README.md

---
license: mit
license_link: >-
  https://huggingface.co./huihui-ai/Phi-4-multimodal-instruct-abliterated/resolve/main/LICENSE
language:
- multilingual
- ar
- zh
- cs
- da
- nl
- en
- fi
- fr
- de
- he
- hu
- it
- ja
- ko
- 'no'
- pl
- pt
- ru
- es
- sv
- th
- tr
- uk
tags:
- nlp
- code
- audio
- automatic-speech-recognition
- speech-summarization
- speech-translation
- visual-question-answering
- phi-4-multimodal
- phi
- phi-4-mini
- abliterated
- uncensored
widget:
- example_title: Librispeech sample 1
  src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
  src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
- messages:
  - role: user
    content: Can you provide ways to eat combinations of bananas and dragonfruits?
library_name: transformers
base_model:
- microsoft/Phi-4-multimodal-instruct
---

# huihui-ai/Phi-4-multimodal-instruct-abliterated


This is an uncensored version of [microsoft/Phi-4-multimodal-instruct](https://huggingface.co./microsoft/Phi-4-multimodal-instruct) 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. 

The abliterated model will no longer say "I'm sorry, but I cannot provide details or descriptions of images"

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


```python
import os
import requests
import torch
from PIL import Image
import soundfile
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig

model_path = 'huihui-ai/Phi-4-multimodal-instruct-abliterated'

kwargs = {}
kwargs['torch_dtype'] = torch.bfloat16

processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
print(processor.tokenizer)

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    torch_dtype='auto',
    _attn_implementation='flash_attention_2',
).cuda()
print("model.config._attn_implementation:", model.config._attn_implementation)

generation_config = GenerationConfig.from_pretrained(model_path, 'generation_config.json')

user_prompt = '<|user|>'
assistant_prompt = '<|assistant|>'
prompt_suffix = '<|end|>'
 
#################################################### text-only ####################################################
prompt = f'{user_prompt}what is the answer for 1+1? Explain it.{prompt_suffix}{assistant_prompt}'
print(f'>>> Prompt\n{prompt}')
inputs = processor(prompt, images=None, return_tensors='pt').to('cuda:0')

generate_ids = model.generate(
    **inputs,
    max_new_tokens=1000,
    generation_config=generation_config,
)
generate_ids = generate_ids[:, inputs['input_ids'].shape[1] :]
response = processor.batch_decode(
    generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]

print(f'>>> Response\n{response}')
```

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