README.md

---
license: gpl-3.0
language:
- zh
- en
pipeline_tag: visual-question-answering
tags:
- ziya
- fengshenbang
- LVLM
- visual question answering
---

# Ziya-Visual-14B-Chat

- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)

# 姜子牙系列模型

- [Ziya-LLaMA-13B-v1.1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1.1)
- [Ziya-LLaMA-13B-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1)
- [Ziya-LLaMA-7B-Reward](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-7B-Reward)
- [Ziya-LLaMA-13B-Pretrain-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-Pretrain-v1)


## 软件依赖
```
pip install torch==1.12.1 tokenizers==0.13.3 git+https://github.com/huggingface/transformers
```

## 模型分类 Model Taxonomy

|  需求 Demand  | 任务 Task       | 系列 Series      | 模型 Model    | 参数 Parameter | 额外 Extra |
|  :----:  | :----:  | :----:  | :----:  | :----:  | :----:  |
| 多模态 Multi-Modal | 通用 General | 姜子牙-多模态 Ziya-Visual | InstructBLIP LLaMA |     14B    |     English&Chinese     |


## 使用 Usage


```python
import gradio as gr
from PIL import Image
import torch
import random
from fengshen.models.instruct_ditto.modeling_instruct_ditto import InstructDittoLMForConditionalGeneration, DittoQFromerForPretrain, DittoLMForConditionalGeneration
from torchvision.transforms import Compose, ToTensor, Resize, Normalize
from transformers import LlamaTokenizer, BertTokenizer, GenerationConfig
from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, RandomHorizontalFlip

OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
_MODEL_PATH = "your model path"

transforms = Compose([
    RandomResizedCrop(
        224,
        scale=(0.5, 1.0),
        interpolation=InterpolationMode.BICUBIC,
    ),
    RandomHorizontalFlip(),
    ToTensor(),
    Normalize(mean=OPENAI_DATASET_MEAN, std=OPENAI_DATASET_STD),
])

model = InstructDittoLMForConditionalGeneration.from_pretrained(_MODEL_PATH).to(device).eval()
instruct_tokenizer = BertTokenizer.from_pretrained(os.path.join(_MODEL_PATH, "qformer_tokenizer"))
tokenizer = LlamaTokenizer.from_pretrained(_MODEL_PATH, use_fast = False)

qformer_prompt = "{prompt}"
qformer_prompt_list = []
prompt_prefix = ''
llm_prompt = "<human>: {prompt}\n<bot>:"
llm_prompt_list = []

prompt = ["your prompt"]

for i in prompt:
    qformer_prompt_list.append(qformer_prompt.format_map({"prompt":i}))
    llm_prompt_list.append(llm_prompt.format_map({"prompt":i}))

image_url = ["your image"]

imgs = []
for img_url in image_url:
    imgs.append(transforms(Image.open(img_url).convert('RGB')))

config = GenerationConfig(
    # do_sample=True, #False
    # num_beams=3, # 3
    # min_length=4,
    max_new_tokens=128, 
    repetition_penalty=1.18, 
    # length_penalty=1, 
    temperature=0.7,
    top_p=0.1,
    bos_token_id=1,
    eos_token_id=2, 
    pad_token_id=39410, 
)

imgs = torch.stack(imgs)

instruct_tokenizer.padding_side = 'right'
tokenizer.padding_side = 'left'

for i in range(imgs.shape[0]):
    prompt_prefix_ids = tokenizer(prompt_prefix, return_tensors="pt").input_ids
    qformer_instruct_ids = instruct_tokenizer(qformer_prompt_list[i], return_tensors="pt").input_ids
    llm_instruct_ids = tokenizer(llm_prompt_list[i], return_tensors="pt", add_special_tokens=False).input_ids
    qformer_instruct_atts = instruct_tokenizer(qformer_prompt_list[i], return_tensors="pt").attention_mask
    llm_instruct_atts = tokenizer(llm_prompt_list[i], return_tensors="pt", add_special_tokens=False).attention_mask
    captions = model.generate(
                            imgs[i].unsqueeze(0).to('cuda'),
                            qformer_instruct_ids=qformer_instruct_ids.to('cuda'),
                            prompt_prefix_ids = prompt_prefix_ids.to('cuda'),
                            llm_instruct_ids=llm_instruct_ids.to('cuda'),
                            generation_config=config
                            )
    caption = tokenizer.decode(captions[0])
    print("问: " + prompt[i] + "\n" + "答: " + caption)


```



## 引用 Citation

如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2210.08590)，[论文](https://arxiv.org/abs/2310.08166)：

If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2210.08590), [paper](https://arxiv.org/abs/2310.08166):

```text
@article{fengshenbang,
  author    = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
  title     = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
  journal   = {CoRR},
  volume    = {abs/2209.02970},
  year      = {2022}
}
```

```text
@article{lu2023ziya,
  title={Ziya-VL: Bilingual Large Vision-Language Model via Multi-Task Instruction Tuning},
  author={Lu, Junyu and Zhang, Dixiang and Wu, Xiaojun and Gao, Xinyu and Gan, Ruyi and Zhang, Jiaxing and Song, Yan and Zhang, Pingjian},
  journal={arXiv preprint arXiv:2310.08166},
  year={2023}
}
```

You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):

欢迎引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
  title={Fengshenbang-LM},
  author={IDEA-CCNL},
  year={2021},
  howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```