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Qwen2.5-VL-3B-Instruct-Unredacted-MAX

Qwen2.5-VL-3B-Instruct-Unredacted-MAX is an optimized release built on top of huihui-ai/Qwen2.5-VL-3B-Instruct-abliterated. This version focuses on improved model packaging, updated compatibility with modern Transformers pipelines, and stable multimodal inference behavior, while preserving the core vision-language reasoning capabilities of the original architecture. The result is a compact 3B vision-language model designed for efficient deployment, research experimentation, and multimodal application development.

Key Highlights

• Optimized Release Packaging Streamlined repository structure for smoother loading, inference, and deployment workflows.

• Modern Transformers Compatibility Updated to ensure stable integration with recent Hugging Face Transformers versions.

• 3B Vision-Language Architecture Built on Qwen2.5-VL-3B-Instruct, balancing multimodal capability with lightweight deployment requirements.

• Stable Multimodal Inference Designed for consistent performance across image-text reasoning tasks.

• Efficient Caption Generation Produces structured, descriptive outputs suitable for annotation and dataset building.

• Dynamic Resolution Support Retains native handling of varying image resolutions and aspect ratios.

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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-3B-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-3B-Instruct-Unredacted-MAX",
 torch_dtype="auto",
 device_map="auto"
)

processor = AutoProcessor.from_pretrained(
 "prithivMLmods/Qwen2.5-VL-3B-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)

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Intended Use

• Multimodal AI research and evaluation
• Image captioning and dataset generation pipelines
• Vision-language prototyping and experimentation
• Lightweight deployment in constrained environments
• Development of multimodal applications and tools

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Limitations & Risks

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

• Performance depends on image quality, resolution, and prompt design
• May produce incomplete or inaccurate interpretations in complex scenes
• Requires adequate GPU resources for stable inference
• Output consistency varies with decoding settings and runtime optimization

Base model

Qwen/Qwen2.5-VL-3B-Instruct

(793)

“abliteration” models – experimental. • 6 items • Updated 2 days ago • 1