Nemesis-VLMer-7B-0818

The Nemesis-VLMer-7B-0818 model is a fine-tuned version of Qwen2.5-VL-7B-Instruct, optimized for Reasoning, Content Analysis, and Visual Question Answering (VQA). Built on top of the Qwen2.5-VL architecture, this model enhances multimodal comprehension capabilities with focused training on reasoning-oriented and analysis-rich datasets for superior reasoning, content interpretation, and visual question answering tasks.

Key Enhancements

• Context-Aware Multimodal Reasoning and Linking: Advanced capability for understanding multimodal context and establishing connections across text, images, and structured elements.

• Enhanced Content Analysis: Designed to efficiently interpret and analyze complex content, ranging from structured text to multimodal information.

• Visual Question Answering (VQA): Specialized for accurately answering visual and multimodal queries across diverse domains.

• Advanced Reasoning Capabilities: Optimized for logical, mathematical, and contextual reasoning tasks involving charts, tables, and diagrams.

• State-of-the-Art Performance Across Benchmarks: Achieves competitive results on reasoning and visual QA datasets such as DocVQA, MathVista, RealWorldQA, and MTVQA.

• Video Understanding up to 20+ minutes: Supports detailed comprehension of long-duration videos for reasoning, summarization, question answering, and multi-modal analysis.

• Visually-Grounded Device Interaction: Enables mobile or robotic device operation via visual inputs and text-based instructions using contextual understanding and reasoning-driven decision-making logic.

Quick Start with Transformers🤗

from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/Nemesis-VLMer-7B-0818", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/Nemesis-VLMer-7B-0818")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "What reasoning can you infer from this image?"},
        ],
    }
]

text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

Intended Use

This model is intended for:

• Context-aware multimodal reasoning and linking across diverse inputs.
• High-fidelity content analysis and interpretation for structured and unstructured data.
• Visual question answering (VQA) across educational, enterprise, and research applications.
• Reasoning-driven analysis of charts, graphs, tables, and visual data representations.
• Extraction and LaTeX formatting of mathematical expressions for academic and professional use.
• Retrieval, reasoning, and summarization from long documents, slides, and multi-modal sources.
• Multilingual reasoning and structured content analysis for global use cases.
• Robotic or mobile automation with vision-guided, reasoning-based contextual interaction.

Limitations

• May show degraded performance on extremely low-quality or occluded images.
• Not optimized for real-time applications on low-resource or edge devices due to computational demands.
• Variable accuracy on uncommon or low-resource languages or scripts.
• Long video processing may require substantial memory and is not optimized for streaming applications.
• Visual token settings affect performance; suboptimal configurations can impact results.
• In rare cases, outputs may contain hallucinated or contextually misaligned reasoning steps.