File size: 3,702 Bytes
1cf7acf 024f6c6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
---
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
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](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,
) |