Update README.md

README.md

@@ -1,3 +1,154 @@
 ---
 license: apache-2.0
+language:
+- en
 ---
+# CogAgent
+
+## Introduction
+
+**CogAgent** is an open-source visual language model improved based on **CogVLM**. 
+
+**CogAgent-18B** has 11 billion visual parameters and 7 billion language parameters and achieves state-of-the-art generalist performance on 9 classic cross-modal benchmarks, including:
+  + VQAv2 
+  + OK-VQ
+  + TextVQA 
+  + ST-VQA
+  + ChartQA 
+  + infoVQA 
+  + DocVQA 
+  + MM-Vet 
+  + POPE
+
+**CogAgent-18B** significantly surpasses existing models on GUI operation datasets such as AITW and Mind2Web.
+
+In addition to all the features already present in **CogVLM** (visual multi-round dialogue, visual grounding), **CogAgent**:
+
+1. Supports higher resolution visual input and dialogue question-answering. It supports ultra-high-resolution image inputs of **1120x1120**.
+
+2. Possesses the capabilities of a visual Agent, being able to return a plan, next action, and specific operations with coordinates for any given task on any GUI screenshot.
+
+3. Enhanced GUI-related question-answering capabilities, allowing it to handle questions about any GUI screenshot, such as web pages, PC apps, mobile applications, etc.
+
+4. Enhanced capabilities in OCR-related tasks through improved pre-training and fine-tuning.
+
+<div align="center">
+    <img src="https://raw.githubusercontent.com/THUDM/CogVLM/master/assets/cogagent_function.jpg" alt="img" style="zoom: 50%;" />
+</div>
+
+## Quick Start
+
+use this python code to get started quickly in `cli_demo.py`:
+
+```python
+import torch
+from PIL import Image
+from transformers import AutoModelForCausalLM, LlamaTokenizer
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--quant", choices=[4], type=int, default=None, help='quantization bits')
+parser.add_argument("--from_pretrained", type=str, default="THUDM/cogagent-chat-hf", help='pretrained ckpt')
+parser.add_argument("--local_tokenizer", type=str, default="lmsys/vicuna-7b-v1.5", help='tokenizer path')
+parser.add_argument("--fp16", action="store_true")
+parser.add_argument("--bf16", action="store_true")
+
+args = parser.parse_args()
+MODEL_PATH = args.from_pretrained
+TOKENIZER_PATH = args.local_tokenizer
+DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+tokenizer = LlamaTokenizer.from_pretrained(TOKENIZER_PATH)
+if args.bf16:
+    torch_type = torch.bfloat16
+else:
+    torch_type = torch.float16
+
+print("========Use torch type as:{} with device:{}========\n\n".format(torch_type, DEVICE))
+
+if args.quant:
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_PATH,
+        torch_dtype=torch_type,
+        low_cpu_mem_usage=True,
+        load_in_4bit=True,
+        trust_remote_code=True
+    ).eval()
+else:
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_PATH,
+        torch_dtype=torch_type,
+        low_cpu_mem_usage=True,
+        load_in_4bit=args.quant is not None,
+        trust_remote_code=True
+    ).to(DEVICE).eval()
+
+while True:
+    image_path = input("image path >>>>> ")
+    if image_path == "stop":
+        break
+
+    image = Image.open(image_path).convert('RGB')
+    history = []
+    while True:
+        query = input("Human:")
+        if query == "clear":
+            break
+        input_by_model = model.build_conversation_input_ids(tokenizer, query=query, history=history, images=[image])
+        inputs = {
+            'input_ids': input_by_model['input_ids'].unsqueeze(0).to(DEVICE),
+            'token_type_ids': input_by_model['token_type_ids'].unsqueeze(0).to(DEVICE),
+            'attention_mask': input_by_model['attention_mask'].unsqueeze(0).to(DEVICE),
+            'images': [[input_by_model['images'][0].to(DEVICE).to(torch_type)]],
+        }
+        if 'cross_images' in input_by_model and input_by_model['cross_images']:
+            inputs['cross_images'] = [[input_by_model['cross_images'][0].to(DEVICE).to(torch_type)]]
+
+        # add any transformers params here.
+        gen_kwargs = {"max_length": 2048,
+                      "temperature": 0.9,
+                      "do_sample": False}
+        with torch.no_grad():
+            outputs = model.generate(**inputs, **gen_kwargs)
+            outputs = outputs[:, inputs['input_ids'].shape[1]:]
+            response = tokenizer.decode(outputs[0])
+            response = response.split("</s>")[0]
+            print("\nCog:", response)
+        history.append((query, response))
+```
+
+Then run:
+
+```bash
+python cli_demo_hf.py --bf16
+```
+for more information such as Web Demo and Finetune, please refer to [Our GitHub](https://github.com/THUDM/CogVLM/)
+
+## License
+
+The code in this repository is open source under the [Apache-2.0 license](./LICENSE), while the use of CogAgent and CogVLM model weights must comply with the [Model License](./MODEL_LICENSE).
+
+## Citation & Acknowledgements
+
+If you find our work helpful, please consider citing the following papers
+
+```
+@misc{hong2023cogagent,
+      title={CogAgent: A Visual Language Model for GUI Agents}, 
+      author={Wenyi Hong and Weihan Wang and Qingsong Lv and Jiazheng Xu and Wenmeng Yu and Junhui Ji and Yan Wang and Zihan Wang and Yuxiao Dong and Ming Ding and Jie Tang},
+      year={2023},
+      eprint={2312.08914},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+
+@misc{wang2023cogvlm,
+      title={CogVLM: Visual Expert for Pretrained Language Models}, 
+      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},
+      year={2023},
+      eprint={2311.03079},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+In the instruction fine-tuning phase of the CogVLM, there are some English image-text data from the [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4), [LLAVA](https://github.com/haotian-liu/LLaVA), [LRV-Instruction](https://github.com/FuxiaoLiu/LRV-Instruction), [LLaVAR](https://github.com/SALT-NLP/LLaVAR) and [Shikra](https://github.com/shikras/shikra) projects, as well as many classic cross-modal work datasets. We sincerely thank them for their contributions.