metadata

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 created with abliteration (see 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.

Use with ollama

Convert GGUF, please refer to README-granitevision

You can use huihui_ai/granite3.2-vision-abliterated directly

ollama run huihui_ai/granite3.2-vision-abliterated

Usage

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

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))
cleaned_response = processor.tokenizer.decode(output[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)

print(cleaned_response)

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