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prithivMLmods
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Model Details
Model Provider
prithivMLmods
Model Tree
Qwen/Qwen3-VL-32B-Instruct
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-VL-32B-Instruct-abliterated.
python
from transformers import Qwen3VLForConditionalGeneration, AutoProcessorfrom qwen_vl_utils import process_vision_infoimport torch model = Qwen3VLForConditionalGeneration.from_pretrained( "prithivMLmods/Qwen3-VL-32B-Instruct-abliterated-v1", torch_dtype="auto", device_map="auto") processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3-VL-32B-Instruct-abliterated-v1") messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, {"type": "text", "text": "Provide a detailed caption and reasoning for this image."}, ], }] 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",).to("cuda") generated_ids = model.generate(**inputs, max_new_tokens=128)generated_ids_trimmed = [out[len(inp):] for inp, out 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 Qwen3VLForConditionalGeneration, AutoProcessorfrom qwen_vl_utils import process_vision_infoimport torch model = Qwen3VLForConditionalGeneration.from_pretrained( "prithivMLmods/Qwen3-VL-32B-Instruct-abliterated-v1", torch_dtype="auto", device_map="auto") processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3-VL-32B-Instruct-abliterated-v1") messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, {"type": "text", "text": "Provide a detailed caption and reasoning for this image."}, ], }] 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",).to("cuda") generated_ids = model.generate(**inputs, max_new_tokens=128)generated_ids_trimmed = [out[len(inp):] for inp, out 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)