---
license: apache-2.0
language:
- en
- zh
- th
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
tags:
- text-generation-inference
- uncensored
- image-captioning
- vlm
- visual-understanding
- caption
- image-to-text
pipeline_tag: image-text-to-text
library_name: transformers
datasets:
- prithivMLmods/blip3o-caption-mini-arrow
- prithivMLmods/Caption3o-Opt-v2
---

![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/BZUG9GehjjprWyoQHTLxk.png)

# **Qwen2.5-VL-7B-Abliterated-Caption-it**

> The **Qwen2.5-VL-7B-Abliterated-Caption-it** model is a fine-tuned version of **Qwen2.5-VL-7B-Instruct**, tailored for **Abliterated Captioning** / **Uncensored Image Captioning**. This variant is designed to generate highly detailed and descriptive captions across a broad range of visual categories, including images with complex, sensitive, or nuanced content—across varying aspect ratios and resolutions.

# Key Highlights

* **Abliterated / Uncensored Captioning**: Fine-tuned to bypass common content filters while preserving factual and descriptive richness across diverse visual categories.

* **High-Fidelity Descriptions**: Generates comprehensive captions for general, artistic, technical, abstract, and low-context images.

* **Robust Across Aspect Ratios**: Capable of accurately captioning images with wide, tall, square, and irregular dimensions.

* **Variational Detail Control**: Produces outputs with both high-level summaries and fine-grained descriptions as needed.

* **Foundation on Qwen2.5-VL Architecture**: Leverages the strengths of the Qwen2.5-VL-7B multimodal model for visual reasoning, comprehension, and instruction-following.

* **Multilingual Output Capability**: Can support multilingual descriptions (English as default), adaptable via prompt engineering.

# Training Details

This model was fine-tuned using the following datasets:

* **[prithivMLmods/blip3o-caption-mini-arrow](https://huggingface.co/datasets/prithivMLmods/blip3o-caption-mini-arrow)**
* **[prithivMLmods/Caption3o-Opt-v2](https://huggingface.co/datasets/prithivMLmods/Caption3o-Opt-v2)**
* **Private/unlisted datasets** curated for uncensored and domain-specific image captioning tasks.

The training objective focused on enhancing performance in unconstrained, descriptive image captioning—especially for edge cases commonly filtered out in standard captioning benchmarks.

# Quick Start with Transformers

> [!note]
Instruction Query: Provide a detailed caption for the image

```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/Qwen2.5-VL-7B-Abliterated-Caption-it", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen2.5-VL-7B-Abliterated-Caption-it")

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 suited for:

* Generating detailed and unfiltered image captions for general-purpose or artistic datasets.
* Content moderation research, red-teaming, and generative safety evaluations.
* Enabling descriptive captioning for visual datasets typically excluded from mainstream models.
* Use in creative applications (e.g., storytelling, art generation) that benefit from rich descriptive captions.
* Captioning for non-standard aspect ratios and stylized visual content.

# Limitations

* May produce explicit, sensitive, or offensive descriptions depending on image content and prompts.
* Not suitable for deployment in production systems requiring content filtering or moderation.
* Can exhibit variability in caption tone or style depending on input prompt phrasing.
* Accuracy for unfamiliar or synthetic visual styles may vary.