Qwen2.5-VL-32B-Instruct-Unredacted-MAX

Qwen2.5-VL-32B-Instruct-Unredacted-MAX is an optimized release built on top of huihui-ai/Qwen2.5-VL-32B-Instruct-abliterated. This version focuses on updated shard sizing, repository optimization, and compatibility improvements for the latest Transformers releases, while preserving the multimodal reasoning and captioning capabilities of the original model. The result is a highly capable 32B vision-language model designed for stable inference, efficient deployment, and modern ecosystem integration.

Key Highlights

• Optimized Repository Packaging Improved model organization for smoother downloads, loading, and deployment workflows.

• Latest Transformers Compatibility Re-sharded and updated for improved compatibility with recent Transformers releases.

• 32B Vision-Language Architecture Built on top of Qwen2.5-VL-32B-Instruct, delivering strong multimodal reasoning capacity.

• Stable Multimodal Inference Designed for consistent image and text processing across a range of deployment environments.

• High-Fidelity Captioning Suitable for detailed visual description, dataset generation, and multimodal analysis workflows.

• Dynamic Resolution Support Retains Qwen2.5-VL’s ability to handle varying image resolutions and aspect ratios effectively.

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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-32B-Instruct-abliterated.

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Quick Start with Transformers

from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/Qwen2.5-VL-32B-Instruct-Unredacted-MAX",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Qwen2.5-VL-32B-Instruct-Unredacted-MAX"
)

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 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=256)

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

• Multimodal Research Studying vision-language reasoning across image and text inputs.

• Captioning and Dataset Work Generating detailed descriptions for accessibility, annotation, and enrichment workflows.

• Evaluation and Prototyping Testing multimodal pipelines and experimenting with visual understanding tasks.

• Local and High-Performance Deployment Running large vision-language models on optimized GPU setups.

Limitations & Risks

Important Note: This model inherits the behavior and limitations of its base architecture.

• Output Variability Responses may vary depending on image quality, prompt design, and decoding settings.

• Resource Requirements The 32B model requires substantial VRAM, especially for high-resolution image processing.

• Deployment Constraints Performance depends on runtime optimization and hardware configuration.

• General Model Limitations May still produce incorrect, incomplete, or inconsistent outputs in complex scenarios.