CSG-VL is a family of small but strong multimodal models. It offers multiple plug-and-play vision encoders, like EVA-CLIP, SigLIP and language backbones, including Wukong-1B, Llama-3-8B, Phi-1.5, StableLM-2, Qwen1.5 and Phi-2.
- 2024.05.09 🔥 CSG-VL is released!
Here we show a code snippet to show you how to use CSG-VL-1B-v0.1 with HuggingFace transformers.
Before running the snippet, you need to install the following dependencies:
pip install torch transformers accelerate pillowimport torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import warnings
# disable some warnings
transformers.logging.set_verbosity_error()
transformers.logging.disable_progress_bar()
warnings.filterwarnings('ignore')
# set device
torch.set_default_device('cpu') # or 'cuda'
model_name = 'opencsg/csg-wukong-1B-VL-v0.1'
# create model
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map='auto',
trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(
model_name,
trust_remote_code=True)
# text prompt
prompt = 'What is the astronaut holding in his hand?'
text = f"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\n{prompt} ASSISTANT:"
text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0)
image = Image.open('example_1.png')
image_tensor = model.process_images([image], model.config).to(dtype=model.dtype)
# generate
output_ids = model.generate(
input_ids,
images=image_tensor,
max_new_tokens=100,
use_cache=True)[0]
print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip())-
CUDA and cuDNN
We use CUDA 11.8 and cuDNN 8.7.0. We actually use the CUDA docker by NVIDIA:
docker pull nvcr.io/nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04. CUDA 12 is fine, too. -
Create a conda virtual environment and activate it:
conda create -n csg-vl python=3.10 conda activate csg-vl
-
Basic requirements
pip install --upgrade pip # enable PEP 660 support pip install transformers pip install torch torchvision xformers --index-url https://download.pytorch.org/whl/cu118 -
Install apex
# https://github.com/NVIDIA/apex#from-source pip install ninja git clone https://github.com/NVIDIA/apex cd apex # if pip >= 23.1 (ref: https://pip.pypa.io/en/stable/news/#v23-1) which supports multiple `--config-settings` with the same key... pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./ # otherwise pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option="--cpp_ext" --global-option="--cuda_ext" ./
-
Install flash-attention
# https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#installation-and-features pip install packaging pip install flash-attn --no-build-isolation -
Install csg-vl and other requirements
git clone https://github.com/OpenCSGs/csg-vl.git cd csg-vl pip install -e .
-
Launching the Gradio Web Server
To interact with the models through a web interface, start the Gradio web server.
Basic start:
python -m csg_vl.serve.gradio_web_server \ --controller http://localhost:10000 \ --model-list-mode reload
If you want to share your web server with others, use
--shareoption. Note thatfrpc_linux_amd64_v0.2may be missing and you can fix it following instructions printed on the screen.python -m csg_vl.serve.gradio_web_server \ --controller http://localhost:10000 \ --model-list-mode reload \ --share
Now, you can open the web interface with the URL printed on the screen. You may notice that there is no model in the model list. Do not worry, as we have not launched any model worker yet. It will be automatically updated when you launch a model worker.
-
Starting the Controller
First, start the controller. This service orchestrates communication between the web server and model workers.
python -m csg_vl.serve.controller \ --host 0.0.0.0 \ --port 10000
-
Launching Model Workers
Model workers handle the processing of model inferences. Configure each worker with the appropriate model and start it.
-
For full-parameter tuning models
python -m csg_vl.serve.model_worker \ --host 0.0.0.0 \ --controller http://localhost:10000 \ --port 40000 \ --worker http://localhost:40000 \ --model-path /path/to/csg-vl/model \ --model-type wukong
-
For LoRA tuning models
You can use
script/merge_lora_weights.pyto merge the LoRA weights and base LLM, and use it as above.python script/merge_lora_weights.py \ --model-path /path/to/csg_vl_lora_weights \ --model-base /path/to/base_llm_model \ --model-type wukong \ --save-model-path /path/to/merged_model
Or you can use it without merging as below.
python -m csg_vl.serve.model_worker \ --host 0.0.0.0 \ --controller http://localhost:10000 \ --port 40000 \ --worker http://localhost:40000 \ --model-path /path/to/csg_vl_lora_weights \ --model-base /path/to/base_llm_model \ --model-type wukong
-
For CLI-based inference without using the Gradio interface, use the following command:
-
For full-parameter tuning models
python -m csg_vl.serve.cli \ --model-path /path/to/csg-vl/model \ --model-type wukong \ --image-file /path/to/the/test/image
-
For LoRA tuning models
You can use
script/merge_lora_weights.pyto merge the LoRA weights and base LLM, and use it as above.python script/merge_lora_weights.py \ --model-path /path/to/csg_vl_lora_weights \ --model-base /path/to/base_llm_model \ --model-type wukong \ --save-model-path /path/to/merged_model
Or you can use it without merging as below.
python -m csg_vl.serve.cli \ --model-path /path/to/csg_vl_lora_weights \ --model-base /path/to/base_llm_model \ --model-type wukong \ --image-file /path/to/the/test/image
This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses. The content of this project itself is licensed under the Apache license 2.0.
We acknowledge all the open-source contributors for the following projects to make this work possible