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text_encoder/README.md

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+---
+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.