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prithivMLmods
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Model Details
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prithivMLmods
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prithivMLmods/Qwen3.6-27B-abliterated-rMAX
Input Modalities
Output Modalities
Supported Functionality
This model has been re-sharded and optimized for the latest Transformers version from the base model: https://huggingface.co/huihui-ai/Huihui-Qwen3.6-27B-abliterated.
The model is trained on a curated mixture of long-caption and optimization datasets:
Caption Datasets
prithivMLmods/Caption3o-LongCap-v4prithivMLmods/Caption3o-XL-v4prithivMLmods/Caption3o-Opt-v3prithivMLmods/Caption3o-Opt-v3-TinyAlignment / Evaluation Dataset
prithivMLmods/harm_benchThese datasets collectively enhance long-form caption quality, structural richness, and robustness under diverse prompts.
Qwen/Qwen3.6-27BprithivMLmods/Qwen3.6-27B-abliterated-rMAXbash
pip install transformers==5.4.0# or latestpip install git+https://github.com/huggingface/transformers.gitpython
from transformers import Qwen3_5ForConditionalGeneration, AutoProcessorimport torch model = Qwen3_5ForConditionalGeneration.from_pretrained( "prithivMLmods/CapQwen3.6-27B-BLIP3o-Long-Caption-Distilled", torch_dtype="auto", device_map="auto") processor = AutoProcessor.from_pretrained( "prithivMLmods/CapQwen3.6-27B-BLIP3o-Long-Caption-Distilled") messages = [ { "role": "user", "content": [ {"type": "text", "text": "Generate a highly detailed caption of a futuristic city skyline at sunset."} ], }] text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True) inputs = processor( text=[text], padding=True, return_tensors="pt").to("cuda") generated_ids = model.generate(**inputs, max_new_tokens=512) 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)Important Note: This model intentionally minimizes built-in safety refusals.
pip install transformers==5.4.0# or latestpip install git+https://github.com/huggingface/transformers.gitfrom transformers import Qwen3_5ForConditionalGeneration, AutoProcessorimport torch model = Qwen3_5ForConditionalGeneration.from_pretrained( "prithivMLmods/CapQwen3.6-27B-BLIP3o-Long-Caption-Distilled", torch_dtype="auto", device_map="auto") processor = AutoProcessor.from_pretrained( "prithivMLmods/CapQwen3.6-27B-BLIP3o-Long-Caption-Distilled") messages = [ { "role": "user", "content": [ {"type": "text", "text": "Generate a highly detailed caption of a futuristic city skyline at sunset."} ], }] text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True) inputs = processor( text=[text], padding=True, return_tensors="pt").to("cuda") generated_ids = model.generate(**inputs, max_new_tokens=512) 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)