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prithivMLmods
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Model Details
Model Provider
prithivMLmods
Model Tree
Qwen/Qwen2.5-VL-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
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.
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.
Base Model Signatures:
This model has been re-sharded and optimized for the latest Transformers version from the base model: https://huggingface.co/huihui-ai/Qwen2.5-VL-7B-Instruct-abliterated.
This model was fine-tuned using the following datasets:
The training objective focused on enhancing performance in unconstrained, descriptive image captioning—especially for edge cases commonly filtered out in standard captioning benchmarks.
[!note] Instruction Query: Provide a detailed caption for the image
python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessorfrom 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)This model is suited for:
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessorfrom 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)