Model Card for llava-lora-12-05-lr

This model is a fine-tuned version of llava-hf/llava-1.5-7b-hf. It has been trained using TRL.

Quick start

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-12-05-lr", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with DPO, a method introduced in Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

Framework versions

• TRL: 0.12.1
• Transformers: 4.46.3
• Pytorch: 2.5.1+cu121
• Datasets: 3.1.0
• Tokenizers: 0.20.3

Citations

Cite DPO as:

@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:

@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}}
}

configuration:

#Train the model training_args = DPOConfig( output_dir="llava-lora-12-05-lr", 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=5e-5, beta=0.1, warmup_ratio=0.1, lr_scheduler_type="cosine", num_train_epochs=3, 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 )

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, )