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
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Abliterated / Uncensored Captioning: Fine-tuned to bypass common content filters while preserving factual and descriptive richness across diverse visual categories.
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High-Fidelity Descriptions: Generates comprehensive captions for general, artistic, technical, abstract, and low-context images.
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Robust Across Aspect Ratios: Capable of accurately captioning images with wide, tall, square, and irregular dimensions.
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Variational Detail Control: Produces outputs with both high-level summaries and fine-grained descriptions as needed.
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Foundation on Qwen2.5-VL Architecture: Leverages the strengths of the Qwen2.5-VL-7B multimodal model for visual reasoning, comprehension, and instruction-following.
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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
- 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
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.