Lh41-1042-Magellanic-7B-0711/README.md

---
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.