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b586f643073f7948b3098cace31f8c69.txt_chunk_38	might stem from in-place modifications, initialize the base model just like you did earlier and construct the inference model. Copied from peft import PeftConfig, PeftModel config = PeftConfig.from_pretrained(repo_name) model = AutoModelForImageClassification.from_pretrained( config.base_model_name_or_path, ...	b586f643073f7948b3098cace31f8c69.txt
b586f643073f7948b3098cace31f8c69.txt_chunk_39	label2id=label2id, id2label=id2label, ignore_mismatched_sizes=True, # provide this in case you're planning to fine-tune an already fine-tuned checkpoint ) # Load the LoRA model inference_model = PeftModel.from_pretrained(model, repo_name) Let’s now fetch an example image for inference. Copied from PIL imp...	b586f643073f7948b3098cace31f8c69.txt
b586f643073f7948b3098cace31f8c69.txt_chunk_40	ort Image import requests url = "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/beignets.jpeg" image = Image.open(requests.get(url, stream=True).raw) image First, instantiate an image_processor from the underlying model repo. Copied image_processor = AutoImageProcessor.from_pretrained(repo_...	b586f643073f7948b3098cace31f8c69.txt
b586f643073f7948b3098cace31f8c69.txt_chunk_41	name) Then, prepare the example for inference. Copied encoding = image_processor(image.convert("RGB"), return_tensors="pt") Finally, run inference! Copied with torch.no_grad(): outputs = inference_model(**encoding) logits = outputs.logits predicted_class_idx = logits.argmax(-1).item() print("Predicted cl...	b586f643073f7948b3098cace31f8c69.txt
b586f643073f7948b3098cace31f8c69.txt_chunk_42	ass:", inference_model.config.id2label[predicted_class_idx]) "Predicted class: beignets"	b586f643073f7948b3098cace31f8c69.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_1	Fully Sharded Data Parallel Fully sharded data parallel (FSDP) is developed for distributed training of large pretrained models up to 1T parameters. FSDP achieves this by sharding the model parameters, gradients, and optimizer states across data parallel processes and it can also offload sharded model parameters to a...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_2	CPU. The memory efficiency afforded by FSDP allows you to scale training to larger batch or model sizes. Currently, FSDP does not confer any reduction in GPU memory usage and FSDP with CPU offload actually consumes 1.65x more GPU memory during training. You can track this PyTorch issue for any updates. FSDP is support...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_3	ed in 🤗 Accelerate, and you can use it with 🤗 PEFT. This guide will help you learn how to use our FSDP training script. You’ll configure the script to train a large model for conditional generation. Configuration Begin by running the following command to create a FSDP configuration file with 🤗 Accelerate. Use the...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_4	config_file flag to save the configuration file to a specific location, otherwise it is saved as a default_config.yaml file in the 🤗 Accelerate cache. The configuration file is used to set the default options when you launch the training script. Copied accelerate config --config_file fsdp_config.yaml You’ll be aske...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_5	a few questions about your setup, and configure the following arguments. For this example, make sure you fully shard the model parameters, gradients, optimizer states, leverage the CPU for offloading, and wrap model layers based on the Transformer layer class name. Copied `Sharding Strategy`: [1] FULL_SHARD (shards...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_6	optimizer states, gradients and parameters), [2] SHARD_GRAD_OP (shards optimizer states and gradients), [3] NO_SHARD `Offload Params`: Decides Whether to offload parameters and gradients to CPU `Auto Wrap Policy`: [1] TRANSFORMER_BASED_WRAP, [2] SIZE_BASED_WRAP, [3] NO_WRAP `Transformer Layer Class to Wrap`: When usi...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_7	ng `TRANSFORMER_BASED_WRAP`, user specifies comma-separated string of transformer layer class names (case-sensitive) to wrap ,e.g, `BertLayer`, `GPTJBlock`, `T5Block`, `BertLayer,BertEmbeddings,BertSelfOutput`... `Min Num Params`: minimum number of parameters when using `SIZE_BASED_WRAP` `Backward Prefetch`: [1] BACKW...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_8	ARD_PRE, [2] BACKWARD_POST, [3] NO_PREFETCH `State Dict Type`: [1] FULL_STATE_DICT, [2] LOCAL_STATE_DICT, [3] SHARDED_STATE_DICT For example, your FSDP configuration file may look like the following: Copied command_file: null commands: null compute_environment: LOCAL_MACHINE deepspeed_config: {} distributed_type: ...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_9	FSDP downcast_bf16: 'no' dynamo_backend: 'NO' fsdp_config: fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP fsdp_backward_prefetch_policy: BACKWARD_PRE fsdp_offload_params: true fsdp_sharding_strategy: 1 fsdp_state_dict_type: FULL_STATE_DICT fsdp_transformer_layer_cls_to_wrap: T5Block gpu_ids: null machine_ran...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_10	k: 0 main_process_ip: null main_process_port: null main_training_function: main megatron_lm_config: {} mixed_precision: 'no' num_machines: 1 num_processes: 2 rdzv_backend: static same_network: true tpu_name: null tpu_zone: null use_cpu: false The important parts Let’s dig a bit deeper into the training script to und...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_11	erstand how it works. The main() function begins with initializing an Accelerator class which handles everything for distributed training, such as automatically detecting your training environment. 💡 Feel free to change the model and dataset inside the main function. If your dataset format is different from the one in...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_12	the script, you may also need to write your own preprocessing function. The script also creates a configuration corresponding to the 🤗 PEFT method you’re using. For LoRA, you’ll use LoraConfig to specify the task type, and several other important parameters such as the dimension of the low-rank matrices, the matrices...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_13	scaling factor, and the dropout probability of the LoRA layers. If you want to use a different 🤗 PEFT method, replace LoraConfig with the appropriate class. Next, the script wraps the base model and peft_config with the get_peft_model() function to create a PeftModel. Copied def main(): + accelerator = Acceler...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_14	tor() model_name_or_path = "t5-base" base_path = "temp/data/FinancialPhraseBank-v1.0" + peft_config = LoraConfig( task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1 ) model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path) + model = get...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_15	_peft_model(model, peft_config) Throughout the script, you’ll see the main_process_first and wait_for_everyone functions which help control and synchronize when processes are executed. After your dataset is prepared, and all the necessary training components are loaded, the script checks if you’re using the fsdp_plugin...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_16	. PyTorch offers two ways for wrapping model layers in FSDP, automatically or manually. The simplest method is to allow FSDP to automatically recursively wrap model layers without changing any other code. You can choose to wrap the model layers based on the layer name or on the size (number of parameters). In the FSDP ...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_17	configuration file, it uses the TRANSFORMER_BASED_WRAP option to wrap the T5Block layer. Copied if getattr(accelerator.state, "fsdp_plugin", None) is not None: accelerator.state.fsdp_plugin.auto_wrap_policy = fsdp_auto_wrap_policy(model) Next, use 🤗 Accelerate’s prepare function to prepare the model, datasets, ...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_18	ptimizer, and scheduler for training. Copied model, train_dataloader, eval_dataloader, optimizer, lr_scheduler = accelerator.prepare( model, train_dataloader, eval_dataloader, optimizer, lr_scheduler ) From here, the remainder of the script handles the training loop, evaluation, and sharing your model to the Hub...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_19	. Train Run the following command to launch the training script. Earlier, you saved the configuration file to fsdp_config.yaml, so you’ll need to pass the path to the launcher with the --config_file argument like this: Copied accelerate launch --config_file fsdp_config.yaml examples/peft_lora_seq2seq_accelerate_f...	484c666f84643d3c63c5dbcc5c8cccea.txt
484c666f84643d3c63c5dbcc5c8cccea.txt_chunk_20	sdp.py Once training is complete, the script returns the accuracy and compares the predictions to the labels.	484c666f84643d3c63c5dbcc5c8cccea.txt
fbfea59dc785a5f35312147c99390e02.txt_chunk_1	LoRA This conceptual guide gives a brief overview of LoRA, a technique that accelerates the fine-tuning of large models while consuming less memory. To make fine-tuning more efficient, LoRA’s approach is to represent the weight updates with two smaller matrices (called update matrices) through low-rank decompositio...	fbfea59dc785a5f35312147c99390e02.txt
fbfea59dc785a5f35312147c99390e02.txt_chunk_2	n. These new matrices can be trained to adapt to the new data while keeping the overall number of changes low. The original weight matrix remains frozen and doesn’t receive any further adjustments. To produce the final results, both the original and the adapted weights are combined. This approach has a number of advant...	fbfea59dc785a5f35312147c99390e02.txt
fbfea59dc785a5f35312147c99390e02.txt_chunk_3	ages: LoRA makes fine-tuning more efficient by drastically reducing the number of trainable parameters. The original pre-trained weights are kept frozen, which means you can have multiple lightweight and portable LoRA models for various downstream tasks built on top of them. LoRA is orthogonal to many other parameter-...	fbfea59dc785a5f35312147c99390e02.txt
fbfea59dc785a5f35312147c99390e02.txt_chunk_4	efficient methods and can be combined with many of them. Performance of models fine-tuned using LoRA is comparable to the performance of fully fine-tuned models. LoRA does not add any inference latency because adapter weights can be merged with the base model. In principle, LoRA can be applied to any subset of weight m...	fbfea59dc785a5f35312147c99390e02.txt
fbfea59dc785a5f35312147c99390e02.txt_chunk_5	atrices in a neural network to reduce the number of trainable parameters. However, for simplicity and further parameter efficiency, in Transformer models LoRA is typically applied to attention blocks only. The resulting number of trainable parameters in a LoRA model depends on the size of the low-rank update matrices, ...	fbfea59dc785a5f35312147c99390e02.txt
fbfea59dc785a5f35312147c99390e02.txt_chunk_6	which is determined mainly by the rank r and the shape of the original weight matrix. Merge LoRA weights into the base model While LoRA is significantly smaller and faster to train, you may encounter latency issues during inference due to separately loading the base model and the LoRA model. To eliminate latency, us...	fbfea59dc785a5f35312147c99390e02.txt
fbfea59dc785a5f35312147c99390e02.txt_chunk_7	e the merge_and_unload() function to merge the adapter weights with the base model which allows you to effectively use the newly merged model as a standalone model. This works because during training, the smaller weight matrices (A and B in the diagram above) are separate. But once training is complete, the weights ca...	fbfea59dc785a5f35312147c99390e02.txt
fbfea59dc785a5f35312147c99390e02.txt_chunk_8	n actually be merged into a new weight matrix that is identical. Utils for LoRA Use merge_adapter() to merge the LoRa layers into the base model while retaining the PeftModel. This will help in later unmerging, deleting, loading different adapters and so on. Use unmerge_adapter() to unmerge the LoRa layers from the ...	fbfea59dc785a5f35312147c99390e02.txt
fbfea59dc785a5f35312147c99390e02.txt_chunk_9	base model while retaining the PeftModel. This will help in later merging, deleting, loading different adapters and so on. Use unload() to get back the base model without the merging of the active lora modules. This will help when you want to get back the pretrained base model in some applications when you want to rese...	fbfea59dc785a5f35312147c99390e02.txt
fbfea59dc785a5f35312147c99390e02.txt_chunk_10	t the model to its original state. For example, in Stable Diffusion WebUi, when the user wants to infer with base model post trying out LoRAs. Use delete_adapter() to delete an existing adapter. Use add_weighted_adapter() to combine multiple LoRAs into a new adapter based on the user provided weighing scheme. Common L...	fbfea59dc785a5f35312147c99390e02.txt
fbfea59dc785a5f35312147c99390e02.txt_chunk_11	oRA parameters in PEFT As with other methods supported by PEFT, to fine-tune a model using LoRA, you need to: Instantiate a base model. Create a configuration (LoraConfig) where you define LoRA-specific parameters. Wrap the base model with get_peft_model() to get a trainable PeftModel. Train the PeftModel as you norm...	fbfea59dc785a5f35312147c99390e02.txt
fbfea59dc785a5f35312147c99390e02.txt_chunk_12	ally would train the base model. LoraConfig allows you to control how LoRA is applied to the base model through the following parameters: r: the rank of the update matrices, expressed in int. Lower rank results in smaller update matrices with fewer trainable parameters. target_modules: The modules (for example, attent...	fbfea59dc785a5f35312147c99390e02.txt
fbfea59dc785a5f35312147c99390e02.txt_chunk_13	ion blocks) to apply the LoRA update matrices. alpha: LoRA scaling factor. bias: Specifies if the bias parameters should be trained. Can be 'none', 'all' or 'lora_only'. modules_to_save: List of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. These typically include model’s cust...	fbfea59dc785a5f35312147c99390e02.txt
fbfea59dc785a5f35312147c99390e02.txt_chunk_14	om head that is randomly initialized for the fine-tuning task. layers_to_transform: List of layers to be transformed by LoRA. If not specified, all layers in target_modules are transformed. layers_pattern: Pattern to match layer names in target_modules, if layers_to_transform is specified. By default PeftModel will loo...	fbfea59dc785a5f35312147c99390e02.txt
fbfea59dc785a5f35312147c99390e02.txt_chunk_15	k at common layer pattern (layers, h, blocks, etc.), use it for exotic and custom models. LoRA examples For an example of LoRA method application to various downstream tasks, please refer to the following guides: Image classification using LoRA Semantic segmentation While the original paper focuses on language model...	fbfea59dc785a5f35312147c99390e02.txt