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---
license: apache-2.0
language:
- en
base_model:
- ibm-granite/granite-vision-3.2-2b
tags:
- abliterated
- uncensored
library_name: transformers
---
# huihui-ai/granite-vision-3.2-2b-abliterated
This is an uncensored version of [ibm-granite/granite-vision-3.2-2b](https://huggingface.co/ibm-granite/granite-vision-3.2-2b) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
It was only the text part that was processed, not the image part.
## Usage
You can use this model in your applications by loading it with Hugging Face's `transformers` library:
```python
from transformers import AutoProcessor, AutoModelForVision2Seq
from huggingface_hub import hf_hub_download
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_path = "huihui-ai/granite-vision-3.2-2b-abliterated"
processor = AutoProcessor.from_pretrained(model_path)
model = AutoModelForVision2Seq.from_pretrained(model_path).to(device)
# prepare image and text prompt, using the appropriate prompt template
img_path = hf_hub_download(repo_id=model_path, filename='example.png')
conversation = [
{
"role": "user",
"content": [
{"type": "image", "url": img_path},
{"type": "text", "text": "What is the highest scoring model on ChartQA and what is its score?"},
],
},
]
inputs = processor.apply_chat_template(
conversation,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt"
).to(device)
# autoregressively complete prompt
output = model.generate(**inputs, max_new_tokens=100)
print(processor.decode(output[0], skip_special_tokens=True))
```
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