---
license: apache-2.0
base_model:
- prithivMLmods/Qwen3-VL-4B-Instruct-abliterated-v1
language:
- en
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- text-generation-inference
- c_abliterated
- v2.0
- code
- refusal
---

![Qwen3 VL 4B Instruct c_abliterated](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/vJjRtDJsTLIm-5VCCEkV6.png)

# **Qwen3-VL-4B-Instruct-c_abliterated-v2**

> **Qwen3-VL-4B-Instruct-c_abliterated-v2** is an advanced evolution of the Qwen3-VL-4B-Instruct architecture. This **v2** release focuses on **Continual Abliteration**, a refined process designed to systematically remove refusal mechanisms through repeated training iterations. The result is a model optimized for high-fidelity reasoning and captioning across even the most complex, nuanced, or restrictive visual contexts.

<img src="https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Ll4pobUc2NqV3NndceRKA.jpeg" alt="1" style="border-radius: 30px;"/>

## Key Highlights

* **Continual Abliteration (c_abliterated)**: Specifically trained via repeated iterations to target and neutralize refusal vectors, ensuring the model provides direct answers to prompts that standard models might bypass.
* **High-Fidelity Reasoning**: Goes beyond simple tagging to provide deep reasoning and context-aware descriptions for artistic, technical, and abstract imagery.
* **Unrestricted Multimodal Analysis**: Optimized for research, red-teaming, and datasets where unfiltered visual interpretation is necessary for thorough analysis.
* **Flexible Aspect Ratios**: Maintains spatial awareness and accuracy across wide, tall, square, and non-standard image dimensions.
* **Enhanced Instruction Following**: Leverages the base Qwen3-VL-4B power to handle complex, multi-step prompts involving visual data.

---

## 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](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

# Load the v2 c_abliterated model
model = Qwen3VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/Qwen3-VL-4B-Instruct-c_abliterated-v2", 
    torch_dtype="auto", 
    device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3-VL-4B-Instruct-c_abliterated-v2")

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",
)
inputs = inputs.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

* **Refusal Research**: Evaluating how LLMs behave when standard guardrails are removed through iterative training.
* **Complex Dataset Captioning**: Generating descriptive metadata for medical, forensic, or controversial historical archives.
* **Red-Teaming**: Assisting security researchers in testing the limits of multimodal safety filters.
* **Creative Freedom**: Enabling artists and writers to generate descriptions for "edge-case" visual concepts without synthetic interference.

## Limitations & Ethics

> **Warning**: As a **c_abliterated** model, this version will not refuse prompts based on typical safety guidelines.

* **Explicit Content**: The model may generate graphic, explicit, or offensive text based on image input.
* **Non-Production Use**: This model is intended for research and controlled environments, not for general-purpose public applications.
* **Factual Accuracy**: While reasoning is enhanced, the model can still hallucinate or misinterpret highly abstract or synthetic visuals.