePs< , #L1f%!!&(3+  r?rF quant_methodbitnetr)bitnet_quantizemxfp4r,r+rCr5rzcompressed-tensorsr)awqgptqquantize_activationscjt|tjr|jdvr t d|j ddr t d|j j\}}|d|jzz}tj|||j|j|jS|S)N)nvfp4mxfp8z8Mode ({m.mode}) does not support activation quantizationrFz?Linear layer with bias does not support activation quantizationr,) rrQuantizedLinearrrSrRrkr1r5QQLinearrC)rout_dimsin_dimss r= _maybe_qqzload_model.._maybe_qq~s!R//066!33$R55'$Y%&HHNN!'2<'{{7HallAFFAFFSSr?r)r)!rupdateglobstrFileNotFoundErrorr2rrRryutilspec_from_file_locationmodule_from_specloader exec_moduler|r} from_dictrrmodels.bitlinear_layersrrtrrrr is_moduleupdate_moduleseval load_weightsrTr_r)rrrrr weight_fileswfrspecr~ model_classmodel_args_classr model_argsrrrArrrleavesrurr@s @@@r= load_modelrs< $F l#99S.B!BCDL F"::, GHHGrwwr{#jj.. ;~~55   # ~~..t4 %(, DNN% (9(H% %F*jj3 K /,78M,NF( )!++F3J  #Euj!..) " >488 E, & +@% H H  H*>: 8 # @#E+>?E W $*,aIL%1F> ",8F( ) l # 1 1*,aJL%1F> ",8F( ) l # _ ,$:7DW$X !G\%1F> ",8F( ) l # zz(%0  )U%7%7%9299CVCVW V$ JJL tGMMO,V<    "# &=r?r adapter_pathc ddlm}|||S)Nr) load_adapters) tuner.utilsr)rr_load_adapterss r=rrs< % ..r?c<t|gd}t|||S)z`Load a huggingface tokenizer and try to infer the type of streaming detokenizer to use. rrrrrrrrr eos_token_ids)r_load_tokenizer)rtokenizer_config_extrars r=load_tokenizerrs.   J # r?tokenizer_config return_configct||}t|||\}} |t||}|jt ||| j dd} |r|| | fS|| fS)aT Load the model and tokenizer from a given path or a huggingface repository. Args: path_or_hf_repo (Path): The path or the huggingface repository to load the model from. tokenizer_config (dict, optional): Configuration parameters specifically for the tokenizer. Defaults to an empty dictionary. model_config(dict, optional): Configuration parameters specifically for the model. Defaults to an empty dictionary. adapter_path (str, optional): Path to the LoRA adapters. If provided, applies LoRA layers to the model. Default: ``None``. lazy (bool): If ``False`` eval the model parameters to make sure they are loaded in memory before returning, otherwise they will be loaded when needed. Default: ``False`` return_config (bool: If ``True`` return the model config as the last item.. revision (str, optional): A revision id which can be a branch name, a tag, or a commit hash. Returns: Union[Tuple[nn.Module, TokenizerWrapper], Tuple[nn.Module, TokenizerWrapper, Dict[str, Any]]]: A tuple containing the loaded model, tokenizer and, if requested, the model config. Raises: FileNotFoundError: If config file or safetensors are not found. ValueError: If model class or args class are not found. )r)rNrr)rrrrrrR) rr rrrr rrrru tokenizers r=rrsxH?X>Jz4lKME6e\2 $FJJ~t4TIi''ir?pipeline_group tensor_groupct|gd}t|dd\}}t|dxrt|jd}t|d}| |s t d | |s t d |s |s t d ||cxurDnnA|rt j j}n |rt j j}||jj|t|d z d 5} tj| d} dddt} t|jD]:\} }  j| ddux}r t d| j!| | <t|| n t|t#|ddi|jdd}t|dd\}} ||j%|||jj|t j&|jt j&t j j)t j*dt j,|r|||fS||fS#1swYgxYw)NrrTF)rrrpipelineshardzGThe model does not support pipelining but a pipeline_group was providedzMThe model does not support tensor parallelism but a tensor_group was providedz'The model does not support any shardingmodel.safetensors.index.jsonr weight_mapz>..0L ^^002N! ^,*==s Cs99S>,7LDe  !1!1!34DAq(,,Q5==y= R OOLO , 5 ${3$ d#jj6I *4>HE1 L!! ^,GGE   GGBNN " "288C= " @Ai''iED Cs 5I33I=cVt|tjjd|SrL)r#r2rr)rr s r= pipeline_loadr%Ds bnn113T= IIr?max_file_size_gbc|dz}g}id}}|jD]@\}}||jz|kDr|j|id}}|||<||jz }B|j||S)z Splits the weights into smaller shards. Args: weights (dict): Model weights. max_file_size_gb (int): Maximum size of each shard in gigabytes. Returns: list: List of weight shards. r)r_rappend)r@r&max_file_size_bytesshardsr shard_sizerqrs r= make_shardsr-Hs+b0 FA:E 1  #6 6 MM% "A:Eaahh   MM% Mr?pathhf_pathcddlm}m}||j|d}n|j |}d|j _d|j _|j jdg|j _n8d|j jvr |j xjdgz c_|t||j _ d|_ |jtjj|d y) z Uploads the model to Hugging Face hub. Args: path (Union[str, Path]): Local path to the model. hf_path (Union[str, Path, None]): Path to the original Hugging Face model. r) ModelCard ModelCardDataNen)languagemlxztext-generation README.md)huggingface_hubr1r2 from_templaterdata library_name pipeline_tagtagsr base_modeltextsaveosr.join)r.r/r1r2cards r=create_model_cardrD`s9&&}d'CD~~g&"DII.DII yy~~  diinn $ 5'!"7| DIIIbggll4-.r? upload_repoc ddlm}m}m}ddlm}|j t|dz }|j|}|jj}|d|d|d |d|d |d } nd } td |d| d|d|_ |j||} | j|d| j||dt!d|dy)z Uploads the model to Hugging Face hub. Args: path (str): Local path to the model. upload_repo (str): Name of the HF repo to upload to. r)HfApir1loggingr) __version__r7Nz This model [z](https://huggingface.co/z,) was converted to MLX format from [z!) using mlx-lm version **z **. r6z # z z ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("av") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_dict=False, ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ``` T)repo_idexist_okr) folder_pathrJ repo_typez0Upload successful, go to https://huggingface.co/z for details.)r8rGr1rHr6rIset_verbosity_inforrr:r>rr?r@ create_repoupload_large_folderprint) r.rErGr1rHrI card_pathrCr/ provenanceapis r= upload_to_hubrUzs:9  T [(I >>) $Dii""G M!:;-H''.i/H R +}-    -  "#. /  DI6 IIi 'COOK$O7  <[M WXr? donate_model save_pathrWct|tr t|}|jddt t |j }t|}t|}|dkDrdnd}td|jD}|t|did}|r*|jtd |j |j~tt|D]g} || } d || <|j!| dz|} || z } t#j$t| | d d i | j'D] } | |d| < ~ it)|dDcic] }||d| c}|d<t+|dz d5}t-j.||dd d d y cc}w#1swYy xYw)z?Save model weights and metadata index into specified directory.T)parentsrKrz"model-{:05d}-of-{:05d}.safetensorszmodel.safetensorsc34K|]}|jywrL)r)rNrs r=rQzsave_model..s8'7!QXX'7s) total_sizetotal_parameters)metadatarc,tjgSrL)r2r3)rs r=rzsave_model..s  r?Nformatr5)r^rrrrr+indent)rrrmkdirdictrrr-lenrZvaluesrrrclearranger`r2save_safetensorsrUsortedrrdump)rXrrWr@r+ shards_countshard_file_formatr\ index_datair shard_name shard_path weight_namerqrs r= save_modelrss)S!O  OOD4O0< 0 0 234G  !Fv;L !  -  8w~~'788J% 4U ;  J X4e6F6F6HIJ MMO 3v; q q &--a!e\B +  C OUh=NO ::J| $[ 1(  17z,7O0P 0P1:l #A &&0P J| i88# >!    ? >   ? >s,F3F88GrCr5rquant_predicatec4 d}tj| xs t|dd|||\}||d d vrd nd d<   fd}tj||||  d d <t |}t d |d d | fS)a Applies quantization to the model weights. Args: model (nn.Module): The model to be quantized. config (dict): Model configuration. group_size (Optional[int]): Group size for quantization. bits (Optional[int]): Bits per weight for quantization. mode (str): The quantization mode. quant_predicate (Callable): A callable that decides how to quantize each layer based on the path. Accepts the layer `path` and the `module`. Returns either a bool to signify quantize/no quantize or a dict of quantization parameters to pass to `to_quantized`. Returns: Tuple: Tuple containing quantized model and config. c8ddddd}||\}}|xs||xs|fS)N)@r+)r,r+)r+)r,r)rrrrr)rrCr5 mode_defaultsdefault_group_size default_bitss r=defaults_for_modez)quantize_model..defaults_for_modes=  ,9+>(L//1EEEr?rtNrrTFct|dsy|jjdzdk7ryd} ||}t|tr |d|<|Sr |rd|<|S)NrFrHrTr)rrkr1rrd)r.modulebool_or_paramsfine_grained_configrC quant_paramsrtquantized_configs r=wrapped_predicatez)quantize_model..wrapped_predicate)sv~. ==  r "Z /1 4  &,T6:N nd +5C ^ ,T 2!^5A ^ ,T 2r?)rrrAz[INFO] Quantized model with z.3fz bits per weight.)copydeepcopygetattrrrrrQ) rrurCr5rrtr|rbpwrrrs ` ` @@@r=quantize_modelrs4F}}V,%P8I4)PO(z4@J",dDIL))##+7(  KK  ) /?~.N*+ !% (C (S 1B CD " ""r?c ddlm}m}g}|jD]\}}d|v}t |t j rt j}d|i}nAt |t jri}t j}nt ||rd|i}|}n{tj|j|j|j|j|j |j"} | j$ddd} || i|} |r|j&| _| | _ |j)|| ft+|dkDr|j-t/||S)z Dequantize the quantized layers in the model. Args: model (nn.Module): The model with quantized layers. Returns: nn.Module: The model with dequantized layers. r)QuantizedSwitchLinear SwitchLinearrNrHr)models.switch_layersrr named_modulesrrrLinearQuantizedEmbedding Embeddingr2 dequantizerkrfrnrCr5rr1rr)rerr) rrrdequantize_layersnamer~rclskwargsrkargsrs r=dequantize_modelrGs8J++- f fb00 1))Cd^F  5 5 6F,,C  5 6d^FC  MM MM MM    KK KK  ||DbD!    [[AF  $+5.8 ! ^,=>? Lr? config_pathc|jdd|jddd|vr|d|d<tt|j}t |d5}t j ||ddddy#1swYyxYw) a Save the model configuration to the ``config_path``. The final configuration will be sorted before saving for better readability. Args: config (dict): The model configuration. config_path (Union[str, Path]): Model configuration file path. _name_or_pathN vision_configrrArrr+ra)poprdrjr_rrrk)rurrs r= save_configrusy JJ% JJ%(.~(>$%&( )F k3 3 &#a(  s BB dst_pathsrc_path_or_repor clt|}|js|}t|}nd}t|}t||dt ||dz |j |dD]>}t j t||z D]} tj| |@t||y)NTrVr)r)rr) rrrrsrsave_pretrainedrrshutilrrD) rrrr rurWsrc_pathrrfiles r=r@r@s$%H ?? ""7+H~HxT2H}$<= h' /IIc(Q,/0D KKh '10h(r?ctt|t|}t|D]}||||k7s|cS|S)a$ Calculates the length of the common prefix of two lists. Args: list1: The first list of strings. list2: The second list of strings. Returns: The length of the common prefix. Returns 0 if lists are empty or do not match at the first element. )minrerh)list1list2min_lenros r=common_prefix_lenrsD#e*c%j)G7^ 8uQx H Nr?c tj|j}d|jvS#tt f$rYywxYw)z Check if the model supports input_embeddings in its call signature. Args: model (nn.Module): The model to check. 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