--- license: cc-by-nc-4.0 pipeline_tag: image-text-to-text library_name: transformers base_model: - google/paligemma-3b-mix-448 - Qwen/Qwen2.5-1.5B-Instruct - google/siglip-so400m-patch14-384 base_model_relation: merge language: - multilingual tags: - eagle - VLM --- # Eagle2-2B GGUF Models ## Model Generation Details This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`b9c3eefd`](https://github.com/ggerganov/llama.cpp/commit/b9c3eefde1b67104bd993485ff38dd62abe9d70c). --- Click here to get info on choosing the right GGUF model format --- # Eagle-2 [\[πŸ“‚ GitHub\]](https://github.com/NVlabs/EAGLE) [\[πŸ“œ Eagle2 Tech Report\]](http://arxiv.org/abs/2501.14818) [\[πŸ€— HF Demo\]](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 Eagle2-2B, a lightweight model that achieves remarkable efficiency and speed while maintaining solid 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| [πŸ€— 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| [πŸ€— link](https://huggingface.co/NVIDIA/Eagle2-2B)| | Eagle2-9B | [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) | Siglip+ConvNext | 16K| [πŸ€— link](https://huggingface.co/NVIDIA/Eagle2-9B)| ## Benchmark Results | Benchmark | InternVL2-2B | InternVL2.5-2B | InternVL2-4B |Qwen2-VL-2B| Eagle2-2B| | :--------------------------: | :------------------: | :----------------: | :----------: |:----------: |:----------: | | DocVQAtest | 86.9 | 88.7 | 89.2 |90.1|88.0| | ChartQAtest | 76.2 | 79.2 | 81.5 |73.0|82.0| | InfoVQAtest | 58.9 | 60.9 | 67.0 |65.5|65.8| | TextVQAval | 73.4 | 74.3 | 74.4 |79.7|79.1| | OCRBench | 784 | 804 | 788 |809|818| | MMEsum | 1876.8 | 2138.2 | 2059.8 |1872.0 | 2109.8 | RealWorldQA | 57.3 | 60.1 | 60.7 |62.6|63.1| | AI2Dtest | 74.1 | 74.9 | 74.7 | 78.9 |79.3| | MMMUval | 36.3 | 43.6 | 47.9 |41.1|43.1| | MMVetGPT-4-Turbo | 39.5 | 60.8 | 51.0 | 49.5|53.8| | HallBenchavg | 37.9 | 42.6 | 41.9 |41.7|45.8 | MathVistatestmini | 46.3 | 51.3 | 58.6 |43.0|54.7| | MMstar | 50.1 | 53.7 | 54.3|48.0|56.4| ## 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/01-nvidia-logo-vert-500x200-2c50-d@2x.png", }, {"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/01-nvidia-logo-vert-500x200-2c50-d@2x.png", }, {"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)
- The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms: - Model License of Qwen2.5-1.5B-Instruct: [Apache-2.0](https://huggingface.co/Qwen/Qwen2.5-1.5B-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/). --- # πŸš€ If you find these models useful Help me test my **AI-Powered Quantum Network Monitor Assistant** with **quantum-ready security checks**: πŸ‘‰ [Quantum Network Monitor](https://readyforquantum.com/?assistant=open&utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : [Source Code Quantum Network Monitor](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/GGUFModelBuilder) πŸ’¬ **How to test**: Choose an **AI assistant type**: - `TurboLLM` (GPT-4.1-mini) - `HugLLM` (Hugginface Open-source models) - `TestLLM` (Experimental CPU-only) ### **What I’m Testing** I’m pushing the limits of **small open-source models for AI network monitoring**, specifically: - **Function calling** against live network services - **How small can a model go** while still handling: - Automated **Nmap security scans** - **Quantum-readiness checks** - **Network Monitoring tasks** 🟑 **TestLLM** – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space): - βœ… **Zero-configuration setup** - ⏳ 30s load time (slow inference but **no API costs**) . No token limited as the cost is low. - πŸ”§ **Help wanted!** If you’re into **edge-device AI**, let’s collaborate! ### **Other Assistants** 🟒 **TurboLLM** – Uses **gpt-4.1-mini** : - **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited. - **Create custom cmd processors to run .net code on Quantum Network Monitor Agents** - **Real-time network diagnostics and monitoring** - **Security Audits** - **Penetration testing** (Nmap/Metasploit) πŸ”΅ **HugLLM** – Latest Open-source models: - 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita. ### πŸ’‘ **Example commands you could test**: 1. `"Give me info on my websites SSL certificate"` 2. `"Check if my server is using quantum safe encyption for communication"` 3. `"Run a comprehensive security audit on my server"` 4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a [Quantum Network Monitor Agent](https://readyforquantum.com/Download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) to run the .net code on. This is a very flexible and powerful feature. Use with caution! ### Final Word I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAIβ€”all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is [open source](https://github.com/Mungert69). Feel free to use whatever you find helpful. If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) β˜•. Your support helps cover service costs and allows me to raise token limits for everyone. I'm also open to job opportunities or sponsorship. Thank you! 😊