prithivMLmods
/

Nemesis-VLMer-7B-0818

Image-Text-to-Text

VLMer:Vision-Language Model for extended reasoning

text-generation-inference

Model card Files Files and versions

prithivMLmods commited on Aug 17

Commit

31b11a2

·

verified ·

1 Parent(s): 72b07f9

Update README.md

Files changed (1) hide show

README.md +3 -1

README.md CHANGED Viewed

@@ -11,6 +11,8 @@ tags:
 - 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.
@@ -99,4 +101,4 @@ This model is intended for:
 * 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.

 - text-generation-inference
 ---
+![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/UWH0Ug5MBj65_cURHIsq8.png)
 # **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.
 * 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.