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Model: prithivMLmods/Lh41-1042-Magellanic-7B-0711 Source: Original Platform
2026-07-09 16:34:06 +08:00
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+---
+license: apache-2.0
+tags:
+- trl
+- text-generation-inference
+- image-captioning
+- optical-character-recognition
+- intelligent-character-recognition
+- caption
+- ocr
+- visual-understanding
+- art
+- icr
+- image-to-text
+- vlm
+- science
+language:
+- en
+- zh
+library_name: transformers
+pipeline_tag: image-text-to-text
+base_model:
+- Qwen/Qwen2.5-VL-7B-Instruct
+---
+
+![1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/sGnKbCZGY6AeQtTWlWmxt.png)
+
+# **Lh41-1042-Magellanic-7B-0711**
+
+> The **Lh41-1042-Magellanic-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 3,000K image pairs for superior image understanding and reasoning tasks across all categories of images with variational dimensions.
+
+# Key Enhancements
+
+* **Advanced Image Captioning**: Superior capability for generating detailed and contextually accurate descriptions of images across diverse categories and dimensions.
+
+* **Enhanced Visual Analysis**: Designed to efficiently analyze and interpret complex visual content, patterns, and relationships within images.
+
+* **Superior Image Reasoning**: Optimized for logical reasoning and inference based on visual information, enabling complex visual question answering.
+
+* **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 Across Resolutions**: Achieves competitive results on OCR and visual QA benchmarks such as DocVQA, MathVista, RealWorldQA, and MTVQA.
+
+* **Video Understanding up to 20+ minutes**: Supports detailed comprehension of long-duration videos for content summarization, Q&A, and multi-modal reasoning.
+
+* **Visually-Grounded Device Interaction**: Enables mobile/robotic device operation via visual inputs and text-based instructions using contextual understanding and 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/Lh41-1042-Magellanic-7B-0711", torch_dtype="auto", device_map="auto"
+)
+
+processor = AutoProcessor.from_pretrained("prithivMLmods/Lh41-1042-Magellanic-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."},
+        ],
+    }
+]
+
+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:
+
+* Advanced image captioning with contextually rich and detailed descriptions.
+* High-fidelity visual analysis and interpretation of complex visual content.
+* Image reasoning tasks requiring logical inference and pattern recognition.
+* Visual question answering for educational and enterprise applications.
+* Multi-modal content understanding across diverse image categories and dimensions.
+* Automated image description generation for accessibility and content management.
+* Visual content analysis for creative and professional applications.
+* Robotic or mobile automation with vision-guided contextual interaction.
+
+## Training Details
+
+| Parameter               | Value                                               |
+|-------------------------|-----------------------------------------------------|
+| **Dataset Size**        | 3,000K image pairs                                  |
+| **Model Architecture**  | `Qwen2_5_VLForConditionalGeneration`                |
+| **Total Disk Volume**   | 600,000 MB                                          |
+| **Training Time**       | approx. 16,488 seconds (~4.58 hours)               |
+| **Model Stage**         | Experimental                                        |
+| **Hardware**            | 3 × NVIDIA A40 (29 vCPUs)                          |
+| **Warmup Steps**        | 750                                                 |
+| **Precision**           | bfloat16                                            |
+
+# 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 visual patterns or highly specialized domain images.
+* 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 information.
+* As an experimental model, performance may vary across different use cases and requires further validation.