Add link to paper and Github repo

README.md

@@ -1,15 +1,14 @@
-
 ---
-license_name: qwen-research
-license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct-AWQ/blob/main/LICENSE
+base_model:
+- Qwen/Qwen2.5-VL-3B-Instruct
 language:
 - en
+library_name: transformers
+license_name: qwen-research
+license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct-AWQ/blob/main/LICENSE
 pipeline_tag: image-text-to-text
 tags:
 - multimodal
-library_name: transformers
-base_model:
-- Qwen/Qwen2.5-VL-3B-Instruct
 ---
 
 # Qwen2.5-VL-3B-Instruct-AWQ
@@ -51,6 +50,11 @@ We enhance both training and inference speeds by strategically implementing wind
 
 We have three models with 3, 7 and 72 billion parameters. This repo contains the instruction-tuned 3B Qwen2.5-VL model with AWQ. For more information, visit our [Blog](https://qwenlm.github.io/blog/qwen2.5-vl/) and [GitHub](https://github.com/QwenLM/Qwen2.5-VL).
 
+See also:
+
+- [Project website](https://chat.qwenlm.ai/)
+- [Technical report](https://huggingface.co/papers/2502.13923)
+- [Github](https://github.com/QwenLM/Qwen2.5-VL)
 
 
 ## Evaluation
@@ -86,7 +90,7 @@ KeyError: 'qwen2_5_vl'
 We offer a toolkit to help you handle various types of visual input more conveniently, as if you were using an API. This includes base64, URLs, and interleaved images and videos. You can install it using the following command:
 
 ```bash
-# It's highly recommanded to use `[decord]` feature for faster video loading.
+# It's highly recommended to use `[decord]` feature for faster video loading.
 pip install qwen-vl-utils[decord]==0.0.8
 ```
 
@@ -97,7 +101,7 @@ If you are not using Linux, you might not be able to install `decord` from PyPI.
 Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`:
 
 ```python
-from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
 from qwen_vl_utils import process_vision_info
 
 # default: Load the model on the available device(s)
@@ -113,7 +117,7 @@ model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
 #     device_map="auto",
 # )
 
-# default processer
+# default processor
 processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct-AWQ")
 
 # The default range for the number of visual tokens per image in the model is 4-16384.
@@ -163,7 +167,6 @@ print(output_text)
 ### 🤖 ModelScope
 We strongly advise users especially those in mainland China to use ModelScope. `snapshot_download` can help you solve issues concerning downloading checkpoints.
 
-
 ### More Usage Tips
 
 For input images, we support local files, base64, and URLs. For videos, we currently only support local files.
@@ -250,80 +253,4 @@ messages = [
         ],
     }
 ]
-```
-
-### Processing Long Texts
-
-The current `config.json` is set for context length up to 32,768 tokens.
-To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
-
-For supported frameworks, you could add the following to `config.json` to enable YaRN:
-
-```
-{
-	...,
-    "type": "yarn",
-    "mrope_section": [
-        16,
-        24,
-        24
-    ],
-    "factor": 4,
-    "original_max_position_embeddings": 32768
-}
-```
-
-However, it should be noted that this method has a significant impact on the performance of temporal and spatial localization tasks, and is therefore not recommended for use.
-
-At the same time, for long video inputs, since MRoPE itself is more economical with ids, the max_position_embeddings can be directly modified to a larger value, such as 64k.
-
-### Benchmark
-#### Performance of Quantized Models
-This section reports the generation performance of quantized models (including GPTQ and AWQ) of the Qwen2.5-VL series. Specifically, we report:
-
-- MMMU_VAL (Accuracy)
-- DocVQA_VAL (Accuracy)
-- MMBench_DEV_EN (Accuracy)
-- MathVista_MINI (Accuracy)
-
-We use [VLMEvalkit](https://github.com/open-compass/VLMEvalKit) to evaluate all models.
-
-| Model Size | Quantization | MMMU_VAL | DocVQA_VAL | MMBench_EDV_EN | MathVista_MINI  |
-| --- | --- | --- | --- | --- | --- |
-| Qwen2.5-VL-72B-Instruct | BF16<br><sup>([🤗](https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct)[🤖](https://modelscope.cn/models/qwen/Qwen2.5-VL-72B-Instruct)) | 70.0 | 96.1 | 88.2 | 75.3 |
-|  | AWQ<br><sup>([🤗](https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct-AWQ)[🤖](https://modelscope.cn/models/qwen/Qwen2.5-VL-72B-Instruct-AWQ)) | 69.1 | 96.0 | 87.9 | 73.8 |
-| Qwen2.5-VL-7B-Instruct | BF16<br><sup>([🤗](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)[🤖](https://modelscope.cn/models/qwen/Qwen2.5-VL-7B-Instruct)) | 58.4 | 94.9 | 84.1 | 67.9 |
-|  | AWQ<br><sup>([🤗](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct-AWQ)[🤖](https://modelscope.cn/models/qwen/Qwen2.5-VL-7B-Instruct-AWQ)) | 55.6 | 94.6 | 84.2 | 64.7 |
-| Qwen2.5-VL-3B-Instruct | BF16<br><sup>([🤗](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct)[🤖](https://modelscope.cn/models/qwen/Qwen2.5-VL-3B-Instruct)) | 51.7 | 93.0 | 79.8 | 61.4 |
-|  | AWQ<br><sup>([🤗](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct-AWQ)[🤖](https://modelscope.cn/models/qwen/Qwen2.5-VL-3B-Instruct-AWQ)) | 49.1 | 91.8 | 78.0 | 58.8 |
-
-
-
-
-## Citation
-
-If you find our work helpful, feel free to give us a cite.
-
-```
-@misc{qwen2.5-VL,
-    title = {Qwen2.5-VL},
-    url = {https://qwenlm.github.io/blog/qwen2.5-vl/},
-    author = {Qwen Team},
-    month = {January},
-    year = {2025}
-}
-
-@article{Qwen2VL,
-  title={Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution},
-  author={Wang, Peng and Bai, Shuai and Tan, Sinan and Wang, Shijie and Fan, Zhihao and Bai, Jinze and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Fan, Yang and Dang, Kai and Du, Mengfei and Ren, Xuancheng and Men, Rui and Liu, Dayiheng and Zhou, Chang and Zhou, Jingren and Lin, Junyang},
-  journal={arXiv preprint arXiv:2409.12191},
-  year={2024}
-}
-
-@article{Qwen-VL,
-  title={Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond},
-  author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren},
-  journal={arXiv preprint arXiv:2308.12966},
-  year={2023}
-}
 ```