--- base_model: llava-hf/llava-1.5-7b-hf library_name: transformers model_name: llava-lora-12-06-rpo-0.1 tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for llava-lora-12-06-rpo-0.1 This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="abshetty/llava-lora-12-06-rpo-0.1", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [Visualize in Weights & Biases](https://wandb.ai/ashetty21-university-of-california-berkeley/huggingface/runs/ck7mmct6) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.2 - Transformers: 4.46.3 - Pytorch: 2.5.1+cu121 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ``` #Train the model training_args = DPOConfig( output_dir="llava-lora-12-06-rpo-0.1", bf16=True, gradient_checkpointing=True, per_device_train_batch_size=8, per_device_eval_batch_size=4, gradient_accumulation_steps=32, evaluation_strategy="steps", eval_steps=1, learning_rate=1e-5, beta=0.1, warmup_ratio=0.1, lr_scheduler_type="cosine", num_train_epochs=2, rpo_alpha=0.1, dataset_num_proc=32, # tokenization will use 32 processes dataloader_num_workers=32, # data loading will use 32 workers logging_steps=1, ) #Define LoRA configuration with specified rank lora_config = LoraConfig( r=64, # Set rank to 64 lora_alpha=128, # Set scaling factor to 128 target_modules="all-linear", # Target all linear layers lora_dropout=0.1, ) trainer = DPOTrainer( model, ref_model=None, # not needed when using peft args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, tokenizer=processor, peft_config=lora_config, )