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--- |
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license: apache-2.0 |
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library_name: transformers |
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pipeline_tag: visual-question-answering |
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--- |
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# CogVLM |
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**CogVLM** is a powerful **open-source visual language model** (**VLM**). CogVLM-17B has 10 billion vision parameters and 7 billion language parameters. CogVLM-17B achieves state-of-the-art performance on 10 classic cross-modal benchmarks, including NoCaps, Flicker30k captioning, RefCOCO, RefCOCO+, RefCOCOg, Visual7W, GQA, ScienceQA, VizWiz VQA and TDIUC, and rank the 2nd on VQAv2, OKVQA, TextVQA, COCO captioning, etc., **surpassing or matching PaLI-X 55B**. CogVLM can also [chat with you](http://36.103.203.44:7861/) about images. |
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<div align="center"> |
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<img src="https://github.com/THUDM/CogVLM/raw/main/assets/metrics-min.png" alt="img" style="zoom: 50%;" /> |
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</div> |
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# Dependencies |
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```base |
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pip install torch==2.1.0 transformers==4.35.0 accelerate==0.24.1 sentencepiece==0.1.99 einops==0.7.0 xformers==0.0.22.post7 triton==2.1.0 |
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``` |
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# Qiuckstart |
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```python |
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import torch |
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from PIL import Image |
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from transformers import AutoModelForCausalLM, LlamaTokenizer |
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model_path = 'Model/folder/path/here' |
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tokenizer = LlamaTokenizer.from_pretrained('lmsys/vicuna-7b-v1.5') |
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model = AutoModelForCausalLM.from_pretrained( |
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model_path, |
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torch_dtype=torch.bfloat16, |
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low_cpu_mem_usage=True, |
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trust_remote_code=True |
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).eval() |
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# chat example |
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query = 'Can you provide a description of the image and include the coordinates [[x0,y0,x1,y1]] for each mentioned object?' |
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image = Image.open("your/image/path/here").convert('RGB') |
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inputs = model.build_conversation_input_ids(tokenizer, query=query, history=[], images=[image]) # chat mode |
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inputs = { |
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'input_ids': inputs['input_ids'].unsqueeze(0).to('cuda'), |
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'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to('cuda'), |
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'attention_mask': inputs['attention_mask'].unsqueeze(0).to('cuda'), |
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'images': [[inputs['images'][0].to('cuda').to(torch.bfloat16)]], |
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} |
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gen_kwargs = {"max_length": 2048, "do_sample": False} |
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with torch.no_grad(): |
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outputs = model.generate(**inputs, **gen_kwargs) |
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outputs = outputs[:, inputs['input_ids'].shape[1]:] |
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print(tokenizer.decode(outputs[0])) |
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``` |
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# (License） |
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The code in this repository is open source under the [Apache-2.0 license](https://github.com/THUDM/CogVLM/raw/main/LICENSE), while the use of the CogVLM model weights must comply with the [Model License](https://github.com/THUDM/CogVLM/raw/main/MODEL_LICENSE). |
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# (Citation） |
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If you find our work helpful, please consider citing the following papers |
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``` |
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@article{wang2023cogvlm, |
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title={CogVLM: Visual Expert for Pretrained Language Models}, |
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author={Weihan Wang and Qingsong Lv and Wenmeng Yu and Wenyi Hong and Ji Qi and Yan Wang and Junhui Ji and Zhuoyi Yang and Lei Zhao and Xixuan Song and Jiazheng Xu and Bin Xu and Juanzi Li and Yuxiao Dong and Ming Ding and Jie Tang}, |
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year={2023}, |
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eprint={2311.03079}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
