nvidia
/

Eagle2-1B

@@ -1,380 +1,392 @@
----
-license: cc-by-nc-4.0
-pipeline_tag: image-text-to-text
-library_name: transformers
-base_model:
-  - google/paligemma-3b-mix-448
-  - Qwen/Qwen2.5-0.5B-Instruct
-  - google/siglip-so400m-patch14-384
-base_model_relation: merge
-language:
-  - multilingual
-tags:
-  - eagle
-  - VLM
----
-# Eagle-2
-[\[📂 GitHub\]](https://github.com/NVlabs/EAGLE)   [\[📜 Eagle2 Tech Report\]](http://arxiv.org/abs/2501.14818)
-[\[🤗 HF Demo\]](https://huggingface.co/spaces/nvidia/Eagle2-Demo)
-# News:
-- We update the model arch to `eagle_2_5_vl` to support  `generate` feature.
-## Introduction
-We are thrilled to release our latest Eagle2 series Vision-Language Model. Open-source Vision-Language Models (VLMs) have made significant strides in narrowing the gap with proprietary models. However, critical details about data strategies and implementation are often missing, limiting reproducibility and innovation. In this project, we focus on VLM post-training from a data-centric perspective, sharing insights into building effective data strategies from scratch. By combining these strategies with robust training recipes and model design, we introduce Eagle2, a family of performant VLMs. Our work aims to empower the open-source community to develop competitive VLMs with transparent processes.
-In this repo, we are open-sourcing Eagle2-1B, a compact and efficient model designed for scenarios requiring fast inference and minimal computational resources, without compromising essential performance
-## Model Zoo
-We provide the following models:
-| model name         | LLM  | Vision  | Max Length| HF Link|
-| ----------- | ------- |---------|-|-|
-| Eagle2-1B | [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) |  Siglip    | 16K| [🤗 link](https://huggingface.co/NVIDIA/Eagle2-1B)|
-| Eagle2-2B | [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) |  Siglip    | 16K| [🤗 link](https://huggingface.co/NVIDIA/Eagle2-2B)|
-| Eagle2-9B | [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) |  Siglip+ConvNext    | 16K| [🤗 link](https://huggingface.co/NVIDIA/Eagle2-9B)|
-## Benchmark Results
-|          Benchmark           | LLaVa-One-Vision-0.5B | InternVL2-1B | InternVL2.5-1B |Qwen2-VL-2B| Eagle2-1B|
-| :--------------------------: | :------------------: | :----------------: | :----------: |:----------: |:----------: |
-|    DocVQA<sub>test</sub>     |         70.0         |        81.7        |     84.8     |90.1|81.8|
-|    ChartQA<sub>test</sub>    |          61.4         |        72.9        |     75.9     |73.0|77.0|
-|    InfoVQA<sub>test</sub>    |          41.8           |        50.9        |     56.0     |65.5|54.8|
-|    TextVQA<sub>val</sub>     |         -         |        70.0       |     72.0     |79.7|76.6|
-|           OCRBench           |         565          |        754         |     785      |809|767|
-|      MME<sub>sum</sub>       |        1438.0     |       1794.4      |    1950.5   |  1872.0| 1790.2|
-|         RealWorldQA          |        55.6     |        50.3       |    57.5     |62.6|55.4|
-|     AI2D<sub>test</sub>      |         57.1         |        64.1        |     69.3    | 74.7 |70.9|
-|      MMMU<sub>val</sub>      |          31.4       |    36.7     | 40.9  |41.1|38.8|
-| MMVet<sub>GPT-4-Turbo</sub>  |         32.2       |        32.7       |    48.8    | 49.5|40.9|             HallBench<sub>avg</sub>    |         27.9      |        34.0       |     39.0     |**41.7**|35.3
-| MathVista<sub>testmini</sub> |         33.8         |        37.7        |     43.2     |43.0|45.3|
-| MMstar |             37.7    |       45.7      |     50.1|48.0|48.5|
-## Quick Start
-We provide a [inference script](./demo.py) to help you quickly start using the model. We support different input types:
-- pure text input
-- single image input
-- multiple image input
-- video input
-###  Install the dependencies
-```bash
-pip install transformers
-pip install flash-attn
-```
-### single image
-```python
-from PIL import Image
-import requests
-from transformers import AutoProcessor, AutoModel
-import torch
-model = AutoModel.from_pretrained("nvidia/Eagle2-1B",trust_remote_code=True, torch_dtype=torch.bfloat16)
-processor = AutoProcessor.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
-processor.tokenizer.padding_side = "left"
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image",
-                "image": "https://www.ilankelman.org/stopsigns/australia.jpg",
-            },
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-text_list = [processor.apply_chat_template(
-    messages, tokenize=False, add_generation_prompt=True
-)]
-image_inputs, video_inputs = processor.process_vision_info(messages)
-inputs = processor(text = text_list, images=image_inputs, videos=video_inputs, return_tensors="pt", padding=True)
-inputs = inputs.to("cuda")
-model = model.to("cuda")
-generated_ids = model.generate(**inputs, max_new_tokens=1024)
-output_text = processor.batch_decode(
-    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
-)
-print(output_text)
-```
-### stream generation
-```python
-from PIL import Image
-import requests
-from transformers import AutoProcessor, AutoModel, AutoTokenizer
-import torch
-from transformers import TextIteratorStreamer
-import threading
-model = AutoModel.from_pretrained("nvidia/Eagle2-1B",trust_remote_code=True, attn_implementation='flash_attention_2', torch_dtype=torch.bfloat16)
-tokenizer = AutoTokenizer.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
-processor = AutoProcessor.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
-processor.tokenizer.padding_side = "left"
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image",
-                "image": "https://www.ilankelman.org/stopsigns/australia.jpg",
-            },
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-text_list = [processor.apply_chat_template(
-    messages, tokenize=False, add_generation_prompt=True
-)]
-image_inputs, video_inputs = processor.process_vision_info(messages)
-inputs = processor(text = text_list, images=image_inputs, videos=video_inputs, return_tensors="pt", padding=True)
-inputs = inputs.to("cuda")
-model = model.to("cuda")
-streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
-generation_kwargs = dict(
-    **inputs,
-    streamer=streamer,
-    max_new_tokens=1024,
-    do_sample=True,
-    top_p=0.95,
-    temperature=0.8
-)
-thread = threading.Thread(target=model.generate, kwargs=generation_kwargs)
-thread.start()
-for new_text in streamer:
-    print(new_text, end="", flush=True)
-```
-### multiple-images
-```python
-from PIL import Image
-import requests
-from transformers import AutoProcessor, AutoModel
-import torch
-model = AutoModel.from_pretrained("nvidia/Eagle2-1B",trust_remote_code=True, torch_dtype=torch.bfloat16)
-processor = AutoProcessor.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
-processor.tokenizer.padding_side = "left"
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image",
-                "image": "https://www.ilankelman.org/stopsigns/australia.jpg",
-            },
-            {
-                "type": "image",
-                "image": "https://www.nvidia.com/content/dam/en-zz/Solutions/about-nvidia/logo-and-brand/[email protected]",
-            },
-            {"type": "text", "text": "Describe these two images."},
-        ],
-    }
-]
-text_list = [processor.apply_chat_template(
-    messages, tokenize=False, add_generation_prompt=True
-)]
-image_inputs, video_inputs = processor.process_vision_info(messages)
-inputs = processor(text = text_list, images=image_inputs, videos=video_inputs, return_tensors="pt", padding=True)
-inputs = inputs.to("cuda")
-model = model.to("cuda")
-generated_ids = model.generate(**inputs, max_new_tokens=1024)
-output_text = processor.batch_decode(
-    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
-)
-print(output_text)
-```
-### single video
-```python
-from PIL import Image
-import requests
-from transformers import AutoProcessor, AutoModel
-import torch
-model = AutoModel.from_pretrained("nvidia/Eagle2-1B",trust_remote_code=True, torch_dtype=torch.bfloat16)
-processor = AutoProcessor.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
-processor.tokenizer.padding_side = "left"
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "video",
-                "video": "../Eagle2-8B/space_woaudio.mp4",
-            },
-            {"type": "text", "text": "Describe this video."},
-        ],
-    }
-]
-text_list = [processor.apply_chat_template(
-    messages, tokenize=False, add_generation_prompt=True
-)]
-image_inputs, video_inputs, video_kwargs = processor.process_vision_info(messages, return_video_kwargs=True)
-inputs = processor(text = text_list, images=image_inputs, videos=video_inputs, return_tensors="pt", padding=True, videos_kwargs=video_kwargs)
-inputs = inputs.to("cuda")
-model = model.to("cuda")
-generated_ids = model.generate(**inputs, max_new_tokens=1024)
-output_text = processor.batch_decode(
-    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
-)
-print(output_text)
-```
-### multieple videos
-```python
-from PIL import Image
-import requests
-from transformers import AutoProcessor, AutoModel
-import torch
-model = AutoModel.from_pretrained("nvidia/Eagle2-1B",trust_remote_code=True, torch_dtype=torch.bfloat16)
-processor = AutoProcessor.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
-processor.tokenizer.padding_side = "left"
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "video",
-                "video": "../Eagle2-8B/space_woaudio.mp4",
-                "nframes": 10,
-            },
-            {
-                "type": "video",
-                "video": "../Eagle2-8B/video_ocr.mp4",
-                "nframes": 10,
-            },
-            {"type": "text", "text": "Describe these two videos respectively."},
-        ],
-    }
-]
-text_list = [processor.apply_chat_template(
-    messages, tokenize=False, add_generation_prompt=True
-)]
-image_inputs, video_inputs, video_kwargs = processor.process_vision_info(messages, return_video_kwargs=True)
-inputs = processor(text = text_list, images=image_inputs, videos=video_inputs, return_tensors="pt", padding=True, videos_kwargs=video_kwargs)
-inputs = inputs.to("cuda")
-model = model.to("cuda")
-generated_ids = model.generate(**inputs, max_new_tokens=1024)
-output_text = processor.batch_decode(
-    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
-)
-print(output_text)
-```
-### batch inference
-```python
-from PIL import Image
-import requests
-from transformers import AutoProcessor, AutoModel
-import torch
-model = AutoModel.from_pretrained("nvidia/Eagle2-1B",trust_remote_code=True, torch_dtype=torch.bfloat16)
-processor = AutoProcessor.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
-processor.tokenizer.padding_side = "left"
-messages1 = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image",
-                "image": "https://www.ilankelman.org/stopsigns/australia.jpg",
-            },
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-messages2 = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image",
-                "image": "https://www.nvidia.com/content/dam/en-zz/Solutions/about-nvidia/logo-and-brand/[email protected]",
-            },
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-text_list = [processor.apply_chat_template(
-    messages, tokenize=False, add_generation_prompt=True
-) for messages in [messages1, messages2]]
-image_inputs, video_inputs = processor.process_vision_info([messages1, messages2])
-inputs = processor(text = text_list, images=image_inputs, videos=video_inputs, return_tensors="pt", padding=True)
-inputs = inputs.to("cuda")
-model = model.to("cuda")
-generated_ids = model.generate(**inputs, max_new_tokens=1024)
-output_text = processor.batch_decode(
-    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
-)
-print(output_text)
-```
-## TODO
-- [ ] Support vLLM Inference
-- [ ] Provide AWQ Quantization Weights
-- [ ] Provide fine-tuning scripts
-## License/Terms of Use
-- The code is released under the Apache 2.0 license as found in the [LICENSE](https://huggingface.co/NVEagle/Eagle-X5-13B-Chat/blob/main/LICENSE) file.
-- The pretrained model weights are released under the [Creative Commons Attribution: Non-Commercial 4.0 International](https://spdx.org/licenses/CC-BY-NC-4.0) <br>
-- The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms:
-  - Model License of Qwen2.5-0.5B-Instruct: [Apache-2.0](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE)
-  - Model License of PaliGemma: [Gemma license](https://ai.google.dev/gemma/terms)
-## Citation
-## Ethical Considerations
-NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
-Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

+---
+license: cc-by-nc-4.0
+pipeline_tag: image-text-to-text
+library_name: transformers
+base_model:
+- google/paligemma-3b-mix-448
+- Qwen/Qwen2.5-0.5B-Instruct
+- google/siglip-so400m-patch14-384
+base_model_relation: merge
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+tags:
+- eagle
+- VLM
+---
+# Eagle-2
+[\[📂 GitHub\]](https://github.com/NVlabs/EAGLE)   [\[📜 Eagle2 Tech Report\]](http://arxiv.org/abs/2501.14818)
+[\[🤗 HF Demo\]](https://huggingface.co/spaces/nvidia/Eagle2-Demo)
+# News:
+- We update the model arch to `eagle_2_5_vl` to support  `generate` feature.
+## Introduction
+We are thrilled to release our latest Eagle2 series Vision-Language Model. Open-source Vision-Language Models (VLMs) have made significant strides in narrowing the gap with proprietary models. However, critical details about data strategies and implementation are often missing, limiting reproducibility and innovation. In this project, we focus on VLM post-training from a data-centric perspective, sharing insights into building effective data strategies from scratch. By combining these strategies with robust training recipes and model design, we introduce Eagle2, a family of performant VLMs. Our work aims to empower the open-source community to develop competitive VLMs with transparent processes.
+In this repo, we are open-sourcing Eagle2-1B, a compact and efficient model designed for scenarios requiring fast inference and minimal computational resources, without compromising essential performance
+## Model Zoo
+We provide the following models:
+| model name         | LLM  | Vision  | Max Length| HF Link|
+| ----------- | ------- |---------|-|-|
+| Eagle2-1B | [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) |  Siglip    | 16K| [🤗 link](https://huggingface.co/NVIDIA/Eagle2-1B)|
+| Eagle2-2B | [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) |  Siglip    | 16K| [🤗 link](https://huggingface.co/NVIDIA/Eagle2-2B)|
+| Eagle2-9B | [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) |  Siglip+ConvNext    | 16K| [🤗 link](https://huggingface.co/NVIDIA/Eagle2-9B)|
+## Benchmark Results
+|          Benchmark           | LLaVa-One-Vision-0.5B | InternVL2-1B | InternVL2.5-1B |Qwen2-VL-2B| Eagle2-1B|
+| :--------------------------: | :------------------: | :----------------: | :----------: |:----------: |:----------: |
+|    DocVQA<sub>test</sub>     |         70.0         |        81.7        |     84.8     |90.1|81.8|
+|    ChartQA<sub>test</sub>    |          61.4         |        72.9        |     75.9     |73.0|77.0|
+|    InfoVQA<sub>test</sub>    |          41.8           |        50.9        |     56.0     |65.5|54.8|
+|    TextVQA<sub>val</sub>     |         -         |        70.0       |     72.0     |79.7|76.6|
+|           OCRBench           |         565          |        754         |     785      |809|767|
+|      MME<sub>sum</sub>       |        1438.0     |       1794.4      |    1950.5   |  1872.0| 1790.2|
+|         RealWorldQA          |        55.6     |        50.3       |    57.5     |62.6|55.4|
+|     AI2D<sub>test</sub>      |         57.1         |        64.1        |     69.3    | 74.7 |70.9|
+|      MMMU<sub>val</sub>      |          31.4       |    36.7     | 40.9  |41.1|38.8|
+| MMVet<sub>GPT-4-Turbo</sub>  |         32.2       |        32.7       |    48.8    | 49.5|40.9|             HallBench<sub>avg</sub>    |         27.9      |        34.0       |     39.0     |**41.7**|35.3
+| MathVista<sub>testmini</sub> |         33.8         |        37.7        |     43.2     |43.0|45.3|
+| MMstar |             37.7    |       45.7      |     50.1|48.0|48.5|
+## Quick Start
+We provide a [inference script](./demo.py) to help you quickly start using the model. We support different input types:
+- pure text input
+- single image input
+- multiple image input
+- video input
+###  Install the dependencies
+```bash
+pip install transformers
+pip install flash-attn
+```
+### single image
+```python
+from PIL import Image
+import requests
+from transformers import AutoProcessor, AutoModel
+import torch
+model = AutoModel.from_pretrained("nvidia/Eagle2-1B",trust_remote_code=True, torch_dtype=torch.bfloat16)
+processor = AutoProcessor.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
+processor.tokenizer.padding_side = "left"
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "https://www.ilankelman.org/stopsigns/australia.jpg",
+            },
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+text_list = [processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)]
+image_inputs, video_inputs = processor.process_vision_info(messages)
+inputs = processor(text = text_list, images=image_inputs, videos=video_inputs, return_tensors="pt", padding=True)
+inputs = inputs.to("cuda")
+model = model.to("cuda")
+generated_ids = model.generate(**inputs, max_new_tokens=1024)
+output_text = processor.batch_decode(
+    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+### stream generation
+```python
+from PIL import Image
+import requests
+from transformers import AutoProcessor, AutoModel, AutoTokenizer
+import torch
+from transformers import TextIteratorStreamer
+import threading
+model = AutoModel.from_pretrained("nvidia/Eagle2-1B",trust_remote_code=True, attn_implementation='flash_attention_2', torch_dtype=torch.bfloat16)
+tokenizer = AutoTokenizer.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
+processor = AutoProcessor.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
+processor.tokenizer.padding_side = "left"
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "https://www.ilankelman.org/stopsigns/australia.jpg",
+            },
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+text_list = [processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)]
+image_inputs, video_inputs = processor.process_vision_info(messages)
+inputs = processor(text = text_list, images=image_inputs, videos=video_inputs, return_tensors="pt", padding=True)
+inputs = inputs.to("cuda")
+model = model.to("cuda")
+streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+generation_kwargs = dict(
+    **inputs,
+    streamer=streamer,
+    max_new_tokens=1024,
+    do_sample=True,
+    top_p=0.95,
+    temperature=0.8
+)
+thread = threading.Thread(target=model.generate, kwargs=generation_kwargs)
+thread.start()
+for new_text in streamer:
+    print(new_text, end="", flush=True)
+```
+### multiple-images
+```python
+from PIL import Image
+import requests
+from transformers import AutoProcessor, AutoModel
+import torch
+model = AutoModel.from_pretrained("nvidia/Eagle2-1B",trust_remote_code=True, torch_dtype=torch.bfloat16)
+processor = AutoProcessor.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
+processor.tokenizer.padding_side = "left"
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "https://www.ilankelman.org/stopsigns/australia.jpg",
+            },
+            {
+                "type": "image",
+                "image": "https://www.nvidia.com/content/dam/en-zz/Solutions/about-nvidia/logo-and-brand/[email protected]",
+            },
+            {"type": "text", "text": "Describe these two images."},
+        ],
+    }
+]
+text_list = [processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)]
+image_inputs, video_inputs = processor.process_vision_info(messages)
+inputs = processor(text = text_list, images=image_inputs, videos=video_inputs, return_tensors="pt", padding=True)
+inputs = inputs.to("cuda")
+model = model.to("cuda")
+generated_ids = model.generate(**inputs, max_new_tokens=1024)
+output_text = processor.batch_decode(
+    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+### single video
+```python
+from PIL import Image
+import requests
+from transformers import AutoProcessor, AutoModel
+import torch
+model = AutoModel.from_pretrained("nvidia/Eagle2-1B",trust_remote_code=True, torch_dtype=torch.bfloat16)
+processor = AutoProcessor.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
+processor.tokenizer.padding_side = "left"
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "video",
+                "video": "../Eagle2-8B/space_woaudio.mp4",
+            },
+            {"type": "text", "text": "Describe this video."},
+        ],
+    }
+]
+text_list = [processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)]
+image_inputs, video_inputs, video_kwargs = processor.process_vision_info(messages, return_video_kwargs=True)
+inputs = processor(text = text_list, images=image_inputs, videos=video_inputs, return_tensors="pt", padding=True, videos_kwargs=video_kwargs)
+inputs = inputs.to("cuda")
+model = model.to("cuda")
+generated_ids = model.generate(**inputs, max_new_tokens=1024)
+output_text = processor.batch_decode(
+    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+### multieple videos
+```python
+from PIL import Image
+import requests
+from transformers import AutoProcessor, AutoModel
+import torch
+model = AutoModel.from_pretrained("nvidia/Eagle2-1B",trust_remote_code=True, torch_dtype=torch.bfloat16)
+processor = AutoProcessor.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
+processor.tokenizer.padding_side = "left"
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "video",
+                "video": "../Eagle2-8B/space_woaudio.mp4",
+                "nframes": 10,
+            },
+            {
+                "type": "video",
+                "video": "../Eagle2-8B/video_ocr.mp4",
+                "nframes": 10,
+            },
+            {"type": "text", "text": "Describe these two videos respectively."},
+        ],
+    }
+]
+text_list = [processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)]
+image_inputs, video_inputs, video_kwargs = processor.process_vision_info(messages, return_video_kwargs=True)
+inputs = processor(text = text_list, images=image_inputs, videos=video_inputs, return_tensors="pt", padding=True, videos_kwargs=video_kwargs)
+inputs = inputs.to("cuda")
+model = model.to("cuda")
+generated_ids = model.generate(**inputs, max_new_tokens=1024)
+output_text = processor.batch_decode(
+    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+### batch inference
+```python
+from PIL import Image
+import requests
+from transformers import AutoProcessor, AutoModel
+import torch
+model = AutoModel.from_pretrained("nvidia/Eagle2-1B",trust_remote_code=True, torch_dtype=torch.bfloat16)
+processor = AutoProcessor.from_pretrained("nvidia/Eagle2-1B", trust_remote_code=True, use_fast=True)
+processor.tokenizer.padding_side = "left"
+messages1 = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "https://www.ilankelman.org/stopsigns/australia.jpg",
+            },
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+messages2 = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "https://www.nvidia.com/content/dam/en-zz/Solutions/about-nvidia/logo-and-brand/[email protected]",
+            },
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+text_list = [processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+) for messages in [messages1, messages2]]
+image_inputs, video_inputs = processor.process_vision_info([messages1, messages2])
+inputs = processor(text = text_list, images=image_inputs, videos=video_inputs, return_tensors="pt", padding=True)
+inputs = inputs.to("cuda")
+model = model.to("cuda")
+generated_ids = model.generate(**inputs, max_new_tokens=1024)
+output_text = processor.batch_decode(
+    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+## TODO
+- [ ] Support vLLM Inference
+- [ ] Provide AWQ Quantization Weights
+- [ ] Provide fine-tuning scripts
+## License/Terms of Use
+- The code is released under the Apache 2.0 license as found in the [LICENSE](https://huggingface.co/NVEagle/Eagle-X5-13B-Chat/blob/main/LICENSE) file.
+- The pretrained model weights are released under the [Creative Commons Attribution: Non-Commercial 4.0 International](https://spdx.org/licenses/CC-BY-NC-4.0) <br>
+- The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms:
+  - Model License of Qwen2.5-0.5B-Instruct: [Apache-2.0](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE)
+  - Model License of PaliGemma: [Gemma license](https://ai.google.dev/gemma/terms)
+## Citation
+## Ethical Considerations
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
+Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).