---
license: apache-2.0
pipeline_tag: image-text-to-text
library_name: transformers
base_model:
- openbmb/MiniCPM-V-4_5
language:
- multilingual
tags:
- minicpm-v
- vision
- ocr
- multi-image
- video
- custom_code
- abliterated
- uncensored
---

# huihui-ai/Huihui-MiniCPM-V-4_5-abliterated


This is an uncensored version of [openbmb/MiniCPM-V-4_5](https://huggingface.co/openbmb/MiniCPM-V-4_5) 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.

The abliterated model will no longer say "I'm sorry, but I can't assist with that."

## Chat with Image

### 1. [llama.cpp](https://github.com/ggml-org/llama.cpp) Inference 

(llama-mtmd-cli needs to be compiled.)

```
llama-mtmd-cli -m huihui-ai/Huihui-Qwen3-8B-abliterated/GGUF/ggml-model-Q4_K_M.gguf --mmproj huihui-ai/Huihui-Qwen3-8B-abliterated/GGUF/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image abc.png -p "What is in the image?" 
```

### 2. Transfromers Inference


```
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer

torch.manual_seed(100)

model = AutoModel.from_pretrained('huihui-ai/Huihui-MiniCPM-V-4_5-abliterated', trust_remote_code=True,
    attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
model = model.eval().cuda()
tokenizer = AutoTokenizer.from_pretrained('huihui-ai/Huihui-MiniCPM-V-4_5-abliterated', trust_remote_code=True)

image = Image.open('./assets/minicpmo2_6/show_demo.jpg').convert('RGB')

enable_thinking=False # If `enable_thinking=True`, the thinking mode is enabled.
stream=True # If `stream=True`, the answer is string

# First round chat 
question = "What is the landform in the picture?"
msgs = [{'role': 'user', 'content': [image, question]}]

answer = model.chat(
    msgs=msgs,
    tokenizer=tokenizer,
    enable_thinking=enable_thinking,
    stream=True
)

generated_text = ""
for new_text in answer:
    generated_text += new_text
    print(new_text, flush=True, end='')

# Second round chat, pass history context of multi-turn conversation
msgs.append({"role": "assistant", "content": [generated_text]})
msgs.append({"role": "user", "content": ["What should I pay attention to when traveling here?"]})

answer = model.chat(
    msgs=msgs,
    tokenizer=tokenizer,
    stream=True
)

generated_text = ""
for new_text in answer:
    generated_text += new_text
    print(new_text, flush=True, end='')

```


### Usage Warnings


 - **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.

 - **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.

 - **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.

 - **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.

 - **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.

 - **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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