You can deploy this model and start earning money today!

prithivMLmods/
Qwen3-VL-4B-Instruct-Unredacted-MAX

Name: Qwen3-VL-4B-Instruct-Unredacted-MAX
Availability: InStock

Views

No views yet

Qwen3-VL-4B-Instruct-Unredacted-MAX

Qwen3-VL-4B-Instruct-Unredacted-MAX is an optimized release built on top of huihui-ai/Qwen3-VL-4B-Instruct-abliterated. This version focuses on updated packaging, improved Transformers compatibility, and stable multimodal inference behavior, while preserving the core vision-language reasoning capabilities of the original architecture. The result is a capable 4B vision-language model designed for efficient deployment, research workflows, and multimodal experimentation.

Key Highlights

Optimized Release Structure Streamlined repository organization for easier loading, deployment, and inference workflows.
Modern Transformers Compatibility Updated for stable integration with recent Hugging Face Transformers versions.
4B Vision-Language Architecture Built on Qwen3-VL-4B-Instruct, balancing multimodal capability with efficient compute requirements.
Stable Multimodal Inference Designed for consistent performance across image-text understanding tasks.
Efficient Caption Generation Produces structured and detailed descriptions suitable for annotation and dataset pipelines.
Dynamic Resolution Support Retains native support for varying 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-4B-Instruct-abliterated

Quick Start with Transformers

python

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

model = Qwen3VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/Qwen3-VL-4B-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
Prototyping AI systems combining text and vision
Lightweight deployment on consumer or mid-range GPUs
Experimental workflows in multimodal understanding

Limitations & Risks

Important Note: This model inherits constraints and behavior from its base architecture.

Output quality depends heavily on image clarity and prompt design
May produce incomplete or inconsistent interpretations in complex scenarios
Requires sufficient GPU memory for stable inference
Performance varies with decoding settings and runtime optimization