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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-VL-7B-Instruct |
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pipeline_tag: image-text-to-text |
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library_name: transformers |
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tags: |
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- VLMer:Vision-Language Model for extended reasoning |
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- text-generation-inference |
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- VLR |
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--- |
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# **Nemesis-VLMer-7B-0818** |
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> 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. |
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## Key Enhancements |
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* **Context-Aware Multimodal Reasoning and Linking**: Advanced capability for understanding multimodal context and establishing connections across text, images, and structured elements. |
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* **Enhanced Content Analysis**: Designed to efficiently interpret and analyze complex content, ranging from structured text to multimodal information. |
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* **Visual Question Answering (VQA)**: Specialized for accurately answering visual and multimodal queries across diverse domains. |
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* **Advanced Reasoning Capabilities**: Optimized for logical, mathematical, and contextual reasoning tasks involving charts, tables, and diagrams. |
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* **State-of-the-Art Performance Across Benchmarks**: Achieves competitive results on reasoning and visual QA datasets such as DocVQA, MathVista, RealWorldQA, and MTVQA. |
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* **Video Understanding up to 20+ minutes**: Supports detailed comprehension of long-duration videos for reasoning, summarization, question answering, and multi-modal analysis. |
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* **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. |
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## Quick Start with Transformers🤗 |
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```python |
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained( |
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"prithivMLmods/Nemesis-VLMer-7B-0818", torch_dtype="auto", device_map="auto" |
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) |
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processor = AutoProcessor.from_pretrained("prithivMLmods/Nemesis-VLMer-7B-0818") |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "image", |
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", |
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}, |
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{"type": "text", "text": "What reasoning can you infer from this image?"}, |
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], |
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} |
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] |
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text = processor.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True |
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) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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) |
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inputs = inputs.to("cuda") |
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generated_ids = model.generate(**inputs, max_new_tokens=128) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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output_text = processor.batch_decode( |
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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) |
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print(output_text) |
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``` |
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## Intended Use |
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This model is intended for: |
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* Context-aware multimodal reasoning and linking across diverse inputs. |
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* High-fidelity content analysis and interpretation for structured and unstructured data. |
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* Visual question answering (VQA) across educational, enterprise, and research applications. |
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* Reasoning-driven analysis of charts, graphs, tables, and visual data representations. |
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* Extraction and LaTeX formatting of mathematical expressions for academic and professional use. |
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* Retrieval, reasoning, and summarization from long documents, slides, and multi-modal sources. |
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* Multilingual reasoning and structured content analysis for global use cases. |
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* Robotic or mobile automation with vision-guided, reasoning-based contextual interaction. |
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## Limitations |
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* May show degraded performance on extremely low-quality or occluded images. |
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* Not optimized for real-time applications on low-resource or edge devices due to computational demands. |
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* Variable accuracy on uncommon or low-resource languages or scripts. |
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* Long video processing may require substantial memory and is not optimized for streaming applications. |
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* Visual token settings affect performance; suboptimal configurations can impact results. |
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* In rare cases, outputs may contain hallucinated or contextually misaligned reasoning steps. |
