Chimera: Improving Generalist Model with
Domain-Specific Experts

[ Paper ] [ Website ] [ Dataset🤗 ] [ Models🤗 ]

News 🔥

• 🔥 🔥 We are developing the Chimera-enhanced version to achieve superior multimodal reasoning by integrating stronger expert models, effective general models, and optimized fusion strategies
• Release the training code and data recipe
• Release the inference code and model checkpoints

🛠️ Installation

• Clone this repository:

  git clone https://github.com/UniModal4Reasoning/Chimera.git

• Create a conda virtual environment and activate it:

  conda create -n chimera python=3.9 -y
  conda activate chimera

• Install dependencies using requirements.txt:

  pip install -r requirements.txt

• Install other requirements:

  cd chimera/
  pip install --upgrade pip  # enable PEP 660 support
  pip install -e .

Additional Instructions

• Install flash-attn==2.3.4:

  pip install flash-attn==2.3.4 --no-build-isolation

  Alternatively you can compile from source:

  git clone https://github.com/Dao-AILab/flash-attention.git
  cd flash-attention
  git checkout v2.3.4
  python setup.py install

Quick Start

Multi-modal reasoning

from chimera.chimera_infer import Chimera4easyuse
import torch
from PIL import Image

# prepare model
# model_path = "U4R/Chimera-Reasoner-2B"
# model_path = "U4R/Chimera-Reasoner-4B"
model_path = "U4R/Chimera-Reasoner-8B"
generation_config = dict(max_new_tokens=256, do_sample=False)
model = Chimera4easyuse(model_path, dtype = torch.bfloat16, generation_config= generation_config)

# prepare input
image_path = "path/to/image"
user_prompt = "<image>\nuser prompt"
input_image = Image.open(image_path).convert('RGB')
response = model.get_response(user_prompt, [input_image])
print(response)

Visual content extraction

from chimera.chimera_infer import Chimera4easyuse
import torch
from PIL import Image

# prepare model
model_path = "U4R/Chimera-Extractor-1B"
generation_config = dict(max_new_tokens=4096, do_sample=False, no_repeat_ngram_size = 20)
model = Chimera4easyuse(model_path, dtype = torch.float16, generation_config= generation_config)

# prepare input
image_path = "path/to/document"
user_prompt = "<image>\nAs a smart PDF to Markdown conversion tool, please convert the content of the provided PDF into Markdown format."
input_image = Image.open(image_path).convert('RGB')
response = model.get_response(user_prompt, [input_image])
print(response)

License

Chimera is released under the Apache License 2.0

Citation

If you find our models / code / papers useful in your research, please consider giving ⭐ and citations 📝, thx :)

@misc{peng2024chimeraimprovinggeneralistmodel,
      title={Chimera: Improving Generalist Model with Domain-Specific Experts}, 
      author={Tianshuo Peng and Mingsheng Li and Hongbin Zhou and Renqiu Xia and Renrui Zhang and Lei Bai and Song Mao and Bin Wang and Conghui He and Aojun Zhou and Botian Shi and Tao Chen and Bo Zhang and Xiangyu Yue},
      year={2024},
      eprint={2412.05983},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.05983}, 
}

Contact Us

If you encounter any issues or have questions, please feel free to contact us via bo.zhangzx@gmail.com or zhangbo@pjlab.org.cn.