初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/WR30a-Deep-7B-0711 Source: Original Platform
2026-07-09 12:38:06 +08:00
commit 0dde139d3a
20 changed files with 346 additions and 0 deletions
--- a/README.md
+++ b/README.md
@@ -0,0 +1,129 @@
+---
+license: apache-2.0
+language:
+- en
+- zh
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- trl
+- text-generation-inference
+- image-captioning
+- optical-character-recognition
+- intelligent-character-recognition
+- caption
+- ocr
+- visual-understanding
+- art
+- icr
+- image-to-text
+- vlm
+- math
+- stem
+base_model:
+- prithivMLmods/VIREX-062225-exp
+---
+
+![1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/WjTcq6bA8tjqCTnytgXVM.png)
+
+# **WR30a-Deep-7B-0711**
+
+> The **WR30a-Deep-7B-0711** model is a fine-tuned version of **Qwen2.5-VL-7B-Instruct**, optimized for **Image Captioning**, **Visual Analysis**, and **Image Reasoning**. Built on top of the Qwen2.5-VL architecture, this experimental model enhances visual comprehension capabilities with focused training on 1,500K image pairs for superior image understanding and reasoning tasks across all categories of images with variational dimensions.
+
+# Key Enhancements
+
+* **Superior Image Captioning**: Advanced capability for generating detailed, contextually accurate captions for diverse image types and content.
+
+* **Enhanced Visual Analysis**: Designed to efficiently analyze and interpret complex visual information across different image categories and formats.
+
+* **Advanced Image Reasoning**: Optimized for logical reasoning about visual content, understanding relationships, and making inferences from images.
+
+* **Multi-Category Image Support**: Specialized in handling all categories of images with variational dimensions, from simple objects to complex scenes.
+
+* **State-of-the-Art Performance**: Achieves competitive results on visual understanding benchmarks and real-world image analysis tasks.
+
+* **Dimensional Flexibility**: Supports images of various resolutions and aspect ratios for comprehensive visual processing.
+
+* **Cross-Domain Visual Understanding**: Enables robust performance across different visual domains and content types.
+
+# 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/WR30a-Deep-7B-0711", torch_dtype="auto", device_map="auto"
+)
+
+processor = AutoProcessor.from_pretrained("prithivMLmods/WR30a-Deep-7B-0711")
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
+            },
+            {"type": "text", "text": "Describe this image in detail."},
+        ],
+    }
+]
+
+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:
+
+* High-quality image captioning across diverse visual content and categories.
+* Comprehensive visual analysis and interpretation of complex imagery.
+* Advanced image reasoning for educational, research, and commercial applications.
+* Multi-dimensional image understanding regardless of resolution or aspect ratio.
+* Visual question answering and image-based dialogue systems.
+* Content moderation and automated image classification tasks.
+* Creative applications requiring detailed visual understanding.
+* Accessibility tools for image description and visual assistance.
+
+## Training Details
+
+| Parameter               | Value                                               |
+|-------------------------|-----------------------------------------------------|
+| **Dataset Size**        | 1,500K image pairs                                  |
+| **Model Architecture**  | `Qwen2_5_VLForConditionalGeneration`                |
+| **Total Disk Volume**   | 400,000 MB                                          |
+| **Training Time**       | approx. 9,612 seconds (~2.67 hours)                |
+| **Model Stage**         | Experimental                                        |
+| **Hardware**            | 2 × NVIDIA A40 (19 vCPUs)                          |
+| **Precision**           | bfloat16                                            |
+
+# Limitations
+
+* May show degraded performance on extremely low-quality or heavily corrupted images.
+* Not optimized for real-time applications on low-resource or edge devices due to computational demands.
+* Variable accuracy on highly specialized or domain-specific visual content.
+* Performance may vary with unusual image compositions or artistic styles.
+* Being in experimental stage, outputs should be validated for critical applications.
+* May require fine-tuning for specific niche use cases or domains.