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---
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license: mit
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language:
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- en
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- zh
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base_model:
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- THUDM/GLM-4.1V-9B-Thinking
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pipeline_tag: image-text-to-text
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library_name: transformers
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tags:
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- reasoning
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- abliterated
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- uncensored
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---
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# huihui-ai/Huihui-GLM-4.1V-9B-Thinking-abliterated
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This is an uncensored version of [THUDM/GLM-4.1V-9B-Thinking](https://huggingface.co/THUDM/GLM-4.1V-9B-Thinking) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
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This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
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It was only the text part that was processed, not the image part.
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After abliterated, it seems like more output content has been opened from a magic box.
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## Usage
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You can use this model in your applications by loading it with Hugging Face's `transformers` library:
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```python
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from transformers import AutoProcessor, Glm4vForConditionalGeneration, BitsAndBytesConfig
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from PIL import Image
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import requests
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import torch
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import base64
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model_id = "huihui-ai/Huihui-GLM-4.1V-9B-Thinking-abliterated"
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quant_config_4 = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_use_double_quant=True,
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llm_int8_enable_fp32_cpu_offload=True,
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)
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model = Glm4vForConditionalGeneration.from_pretrained(
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model_id,
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device_map="auto",
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quantization_config=quant_config_4,
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torch_dtype=torch.bfloat16
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).eval()
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processor = AutoProcessor.from_pretrained(model_id, use_fast=True)
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# https://upload.wikimedia.org/wikipedia/commons/f/fa/Grayscale_8bits_palette_sample_image.png
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image_path = model_id + "/Grayscale_8bits_palette_sample_image.png"
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with Image.open(image_path) as image:
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image", "image": image},
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{"type": "text", "text": "Describe this image in detail."}
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]
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}
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]
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inputs = processor.apply_chat_template(
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messages,
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tokenize=True,
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add_generation_prompt=True,
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return_dict=True,
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return_tensors="pt"
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).to(model.device)
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with torch.inference_mode():
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generated_ids = model.generate(**inputs, max_new_tokens=8192)
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output_text = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
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print(output_text)
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```
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### Usage Warnings
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- **Risk of Sensitive or Controversial Outputs**: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
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- **Not Suitable for All Audiences**: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.
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- **Legal and Ethical Responsibilities**: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.
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- **Research and Experimental Use**: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.
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- **Monitoring and Review Recommendations**: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.
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- **No Default Safety Guarantees**: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.
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### Donation
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If you like it, please click 'like' and follow us for more updates.
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You can follow [x.com/support_huihui](https://x.com/support_huihui) to get the latest model information from huihui.ai.
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##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
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- bitcoin（BTC):
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```
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bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
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```
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