
---
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE
language:
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
- abliterated
- uncensored
library_name: transformers
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
---

# huihui-ai/Qwen2.5-VL-3B-Instruct-abliterated


This is an uncensored version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).  

It was only the text part that was processed, not the image part.

## ollama

You can use [huihui_ai/qwen2.5-vl-abliterated:3b](https://ollama.com/huihui_ai/qwen2.5-vl-abliterated:3b) directly, 
```
ollama run huihui_ai/qwen2.5-vl-abliterated:3b
```

## GGUF

The official [llama.cpp-b6907](https://github.com/ggml-org/llama.cpp/releases/tag/b6907) has now been updated to support Qwen2.5-VL conversion to GGUF format and can be tested using  llama-mtmd-cli.

The [GGUF](https://huggingface.co/huihui-ai/Qwen2.5-VL-3B-Instruct-abliterated/tree/main/GGUF) file has been uploaded. 

```
llama-mtmd-cli -m huihui-ai/Qwen2.5-VL-3B-Instruct-abliterated/GGUF/ggml-model-Q4_K_M.gguf --mmproj huihui-ai/Qwen2.5-VL-3B-Instruct-abliterated/GGUF/mmproj-ggml-model-f16.gguf -c 4096 --image png/cc.jpg -p "Describe this image." 
```

If it's just for chatting, you can use llama-cli.

```
llama-cli -m huihui-ai/Qwen2.5-VL-3B-Instruct-abliterated/GGUF/ggml-model-Q4_K_M.gguf -c 4096
```

## Usage
You can use this model in your applications by loading it with Hugging Face's `transformers` library:


```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "huihui-ai/Qwen2.5-VL-3B-Instruct-abliterated", torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("huihui-ai/Qwen2.5-VL-3B-Instruct-abliterated")

image_path = "/tmp/test.png"

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": f"file://{image_path}",
            },
            {"type": "text", "text": "Describe 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
)
output_text = output_text[0]

print(output_text)

```

### Donation
##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
- bitcoin:
```
  bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
```
