Update README.md

- remove eagle arxiv link
- remove demo code related to github
- add license

README.md

@@ -1,3 +1,8 @@
+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+---
 # Valley 2.0
 ## Introduction
 Valley [github](https://github.com/bytedance/Valley) is a cutting-edge multimodal large model designed to handle a variety of tasks involving text, images, and video data, which is developed by ByteDance. Our model not only
@@ -7,11 +12,13 @@ Valley [github](https://github.com/bytedance/Valley) is a cutting-edge multimoda
 
 when evaluated against models of the same scale. 
 
+## Release
+- [12/23] 🔥 Announcing [Valley-Qwen2.5-7B](https://huggingface.co/ByteDance)!
 
 ## Valley-Eagle
 The foundational version of Valley is a multimodal large model aligned with Siglip and Qwen2.5, incorporating LargeMLP and ConvAdapter to construct the projector. 
 
-- In the final version, we also referenced [Eagle](https://arxiv.org/pdf/2408.15998), introducing an additional VisionEncoder that can flexibly adjust the number of tokens and is parallelized with the original visual tokens. 
+- In the final version, we also referenced Eagle, introducing an additional VisionEncoder that can flexibly adjust the number of tokens and is parallelized with the original visual tokens. 
 - This enhancement supplements the model’s performance in extreme scenarios, and we chose the Qwen2vl VisionEncoder for this purpose. 
 
 and the model structure is shown as follows:
@@ -21,85 +28,15 @@ and the model structure is shown as follows:
 </div>
 
 
-## Release
-- [12/23] 🔥 Announcing [Valley-Qwen2.5-7B](https://huggingface.co/ByteDance)!
-
 ## Environment Setup
 ``` bash
 pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu121
 pip install -r requirements.txt
 ```
 
-## Inference Demo
-- Single image
-``` python
-from valley_eagle_chat import ValleyEagleChat
-model = ValleyEagleChat(
-    model_path='path/to/ckpt',
-    padding_side = 'left',
-)
-
-url = 'http://p16-goveng-va.ibyteimg.com/tos-maliva-i-wtmo38ne4c-us/4870400481414052507~tplv-wtmo38ne4c-jpeg.jpeg'
-img = urllib.request.urlopen(url=url, timeout=5).read()
-
-request = {
-    "chat_history": [
-        {'role': 'system', 'content': 'You are Valley, developed by ByteDance. Your are a helpfull Assistant.'},
-        {'role': 'user', 'content': 'Describe the given image.'},
-    ],
-    "images": [img],
-}
-
-result = model(request)
-print(f"\n>>> Assistant:\n")
-print(result)
-```
-
-- Video
-``` python
-from valley_eagle_chat import ValleyEagleChat
-import decord
-import requests
-import numpy as np
-from torchvision import transforms
-
-model = ValleyEagleChat(
-    model_path='path/to/ckpt',
-    padding_side = 'left',
-)
-
-url = 'https://videos.pexels.com/video-files/29641276/12753127_1920_1080_25fps.mp4'
-video_file = './video.mp4'
-response = requests.get(url)
-if response.status_code == 200:
-    with open("video.mp4", "wb") as f:
-        f.write(response.content)
-else:
-    print("download error!")
-    exit(1)
-
-video_reader = decord.VideoReader(video_file)
-decord.bridge.set_bridge("torch")
-video = video_reader.get_batch(
-    np.linspace(0,  len(video_reader) - 1, 8).astype(np.int_)
-).byte()
-print([transforms.ToPILImage()(image.permute(2, 0, 1)).convert("RGB") for image in video])
-
-request = {
-    "chat_history": [
-        {'role': 'system', 'content': 'You are Valley, developed by ByteDance. Your are a helpfull Assistant.'},
-        {'role': 'user', 'content': 'Describe the given video.'},
-    ],
-    "images": [transforms.ToPILImage()(image.permute(2, 0, 1)).convert("RGB") for image in video],
-}
-result = model(request)
-print(f"\n>>> Assistant:\n")
-print(result)
-```
-
 ## License Agreement
 All of our open-source models are licensed under the Apache-2.0 license.
 
 
 ## Citation
-Coming Soon!
+Coming Soon!