prithivMLmods/Qwen3-VL-8B-Instruct-Unredacted-MAX

Qwen3-VL-8B-Instruct-Unredacted-MAX

Qwen3-VL-8B-Instruct-Unredacted-MAX is an optimized release built on top of huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated. This version focuses on packaging improvements, inference stability, and modern Transformers compatibility, while preserving the strong multimodal reasoning capabilities of the base architecture. The result is a powerful 8B vision-language model designed for efficient research, structured captioning, and multimodal experimentation at scale.

Key Highlights

• —Optimized Release Pipeline Improved repository structure and loading consistency for smoother deployment and inference.

• —Modern Transformers Integration Updated compatibility for recent Hugging Face Transformers versions and vision-language utilities.

• —8B Vision-Language Architecture Built on Qwen3-VL-8B-Instruct, offering strong reasoning ability across image-text tasks with balanced compute requirements.

• —Stable Multimodal Inference Improved consistency for caption generation, visual reasoning, and structured outputs.

• —High-Quality Caption Generation Produces detailed, structured descriptions suitable for dataset creation, annotation workflows, and accessibility applications.

• —Dynamic Resolution Handling Maintains native support for variable image resolutions and aspect ratios.

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/Huihui-Qwen3-VL-8B-Instruct-abliterated

Quick Start with Transformers

from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch

model = Qwen3VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/Qwen3-VL-8B-Instruct-Unredacted-MAX",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Qwen3-VL-8B-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)

output_text = processor.batch_decode(
    [out[len(inp):] for inp, out in zip(inputs.input_ids, generated_ids)],
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False
)

print(output_text)

Intended Use

• —Multimodal research and vision-language evaluation
• —Image captioning and dataset generation pipelines
• —Red-teaming and robustness testing of VLMs
• —Creative and descriptive visual storytelling tasks
• —AI system prototyping with image-text reasoning components

Limitations & Risks

Important Note: This model inherits behavioral characteristics from its base architecture and fine-tuning process.

• —Performance depends on image quality, prompt clarity, and decoding settings
• —May produce incomplete or inconsistent reasoning in complex visual scenes
• —Requires moderate to high VRAM for stable inference depending on resolution
• —Output quality varies across domains such as medical, artistic, or technical imagery