diff --git a/examples/dora_finetuning/QDoRA_finetuning.ipynb b/examples/dora_finetuning/QDoRA_finetuning.ipynb new file mode 100644 index 0000000000..17674a9e0c --- /dev/null +++ b/examples/dora_finetuning/QDoRA_finetuning.ipynb @@ -0,0 +1,8544 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "CV_gQs58bsvM" + }, + "source": [ + "# Fine-tuning [Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) Dataset using QDora (quantized Lora w/ use_dora=True) on T4 Free Colab GPU." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FuXIFTFapAMI", + "outputId": "b95d8260-65bd-405f-f1e2-8d353aa46814" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m37.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m309.4/309.4 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.8/40.8 MB\u001b[0m \u001b[31m28.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m27.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m20.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n", + "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n", + "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "# Install the libraries\n", + "!pip install -q -U bitsandbytes\n", + "!pip install -q -U git+https://github.com/huggingface/transformers.git\n", + "!pip install -q -U git+https://github.com/huggingface/peft.git\n", + "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n", + "!pip install -q datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "8cc86330c2af436c9af314e8c04c8c2b", + "e25f9ca445b14e3f8397779df071dfb4", + "8365680c634a44aa880317e36fa5e46e", + "0c4ac7c3db0b431397cc812f7c9e785c", + "c84f542c863043dea8a3675fa153e78d", + "b3b3f4ddd4ed4d938c923887939a0440", + "35186465f87341f683affb9399661540", + "791df472db174df69b8c9f0e200af254", + "6bb9c7182d2a464ea21809e59043562a", + "31c574113731403b88edc5bb0798bc6d", + "3b8bc5b9392e45758813a1db9db824a9", + "90661b333d6f496ca606b3046622660e", + "5f551f9b217e44cf8b5433f314b3844b", + "d2d81cc8296c4b10bf80b86c0a3302d3", + "7e3a386e672f4748882211227b7721a9", + "57f251691b4c453896b2508c431dfc2f", + "4bdb196cd1494f809829651ec5b6cbf8", + "7cd50bcc8fcc4b83abcda6d3604bd4cc", + "7a00aa4a97a34da39cc052c6926dbe13", + "14c73d88df9e46e3bbb6690fdb48ad07", + "0e2beab611114239b6ee48a3cbb09c49", + "006b78b5191b4fb888d98bdf6c20ec1e", + "5f6ffa1d929443a5bd9c7c550f0690f0", + "668a7f88506148a9ba2b48920afc028f", + "57b0096985ab44aea342e52795c4f999", + "a4c404e420cc4ce781ce569f9ab3f987", + "ee4e4af964ec4dd597cb04a90f0697f9", + "974e3687f18a4e1a975969b880d086aa", + "93a50117ece543d4857ba02505dc4514", + "71a3a56edbdb45669d382fef4b097e1b", + "53f287d4927541d08e2ae7d4d0b3c396", + "afa442ab223b46cb82569438c0047823" + ] + }, + "id": "wAAPv5CRmg7e", + "outputId": "687f979a-04c1-4160-d71c-4de8ecdb07d9" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8cc86330c2af436c9af314e8c04c8c2b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value='
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StepTraining Loss
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "TrainOutput(global_step=10, training_loss=1.662518608570099, metrics={'train_runtime': 269.3407, 'train_samples_per_second': 0.149, 'train_steps_per_second': 0.037, 'total_flos': 530537216679936.0, 'train_loss': 1.662518608570099, 'epoch': 0.004062563477554337})" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import transformers\n", + "\n", + "tokenizer.pad_token = tokenizer.eos_token\n", + "\n", + "trainer = transformers.Trainer(\n", + " model=model,\n", + " train_dataset=data[\"train\"],\n", + " args=transformers.TrainingArguments(\n", + " per_device_train_batch_size=1,\n", + " gradient_accumulation_steps=4,\n", + " warmup_steps=2,\n", + " max_steps=10,\n", + " learning_rate=2e-4,\n", + " fp16=True,\n", + " logging_steps=1,\n", + " output_dir=\"path/to/your/HF/repo\", # change it to your desired repo!\n", + " optim=\"paged_adamw_8bit\",\n", + " ),\n", + " data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n", + ")\n", + "model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n", + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Mr3rLrHwqhf6" + }, + "source": [ + "## Usage Example" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true, + "id": "9mrOJ9l8SMHv" + }, + "outputs": [], + "source": [ + "model.config.use_cache = True\n", + "model.eval();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 122 + }, + "id": "AM6FNOFzqKfI", + "outputId": "fdbe28b1-e440-45d3-bd6d-c15e744ad23d" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" + ] + }, + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "\"A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. ### Human: What is the purpose of quantization in LLMs?### Assistant: Quantization is a technique used to reduce the size of a model without significantly impacting its performance. In the context of language models, quantization is the process of converting floating-point numbers (which are used to represent the weights and activations of a model) to smaller, fixed-point numbers. This can be done by grouping the weights into small chunks and assigning each chunk a single, fixed-point number. Quantization can significantly reduce the size of a model, making it more efficient to train and deploy. In addition, quantization can improve the performance of a model on low-power devices, such as mobile phones\"" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from transformers import GenerationConfig\n", + "\n", + "max_new_tokens = 120\n", + "top_p = 0.9\n", + "temperature = 0.7\n", + "user_question = \"What is the purpose of quantization in LLMs?\"\n", + "\n", + "\n", + "prompt = (\n", + " \"A chat between a curious human and an artificial intelligence assistant. \"\n", + " \"The assistant gives helpful, detailed, and polite answers to the user's questions. \"\n", + " \"### Human: {user_question}\"\n", + " \"### Assistant: \"\n", + ")\n", + "\n", + "\n", + "def generate(model, user_question, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature):\n", + " inputs = tokenizer(prompt.format(user_question=user_question), return_tensors=\"pt\").to(\"cuda\")\n", + "\n", + " outputs = model.generate(\n", + " **inputs,\n", + " generation_config=GenerationConfig(\n", + " do_sample=True,\n", + " max_new_tokens=max_new_tokens,\n", + " top_p=top_p,\n", + " temperature=temperature,\n", + " ),\n", + " )\n", + "\n", + " text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n", + " # print(text)\n", + " return text\n", + "\n", + "\n", + "generate(model, user_question)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "T5t_gl2_f5OO" + }, + "outputs": [], + "source": [ + "# trainer.push_to_hub()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.1.-1" + }, + 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mode 100644 index 0000000000..af666a1321 --- /dev/null +++ b/examples/dora_finetuning/README.md @@ -0,0 +1,106 @@ +# DoRA: Weight-Decomposed Low-Rank Adaptation + +![dora](https://i.ytimg.com/vi/m7KQdGSr0Dg/maxresdefault.jpg) + + +## Introduction +[DoRA](https://arxiv.org/abs/2402.09353) is a novel approach that leverages low rank adaptation through weight decomposition analysis to investigate the inherent differences between full fine-tuning and LoRA. DoRA initially decomposes the pretrained weight into its magnitude and directional components and finetunes both of them. Because the directional component is large in terms of parameter numbers, we further decompose it with LoRA for efficient finetuning. This results in enhancing both the learning capacity and training stability of LoRA while avoiding any additional inference overhead. + +## Quick start +```python +import torch +from peft import LoraConfig, get_peft_model +from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer +from datasets import load_dataset + +model = AutoModelForCausalLM.from_pretrained("huggyllama/llama-7b", device_map="cuda") +tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b") +dataset = load_dataset("timdettmers/openassistant-guanaco", split="train") +lora_config = LoraConfig( + use_dora=True +) +peft_model = get_peft_model(model, lora_config) +trainer = transformers.Trainer( + model=peft_model, + train_dataset=dataset, + dataset_text_field="text", + max_seq_length=2048, + tokenizer=tokenizer, +) +trainer.train() +peft_model.save_pretrained("dora-llama-3-8b") +``` + +There is no additional change needed to your standard LoRA procedure, except for specifying `use_dora = True` option in your lora configuration. + + +Run the finetuning script simply by running: +```bash +python examples/dora_finetuning/dora_finetuning.py --base_model meta-llama/Meta-Llama-3-8B --data_path timdettmers/openassistant-guanaco +``` +This 👆🏻 by default will load the model in peft set up with LoRA config. Now if you wanna quickly compare it with Dora, all you need to do is to input ` --use_dora` in the command line. So same above example would be 👇🏻; + +```bash +python examples/dora_finetuning/dora_finetuning.py --base_model meta-llama/Meta-Llama-3-8B --data_path timdettmers/openassistant-guanaco --use_dora +``` + +DoRA also supports quantization. To use 4-bit quantization try: + +```bash +python examples/dora_finetuning/dora_finetuning.py --base_model meta-llama/Meta-Llama-3-8B --quantize +``` + +Similarly, by default the LoRA layers are the attention and MLP layers of LLama model, if you get to choose a different set of layers for LoRA to be applied on, you can simply define it using: +```bash +python examples/dora_finetuning/dora_finetuning.py --lora_target_modules "q_proj,k_proj,v_proj,o_proj" +``` + +### Full example of the script +```bash +python dora_finetuning.py \ + --base_model "PATH_TO_MODEL" \ + --data_path "PATH_TO_DATASET" \ + --output_dir "PATH_TO_OUTPUT_DIR" \ + --batch_size 1 \ + --num_epochs 3 \ + --learning_rate 3e-4 \ + --cutoff_len 512 \ + --val_set_size 500 \ + --use_dora \ + --quantize \ + --eval_step 10 \ + --save_step 100 \ + --device "cuda:0" \ + --lora_r 16 \ + --lora_alpha 32 \ + --lora_dropout 0.05 \ + --lora_target_modules "q_proj,k_proj,v_proj,o_proj" \ + --hub_model_id "YOUR_HF_REPO" \ + --push_to_hub +``` +## Use the model on 🤗 +You can load and use the model as any other 🤗 models. +```python +from transformers import AutoModel +model = AutoModel.from_pretrained("ShirinYamani/huggyllama-llama-7b-finetuned") +``` + +## DoRA vs. LoRA +In general, DoRA finetuning on diffusion models is still experimental and is likely to require different hyperparameter values to perform best compared to LoRA. + +Specifically, people have noticed 2 differences to take into account in your training: + +1. LoRA seem to converge faster than DoRA (so a set of parameters that may lead to overfitting when training a LoRA may be working well for a DoRA) + +2. DoRA quality superior to LoRA especially in lower ranks: The difference in quality of DoRA of rank 8 and LoRA of rank 8 appears to be more significant than when training ranks of 32 or 64 for example. + + +## Citation +``` +@article{liu2024dora, + title={DoRA: Weight-Decomposed Low-Rank Adaptation}, + author={Liu, Shih-Yang and Wang, Chien-Yi and Yin, Hongxu and Molchanov, Pavlo and Wang, Yu-Chiang Frank and Cheng, Kwang-Ting and Chen, Min-Hung}, + journal={arXiv preprint arXiv:2402.09353}, + year={2024} +} +``` \ No newline at end of file diff --git a/examples/dora_finetuning/dora_finetuning.py b/examples/dora_finetuning/dora_finetuning.py new file mode 100644 index 0000000000..d7ecd54fc4 --- /dev/null +++ b/examples/dora_finetuning/dora_finetuning.py @@ -0,0 +1,200 @@ +import os + +import torch +from datasets import load_dataset +from transformers import ( + AutoModelForCausalLM, + AutoTokenizer, + BitsAndBytesConfig, + DataCollatorWithPadding, + Trainer, + TrainingArguments, +) + +from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training + + +def train_model( + base_model: str, + data_path: str, + output_dir: str, + batch_size: int, + num_epochs: int, + learning_rate: float, + cutoff_len: int, + val_set_size: int, + use_dora: bool, + quantize: bool, + eval_step: int, + save_step: int, + device: str, + lora_r: int, + lora_alpha: int, + lora_dropout: float, + lora_target_modules: str, + hub_model_id: str, + push_to_hub: bool, +): + os.environ["TOKENIZERS_PARALLELISM"] = "false" + hf_token = os.getenv("HF_TOKEN") + + # Setup device + device = torch.device(device) + print(f"Using device: {device}") + + # load tokenizer + tokenizer = AutoTokenizer.from_pretrained(base_model, token=hf_token) + + # QDoRA (quantized dora): IF YOU WANNA QUANTIZE THE MODEL + if quantize: + model = AutoModelForCausalLM.from_pretrained( + base_model, + token=hf_token, + quantization_config=BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_compute_dtype=( + torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16 + ), + bnb_4bit_use_double_quant=True, + bnb_4bit_quant_type="nf4", + ), + ) + # setup for quantized training + model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=True) + else: + model = AutoModelForCausalLM.from_pretrained(base_model, token=hf_token) + # LoRa config for the PEFT model + lora_config = LoraConfig( + use_dora=use_dora, # to use Dora OR compare to Lora just set the --use_dora + r=lora_r, # Rank of matrix + lora_alpha=lora_alpha, + target_modules=( + lora_target_modules.split(",") + if lora_target_modules + else ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"] + ), + lora_dropout=lora_dropout, + bias="none", + ) + + # get the peft model with LoRa config + model = get_peft_model(model, lora_config) + + model.to(device) # MODEL TO GPU/CUDA + tokenizer.pad_token = tokenizer.eos_token + + # Load the dataset + dataset = load_dataset(data_path) + + def tokenize_function(examples): + inputs = tokenizer(examples["text"], padding="max_length", truncation=True, max_length=cutoff_len) + inputs["labels"] = inputs["input_ids"].copy() # setting labels for a language modeling task + return inputs + + # Tokenize the dataset and prepare for training + tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset["train"].column_names) + + # Data collator to dynamically pad the batched examples + data_collator = DataCollatorWithPadding(tokenizer) + + # Define training arguments + training_args = TrainingArguments( + output_dir=output_dir, + num_train_epochs=num_epochs, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + warmup_steps=100, + weight_decay=0.01, + logging_dir="./logs", + logging_steps=eval_step, + save_steps=save_step, + save_total_limit=2, + push_to_hub=push_to_hub, + hub_model_id=hub_model_id, + gradient_accumulation_steps=16, + fp16=True, + learning_rate=learning_rate, + hub_token=hf_token, + ) + + # Clear CUDA cache to free memory + torch.cuda.empty_cache() + + # Initialize the Trainer + trainer = Trainer( + model=model, + args=training_args, + train_dataset=tokenized_datasets["train"], + eval_dataset=tokenized_datasets["test"], + data_collator=data_collator, + ) + + # Start model training + trainer.train() + + # Save and push the trained model and tokenizer + if push_to_hub: + # Push the main model to the hub + trainer.push_to_hub(commit_message="Fine-tuned model") + + # Save the model and tokenizer locally + model.save_pretrained(output_dir) + tokenizer.save_pretrained(output_dir) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description="Fine-tune LLaMA with DoRA and PEFT") + parser.add_argument("--base_model", type=str, default="huggyllama/llama-7b", help="Base model path or name") + parser.add_argument( + "--data_path", type=str, default="timdettmers/openassistant-guanaco", help="Dataset path or name" + ) + parser.add_argument( + "--output_dir", type=str, default="path/to/output", help="Output directory for the fine-tuned model" + ) + parser.add_argument("--batch_size", type=int, default=1, help="Batch size") + parser.add_argument("--num_epochs", type=int, default=1, help="Number of training epochs") + parser.add_argument("--learning_rate", type=float, default=3e-4, help="Learning rate") + parser.add_argument("--cutoff_len", type=int, default=512, help="Cutoff length for tokenization") + parser.add_argument("--val_set_size", type=int, default=500, help="Validation set size") + parser.add_argument("--use_dora", action="store_true", help="Apply Dora") + parser.add_argument("--quantize", action="store_true", help="Use quantization") + parser.add_argument("--eval_step", type=int, default=10, help="Evaluation step interval") + parser.add_argument("--save_step", type=int, default=100, help="Save step interval") + parser.add_argument("--device", type=str, default="cuda:0", help="Device to use for training") + parser.add_argument("--lora_r", type=int, default=8, help="LoRA rank") + parser.add_argument("--lora_alpha", type=int, default=16, help="LoRA alpha") + parser.add_argument("--lora_dropout", type=float, default=0.05, help="LoRA dropout rate") + parser.add_argument( + "--lora_target_modules", type=str, default=None, help="Comma-separated list of target modules for LoRA" + ) + parser.add_argument( + "--hub_model_id", + type=str, + default="path/to/repo", + help="Repository name to push the model on the Hugging Face Hub", + ) + parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to Hugging Face Hub") + args = parser.parse_args() + train_model( + base_model=args.base_model, + data_path=args.data_path, + output_dir=args.output_dir, + batch_size=args.batch_size, + num_epochs=args.num_epochs, + learning_rate=args.learning_rate, + cutoff_len=args.cutoff_len, + val_set_size=args.val_set_size, + use_dora=args.use_dora, + quantize=args.quantize, + eval_step=args.eval_step, + save_step=args.save_step, + device=args.device, + lora_r=args.lora_r, + lora_alpha=args.lora_alpha, + lora_dropout=args.lora_dropout, + lora_target_modules=args.lora_target_modules, + hub_model_id=args.hub_model_id, + push_to_hub=args.push_to_hub, + )