---
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
---

# 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.

## 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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