Mungert / Eagle2-1B-GGUF

Eagle2-1B GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit b9c3eefd .

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Eagle-2

[📂 GitHub] [📜 Eagle2 Tech Report] [🤗 HF 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:

Benchmark Results

Quick Start

We provide a inference script 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

pip install transformers
pip install flash-attn

single image

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

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

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

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

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

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 file.
• The pretrained model weights are released under the Creative Commons Attribution: Non-Commercial 4.0 International
  
• 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
  • Model License of PaliGemma: Gemma license

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 .

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🚀 If you find these models useful

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks :

👉 Quantum Network Monitor

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 . You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 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):

• ✅ Zero-configuration setup
• ⏳ 30s load time (slow inference but no API costs ) . No token limited as the cost is low.
• 🔧 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)

🔵 HugLLM – Latest Open-source models:

• 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 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 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 OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source . Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. 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! 😊