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Phi-4-multimodal-instruct-abliterated

Automatic Speech Recognition
Transformers
Safetensors
phi4mm
text-generation
nlp
code
audio
speech-summarization
speech-translation
visual-question-answering
phi-4-multimodal
phi
phi-4-mini
abliterated
uncensored
custom_code
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Phi-4-multimodal-instruct-abliterated / README.md
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metadata
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 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.

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:

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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