Update README.md

README.md

@@ -9,4 +9,94 @@ library_name: transformers
 tags:
 - VLMec:Vision-Language Model for extended reasoning
 - text-generation-inference
----
+---
+
+# **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🤗
+
+```python
+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.