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

This is an uncensored version of Qwen/Qwen2.5-VL-3B-Instruct created with abliteration (see 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 directly,

ollama run huihui_ai/qwen2.5-vl-abliterated:3b

GGUF

The official llama.cpp-b6907 has now been updated to support Qwen2.5-VL conversion to GGUF format and can be tested using llama-mtmd-cli.

The 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:

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)

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