Huggingface save and load model

Huggingface save and load model. index). PathLike], use_diff: bool = True): """. state_dict(), path), the model will be saved twice (because I used two gpus) In the PyTorch DDP example, they save the model only when the rank is 0, which avoid saving the model multiple times. A path or url to a tensorflow index checkpoint file (e. encode(sentences) I came across some comments about. A dataset is a directory that contains: some data files in generic formats (JSON, CSV, Parquet, text, etc. Jun 3, 2023 · Hi, I am having problems trying to load a model after training it. GPU inference. Get started. Jul 17, 2021 · You can’t use load_best_model_at_end=True if you don’t want to save checkpoints: it needs to save checkpoints at every evaluation to make sure you have the best model, and it will always save 2 checkpoints (even if save_total_limit is 1): the best one and the last one (to resume an interrupted training). How can I do that with accelerate? Thanks! The timm library has a built-in integration with the Hugging Face Hub, making it easy to share and load models from the 🤗 Hub. DeepSpeed ZeRO-3 can be used for inference as well since it allows huge models to be loaded on multiple GPUs, which won’t be possible on a single GPU. In TRL we provide an easy-to-use API to create your SFT models and train them with few lines of code on your dataset. You could also use a distilled Stable Diffusion model and autoencoder to speed up inference. SM_CHANNEL_TRAIN, SM_CHANNEL_MODEL or directly from, e. output_dir) means I have save a trained model, not just a checkpoint? I try many ways to load the trained model but errors like 500. If you call it after Trainer. 122,511. The models argument are the models as saved in the accelerator state under accelerator. This is my project if you want to look at it! So from now, if you look at the section “Load Model” of my colab file, I have the model saved in 3 different ways: from here, how can I convert it into an unique TFLite or Load a dataset from the Hugging Face Hub, or a local dataset. json, pytorch_model. co' to load this file, couldn't find it in the cached files and it looks like google/vit-base-patch16-224 is not the path to a directory containing a file named config. 2 Answers. Load a tokenizer with AutoTokenizer. What am I doing wrong? Am i supposed to do something with the pytorch_model. As we did in the previous example with the recommender model, we can create a Python class that inherits from PythonModel and then place everything we need there. Something like this: Model sharing and uploading. Hence, the correct way to load tokenizer must be: tokenizer = BertTokenizer. Models. safetensors is a safe and fast file format for storing and loading tensors. Aug 9, 2022 · In google Colab, after successfully training the BERT model, I downloaded it after saving: trainer. I can’t figure out how to save a trained classifier model and then reload so to make target variable predictions on new data. Learn more about the quantization method in the LLM. During distillation, many of the UNet’s residual and attention blocks are shed to reduce the model size. However, pickle is not secure and pickled files may contain malicious code that can be executed. PyTorch offers parallelized data loading, retrieving batches of indices instead of individually, and streaming to iterate over the dataset without downloading it on disk. In the provided Python code, we are creating a custom cache path (D:/huggingface_cache/) for the hugging face model. Control how a dataset is loaded from the cache. It does not work for subclassed models, because such models are defined via the body of a Python method, which isn't safely serializable. However, with this setup, a much higher compression of images can be achieved. To upload your model, just type. /my_model_directory/. load_pretrained(), etc. def load_peft_model(): peft_model_id = "DioulaD/falcon-7b-instruct-qlora-ge-dq-v2". save/torch. g, . The base classes PreTrainedModel, TFPreTrainedModel, and FlaxPreTrainedModel implement the common methods for loading/saving a model either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace’s AWS S3 repository). Trainer( model=model, train_dataset=data["train"], args=transformers. Here are the things you can do using bitsandbytes integration. Otherwise it’s regular PyTorch code to save and load (using torch. from_pretrained(model, adapter_model_name) model = model. Jun 21, 2023 · How do I load an SFTTrainer model finetuned falcon-7b-sharded-bf16 using custom dataset, and make prediction with it Model heads At this point, you have a base DistilBERT model which outputs the hidden states. from sentence_transformers import SentenceTransformer # Load or train a model model = SentenceTransformer() # Push to Hub model. # Create and train a new model instance. A tokenizer converts your input into a format that can be processed by the model. Below is the Python code to save huggingface model to any disk or directory. This function also facilitates the device to load the data into (see Saving & Loading Model Nov 2, 2023 · now, without internet, and without requiring the pretrained weight, how to load this model? Until now, it gives. json + model. models. It will make the model more robust. ckpt. There are several ways you can increase the speed your data is loaded which can save you time, especially if you are working with large datasets. Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. model = SentenceTransformer( 'model_name') Here is an example that encodes sentences and then computes the distance between them for doing semantic search. Typically, PyTorch model weights are saved or pickled into a . As an example, I trained a model to predict imbd ratings with an example from the HuggingFace resources, shown below. One very classic use case is in transformers the embeddings are shared with lm_head. For example, for save_total_limit=5 and load_best_model_at_end, the four last checkpoints will always be retained alongside the best model. As @mihai said, it saves the model currently inside the Trainer. The pre-trained models on the Hub can be loaded with a single line of code. How does one initialize a pipeline using a locally saved Jul 20, 2022 · Hi there, I saved the model using the following code, that load a pre-trained model, save the model, and then load/save the model in the TF version: from transformers Load safetensors. It can be one device for the whole module, or a dictionary mapping module name to device. train() trainer. One can use SegformerImageProcessor to prepare images and corresponding segmentation maps for the model. The load_dataset() function fetches the requested dataset locally or from the Hugging Face Hub. Download pre-trained models with the huggingface_hub client library, with 🤗 Transformers for fine-tuning and other usages or with any of the over 15 integrated libraries. nn. Each derived config class implements model specific attributes. ← Gated Models Downloading Models →. This function uses Python’s pickle utility for serialization. Check out a complete flexible example at examples/scripts/sft. load_state_dict(torch. For BLOOM using this format enabled to load the model on 8 GPUs from 10mn with regular PyTorch weights down to 45s. Load those weights inside the model. Supervised Fine-tuning Trainer. 0 checkpoint, please set from_tf=True. Audio. I am starting to think that Huggingface has low support to tensorflow and that pytorch is recommended. To create a new repository, visit huggingface. SyntaxError: Unexpected token < in JSON at position 4. torch. Can anyone tell me how can I save the bert model directly and load directly to use in production/deployment? Jul 20, 2022 · When i try to load tf_model. Jan 16, 2023 · It doesn't help to just add a dict of save/load_pretrained without being able to save multiple models in different subfolders (otherwise things would overwrite each other). from_pretrained(<Path to the directory containing pretrained model/tokenizer>) In your case: Apr 26, 2022 · To save a Huggingface dataset or repo, you can follow these steps: First, make sure you have Git installed on your system. We’re on a journey to advance and democratize artificial intelligence through open source and open science. The folder doesn’t have config. h5 (or even TenforFlowLIte) that contains my model that i can load and get the whole model! Aug 16, 2023 · I'm trying to save the microsoft/table-transformer-structure-recognition Huggingface model (and potentially its image processor) to my local disk in Python 3. " Finally, drag or upload the dataset, and commit the changes. The SavedModel guide goes into detail about how to serve/inspect the SavedModel. NotImplementedError: Saving the model to HDF5 format requires the model to be a Functional model or a Sequential model. The base class PretrainedConfig implements the common methods for loading/saving a configuration either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace’s AWS S3 repository). 🤗 Transformers Quick tour Installation. Load a model as a backbone. Wherever a dataset is stored, 🤗 Datasets can help you load it. Hello. With LoRA, it is much easier to fine-tune a model on a custom dataset. Supervised fine-tuning (or SFT for short) is a crucial step in RLHF. Thank you for your assistance. This will upload the folder containing the weights, tokenizer and configuration we prepared in the previous section. As indicated here as well, there are different ways to save the best checkpoint. Oct 17, 2020 · return pred. The section below illustrates the steps to save and restore the model. save_vocabulary (), saves only the vocabulary file of the tokenizer (List of BPE tokens). In this page, we will show you how to share a model you have trained or fine-tuned on new data with the community on the model hub. Navigating the Model Hub. save_pretrained("merged_adapters") Once you have the model loaded and either merged the adapters or keep them separately on top you can run generation as with a normal model outlined Oct 17, 2020 · return pred. Your data can be stored in various places; they can be on your local machine’s disk, in a Github repository, and in in-memory data structures like Python dictionaries and Pandas DataFrames. . /opt/ml/input/train. I’d like to inquire about how to save the model in a way that allows consistent prediction results when the model is loaded. To keep up with the larger sizes of modern models or to run these large models on existing and older hardware, there are several optimizations you can use to speed up GPU inference. Here is the code I use to load and run the model. Sep 17, 2020 · Method 2: Save model to custom cache directory. save_safetensors (bool, optional, defaults to True Method1: Save Huggingface model to custom folder path. json file for this custom model ? When I load the custom trained model, the last CRF layer was not there? from torchcrf import CRF Load. Using huggingface-cli: To download the "bert-base-uncased" model, simply run: $ huggingface-cli download bert-base-uncased Using snapshot_download in Python: May 2, 2021 · when I use Accelerator. Uploading models Using the web interface Upload from a library with built-in support Upload a Py Torch model using huggingface_hub Using Git. Optionally, you can join an existing organization or create a new one. save_model(script_args. save(unwrapped_model. This guide will show you how to: Change the cache directory. This guide will show you how to load a dataset from: Pick a name for your model, which will also be the repository name. Thank you very much for the detailed answer! 3 days ago · Models saved in this format can be restored using tf. Stable Cascade consists of three models: Stage A, Stage B and Stage C, representing a cascade to generate images, hence the name "Stable Cascade". TrainingArguments( per_device_train_batch_size=1, gradient_accumulation_steps=8, warmup_steps=2, max_steps=20, learning_rate=2e-4, fp16=True, logging_steps=1, output_dir="outputs", optim="paged Sep 8, 2021 · model = Model(model_name=model_name) model. load ). save and load the model WITH the linear layer, not separately). cache\huggingface) to this location ( D:/Question_Answering/model Dec 8, 2022 · Model description I add simple custom pytorch-crf layer on top of TokenClassification model. I have tested it in Colab and it works perfectly. Mar 8, 2013 · save_total_limit will control the number of checkpoints being saved, so with save_total_limit=2: when load_best_model_at_end=True, you have the best model and the last model (unless the last model is the best model in which case you have the two last models) when load_best_model_at_end=False, you have the last two models Oct 20, 2020 · huggingface - save fine tuned model locally - and tokenizer too? bert-language-model, huggingface-transformers asked by ctiid on 01:37PM - 20 Oct 20 UTC Jun 23, 2022 · Go to the "Files" tab (screenshot below) and click "Add file" and "Upload file. Apr 17, 2022 · Saving a HuggingFace model with Mlflow. gfile. There are three kinds of repositories on the Hub, and in this guide you’ll be creating a model repository for demonstration purposes. Module) — The module where we want to attach the hooks. train (), since load_best_model_at_end will have reloaded the PEFT. TrainingArguments( per_device_train_batch_size=1, gradient_accumulation_steps=8, warmup_steps=2, max_steps=20, learning_rate=2e-4, fp16=True, logging_steps=1, output_dir="outputs", optim="paged module (torch. Diffusers now provides a LoRA fine-tuning script that can run Jul 25, 2022 · My purpose is to convert my model folder (config. Oct 2, 2021 · else: config_dict = self. If the dataset only contains data files, then load_dataset() automatically infers how to load the data files from their extensions (json, csv, parquet, txt, etc. First, you’ll need to make sure you have the huggingface_hub package installed. bin file ? Trying to load model from hub: yields. from_pretrained(config. save_pretrained ('modeldir') How can I re-instantiate that model from a different system. I encountered an issue where the predictions of the fine-tuned model after training and the predictions after loading the model again are different. device], optional) — The device on which inputs and model weights should be placed before the forward pass. Aug 22, 2023 · And I save the checkpoint and the model in the same dir. I train the model successfully but when I save the mode. Tutorials. save (model. This is supported by most of the GPU hardwares since the 0. Note that this image processor is fairly basic and does not include all data augmentations used in the original paper. from Mar 5, 2023 · Save and Load fine-tuned Huggingface Transformers model from local disk. Computer Vision Apr 8, 2021 · OlivierCR April 8, 2021, 5:43pm 1. GFile object at 0x7f83009fb110> What i am trying to do is having ONLY ONE file . Nov 25, 2022 · torch. from_pretrained() method automatically detects the correct pipeline class from the checkpoint, downloads, and caches all the required configuration and weight files, and returns a pipeline instance ready for inference. Now the dataset is hosted on the Hub for free. Using existing models. save_model("distilbert_classification") The downloaded model has three files: config. merge_and_unload() model. If you use save_total_limits=2 and load_best_model_at_end=True, then the latest and the best model will be saved. patch_sizes. . In your case, the tokenizer need not be saved as it you have not changed the tokenizer or added new tokens. 37. You (or whoever you want to share the embeddings with) can quickly load them. g. pt') Now When I want to reload the model, I have to explain whole network again and reload the weights and then push to the device. Models, tensors, and dictionaries of all kinds of objects can be saved using this function. keras. So in short, if you can think of a solution to create folder names and save models in separate folders that would be great, if it's too much totally fine - we leave things Mar 9, 2022 · I want to extend a transformer model (let’s take bert, electra, etc for example) with a linear layer and initialize the linear layer with the same initializer as the transformer model. 122,179. GPUs are the standard choice of hardware for machine learning, unlike CPUs, because they are optimized for memory bandwidth and parallelism. This code is to copy the model files from this location ( C:\Users\Anindya. 10. PEFT methods only fine-tune a small number of (extra) model parameters - significantly decreasing computational Jan 26, 2023 · LoRA fine-tuning. def to_json_file(self, json_file_path: Union[str, os. to_dict() return json. Distilled model. save_to_hub("my_new_model") Feb 10, 2022 · According to here pipeline provides an interface to save a pretrained pipeline locally with a save_pretrained method. co for this. ). Load the model weights (in a dictionary usually called a state dict) from the disk. This is extremely interesting to reduce memory usage in general. Task Guides. load : Uses pickle ’s unpickling facilities to deserialize pickled object files to memory. tokenizer = AutoTokenizer. , you can’t use DistilBERT for a sequence-to-sequence task like translation). Now click on the Files tab and click on the Add file button to upload a new file to your repository. So that model files will directly be downloaded to that custom folder path. Choose whether your model is public or private. Aug 22, 2023 · I used PEFT LoRA + Trainer to fine-tune a model. 🤗 Accelerate integrates DeepSpeed via 2 options: Jan 2, 2022 · save_model itself does what it say on the can: saves the model, good, bad, best it does not matter. I first saved the already existing dataset using the following code: from datasets import load_dataset datasets = load_dataset("glue", "mrpc") datasets. You can load your model in 8-bit precision with few lines of code. save_model (optional_output_dir), which will behind the scenes call the save_pretrained of your model ( optional_output_dir is optional and will default to the output_dir you set). from sentence_transformers import SentenceTransformer. import torch from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer peft_model_id = "lucas0/empath-llama-7b" config = PeftConfig. Huggingface tokenizer provides an option of adding new tokens or redefining the special tokens such as [MASK], [CLS], etc. AutoTokenizer. When you download a dataset, the processing scripts and data are stored locally on your computer. By default you will be prompted to confirm that you want these files to be uploaded. Then drag-and-drop a file to upload and add a commit message. I am struggling a couple of weeks trying to find what I am doing wrong on saving and loading the fine tuned model. It’s the rotate checkpoints method that will keep the best model from being deleted. py. How to save the config. 🤗 PEFT (Parameter-Efficient Fine-Tuning) is a library for efficiently adapting large pretrained models to various downstream applications without fine-tuning all of a model’s parameters because it is prohibitively costly. What if the pre-trained model is saved by using torch. Full model fine-tuning of Stable Diffusion used to be slow and difficult, and that's part of the reason why lighter-weight methods such as Dreambooth or Textual Inversion have become so popular. dumps(config_dict, indent=2, sort_keys=True) + "". Specify the license usage for your model. Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources. Load a pretrained model. Unfortunately, there is no really better way to share code that using regular code sharing tools pip / github / npm / cargo etc. json file and three folders for train, test, and validation respectively. # this code is load DeepSpeed ZeRO-2 is primarily used only for training, as its features are of no use to inference. list_datasets. Pre-trained models can reduce your computing costs, and save you the time and resources required to train a model from scratch. Stage A & B are used to compress images, similar to what the job of the VAE is in Stable Diffusion. PathLike`): Path to the JSON file in which this configuration instance's parameters Jul 5, 2023 · How do I load a saved SFTTrainer model after uploading it to HuggingFace and how do I make a prediction with the model? The model was trained using Colab notebook to fine-tune Falcon-7B on Guanaco dataset using 4bit and PEFT It was trained on a custom dataset similar to the Guanaco dataset. Use multiple Workers CLIP is a multi-modal vision and language model. h5) to only one file in the format “. Module], weights: list[dict[str, torch. The cache allows 🤗 Datasets to avoid re-downloading or processing the entire dataset every time you use it. , . and optionally a dataset script, if it requires some code to read the data files. save(model. from peft import PeftModel, PeftConfig. load of entire models, is an extremely practical thing when you own the systems you are working on, but it's not great for broader scale sharing. But I don't know how to load the model with the checkpoint. save and torch. A path to a directory containing model weights saved using save_pretrained(), e. In plain English, those steps are: Create the model with randomly initialized weights. python. The goal is to load the model inside a Docker container later on without having to pull the model weights and configs from HuggingFace each time the container and Python server boots May 19, 2021 · To download models from 🤗Hugging Face, you can use the official CLI tool huggingface-cli or the Python method snapshot_download from the huggingface_hub library. Not Found. The DiffusionPipeline class is the simplest and most generic way to load the latest trending diffusion model from the Hub. This is what I’m currently doing, but it’s not working class Jul 25, 2022 · Accuracy dropped to below 0. CLIP uses a ViT like transformer to get visual features and a causal language model to get the text features. h5, I have the following Error: ValueError: No model config found in the file at <tensorflow. What code snippet can do that? Oct 27, 2020 · 3 Answers. json file inside it. import torch. int8() paper, or the blogpost about the collaboration. Here’s my code. Save this instance to a JSON file. If you make your model a subclass of PreTrainedModel, then you can use our methods save_pretrained and from_pretrained. save_safetensors (bool, optional, defaults to True Sep 9, 2021 · SageMaker will then download when starting the training job all of these files into your container. load_state(). Aug 16, 2021 · My office PC is not connected to internet, and I want to use the datasets package to load the dataset. Run inference with pipelines Write portable code with AutoClass Preprocess data Fine-tune a pretrained model Train with a script Set up distributed training with 🤗 Accelerate Load and train adapters with 🤗 PEFT Share your model Agents Generation with LLMs. Load a pretrained processor. bin file with Python’s pickle utility. You will need to create an account on huggingface. When I use it, I see a folder created with a bunch of json and bin files presumably for the tokenizer and the model. bin, training_args. The DiffusionPipeline. The Path of the files can either be accessed from the env var SM_CHANNEL_XXXX, e. Tensor]], input_dir: str) -> None. 3. platform. Unexpected token < in JSON at position 4. I moved them encased in a folder named 'distilbert_classification' somewhere in my google drive. 0 release of bitsandbytes. I Mar 21, 2022 · I had fine tuned a bert model in pytorch and saved its checkpoints via torch. You can even leverage Jun 11, 2021 · I’m fairly new to Python and HuggingFace and have what is probably a simple question about saving and loading a model. If you don't have it already, you can download and install Git from the official website. Args: json_file_path (`str` or `os. from_pretrained(base_model_name) model = PeftModel. Refresh. By using the same matrix, the model uses less parameters, and gradients flow much better to the embeddings (which is the start of the model, so Jun 3, 2023 · Hi, I am having problems trying to load a model after training it. Dec 8, 2022 · Model description I add simple custom pytorch-crf layer on top of TokenClassification model. While this works very well for regularly sized models, this workflow has some clear limitations when we deal with a huge model: in step 1 A string, the model id of a pretrained model hosted inside a model repo on huggingface. This really speeds up feedbacks loops when developing on the model. Once Git is installed, you need to set up Git LFS (Large File Storage) by running the following command in your terminal: Jun 23, 2020 · 2. The distilled model is faster and uses less memory while generating images of comparable quality to the full Stable Diffusion model. execution_device (torch. co. In this short guide, we’ll see how to: Share a timm model on the Hub; How to load that model back from the Hub; Authenticating. Or I just want to konw that trainer. 🤗 Transformers provides a different model head for each task as long as a model supports the task (i. Aug 11, 2021 · To save your model at the end of training, you should use trainer. Mlflow doesn’t support directly HuggingFace models, so we have to use the flavor pyfunc to save it. co/new: Lazy loading: in distributed (multi-node or multi-gpu) settings, it's nice to be able to load only part of the tensors on the various models. The hidden states are passed as inputs to a model head to produce the final output. content_copy. In this way, you can copy the downloaded model to any custom folder path. json. I also want save_pretrained and load_pretrained to work smoothly (i. My guess is that the fine tuned weights are not being loaded. hook(models: list[torch. load_model and are compatible with TensorFlow Serving. I’ve tried a number of ways Pytorch uses shared tensors for some computation. When save_total_limit=1 and load_best_model_at_end, it is possible that two checkpoints are saved: the last one and the best one (if they are different). Ctrl+K. device or Dict [str, torch. bin. This is not very efficient, is there another way to load the model ? Configuration. save_to_disk('glue-mrpc') A folder is created with dataset_dict. It can be used for image-text similarity and for zero-shot image classification. And then you can load your model in your training script with. If you do such modifications, then you may have to save the tokenizer to reuse it later. h5” or “TFLite”. From the numbers in the names of these directories, one could infer which checkpoint is which. Oct 17, 2021 · To upload your Sentence Transformers models to the Hugging Face Hub, log in with huggingface-cli login and use the save_to_hub method within the Sentence Transformers library. Natural Language Processing. Transformers provide APIs to download and train state-of-the-art pre-trained models easily. SegFormer works on any input size, as it pads the input to be divisible by config. In this way, you can directly change the default cache location of Huggingface. 1. Keras PyTorch July 16, 2023 March 5, 2023. base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto') tokenizer For example, for save_total_limit=5 and load_best_model_at_end, the four last checkpoints will always be retained alongside the best model. Jul 14, 2023 · I have a fine-tuned model. load(model_path)) However the problem is that every time i load a model with the Model() class it installs and reads into memory a model from huggingface’s transformers due to the code line 6 in the Model() class. The Hub is a central repository where all the Hugging Face datasets and models are stored. But the documentation does not specify a load method. To save the entire tokenizer, you should use save_pretrained () Thus, as follows: BASE_MODEL = "distilbert-base-multilingual-cased". Nearly every NLP task begins with a tokenizer. _models, weigths argument are the state dicts of the models, and the input_dir argument is the input_dir argument passed to Accelerator. First, I trained and saved the model using trainer = transformers. json file for this custom model ? When I load the custom trained model, the last CRF layer was not there? from torchcrf import CRF Oct 16, 2019 · If you look at the syntax, it is the directory of the pre-trained model that you are supposed to pass. OSError: We couldn't connect to 'https://huggingface. Aug 10, 2022 · If you tried to load a PyTorch model from a TF 2. state_dict(), 'model. from transformers import AutoModelForCausalLM, AutoTokenizer. Load a large model Cache management. Load a pretrained image processor; Load a pretrained feature extractor. You can find the list of datasets on the Hub or with huggingface_hub. Model Overview. Let's see how. /tf_model/model. For information on creating and managing models, datasets, and Spaces, refer to their respective documentation. transformers-cli upload path/to/awesome-name-you-picked/. Sorted by: 28. state_dict ()). from_pretrained(peft_model_id) model = AutoModelForCausalLM. Hi, I have a system saving an HF pipeline with the following code: from transformers import pipeline text_generator = pipeline ('') text_generator. from_pretrained(BASE_MODEL) The Model Hub is where the members of the Hugging Face community can host all of their model checkpoints for simple storage, discovery, and sharing. model = AutoModelForCausalLM. keyboard_arrow_up. safetensors is a secure alternative to pickle Aug 8, 2022 · from sentence_transformers import SentenceTransformer # initialize sentence transformer model # How to load 'bert-base-nli-mean-tokens' from local disk? model = SentenceTransformer('bert-base-nli-mean-tokens') # create sentence embeddings sentence_embeddings = model. e. zd xk oq tf jt ps mm lu pz da