Update README.md

README.md

@@ -6,29 +6,30 @@ pipeline_tag: visual-question-answering
 
 # CogVLM
 
-**CogVLM** 是一个强大的开源视觉语言模型（VLM）。CogVLM-17B 拥有 100 亿视觉参数和 70 亿语言参数，在 10 个经典跨模态基准测试上取得了 SOTA 性能，包括 NoCaps、Flicker30k captioning、RefCOCO、RefCOCO+、RefCOCOg、Visual7W、GQA、ScienceQA、VizWiz VQA 和 TDIUC，而在 VQAv2、OKVQA、TextVQA、COCO captioning 等方面则排名第二，超越或与 PaLI-X 55B 持平。您可以通过线上 [demo](http://36.103.203.44:7861/) 体验 CogVLM 多模态对话。
-
-**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.
-
-<div align="center">
-    <img src="https://github.com/THUDM/CogVLM/raw/main/assets/metrics-min.png" alt="img" style="zoom: 50%;" />
-</div>
-# 快速开始（Qiuckstart）
+# Qiuckstart
 
 ```python
 import torch
+import requests
 from PIL import Image
 from transformers import AutoModelForCausalLM, LlamaTokenizer
+
+model_path = 'Model/folder/path/here'
+
+
 tokenizer = LlamaTokenizer.from_pretrained('lmsys/vicuna-7b-v1.5')
 model = AutoModelForCausalLM.from_pretrained(
-    'THUDM/cogvlm-grounding-generalist-hf',
+    model_path,
     torch_dtype=torch.bfloat16,
     low_cpu_mem_usage=True,
     trust_remote_code=True
-).to('cuda').eval()
+).eval()
+
+
+# chat example
 query = 'Can you provide a description of the image and include the coordinates [[x0,y0,x1,y1]] for each mentioned object?'
-image = Image.open(requests.get('https://github.com/THUDM/CogVLM/blob/main/examples/4.jpg?raw=true', stream=True).raw).convert('RGB')
-inputs = model.build_conversation_input_ids(tokenizer, query=query, images=[image])
+image = Image.open("your/image/path/here").convert('RGB')
+inputs = model.build_conversation_input_ids(tokenizer, query=query, history=[], images=[image])  # chat mode
 inputs = {
     'input_ids': inputs['input_ids'].unsqueeze(0).to('cuda'),
     'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to('cuda'),
@@ -36,21 +37,14 @@ inputs = {
     'images': [[inputs['images'][0].to('cuda').to(torch.bfloat16)]],
 }
 gen_kwargs = {"max_length": 2048, "do_sample": False}
+
 with torch.no_grad():
     outputs = model.generate(**inputs, **gen_kwargs)
     outputs = outputs[:, inputs['input_ids'].shape[1]:]
     print(tokenizer.decode(outputs[0]))
+    
 ```
 
-# 方法（Method）
-
-CogVLM 模型包括四个基本组件：视觉变换器（ViT）编码器、MLP适配器、预训练的大型语言模型（GPT）和一个**视觉专家模块**。更多细节请参见[Paper](https://github.com/THUDM/CogVLM/blob/main/assets/cogvlm-paper.pdf)。
-
-CogVLM model comprises four fundamental components: a vision transformer (ViT) encoder, an MLP adapter, a pretrained large language model (GPT), and a **visual expert module**. See [Paper](https://github.com/THUDM/CogVLM/blob/main/assets/cogvlm-paper.pdf) for more details.
-
-<div align="center">
-    <img src="https://github.com/THUDM/CogVLM/raw/main/assets/method-min.png" style="zoom:50%;" />
-</div>
 # 许可（License）
 
 此存储库中的代码是根据 [Apache-2.0 许可](https://github.com/THUDM/CogVLM/raw/main/LICENSE) 开放源码，而使用 CogVLM 模型权重必须遵循 [模型许可](https://github.com/THUDM/CogVLM/raw/main/MODEL_LICENSE)。