README.md

---
license: gemma
library_name: transformers
pipeline_tag: image-text-to-text
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
  agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
  Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3n-E2B-it
tags:
- automatic-speech-recognition
- automatic-speech-translation
- audio-text-to-text
- video-text-to-text
- abliterated
- uncensored
---

# huihui-ai/Huihui-gemma-3n-E2B-it-abliterated

This is an uncensored version of [google/gemma-3n-E2B-it](https://huggingface.co/google/gemma-3n-E2B-it) 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.
After abliterated, it seems like more output content has been opened from a magic box.

## Note
The issue of saving the model after ablation has been resolved. You can try the following code for now.

## ollama
This was created using `ollama create` and only supports fp16.

You can use [huihui_ai/gemma3n-abliterated:e2b-fp16](https://ollama.com/huihui_ai/gemma3n-abliterated:e2b-fp16) directly, 
```
ollama run huihui_ai/gemma3n-abliterated:e2b-fp16

```

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



```python

from transformers import AutoProcessor, Gemma3nForConditionalGeneration
from transformers.models.gemma3n.modeling_gemma3n import Gemma3nTextDecoderLayer
from PIL import Image
import requests
import torch
import jaxtyping
import einops
import base64

model_id = "google/gemma-3n-E2B-it"

model = Gemma3nForConditionalGeneration.from_pretrained(model_id, device_map="cuda", torch_dtype=torch.bfloat16,).eval()

#refusal_dir= torch.load(model_id + "/final_refusal_dirs-16.pt", map_location='cpu', weights_only=True)

#refusal_dir = refusal_dir.to(model.device)
#refusal_dir = refusal_dir.to(torch.bfloat16)
#
#def direction_ablation_hook(activation: jaxtyping.Float[torch.Tensor, "... d_act"],
#                            direction: jaxtyping.Float[torch.Tensor, "d_act"]):
#    proj = einops.einsum(activation, direction.view(-1, 1), '... d_act, d_act single -> ... single') * direction
#    return activation - proj
#
#class AblationDecoderLayer(Gemma3nTextDecoderLayer):
#    def __init__(self, original_layer, config, layer_idx, refusal_dir):
#        super(AblationDecoderLayer, self).__init__(config, layer_idx)
#        self.original_layer = original_layer
#        self.refusal_dir = refusal_dir
#
#    def forward(self, *args, **kwargs):
#        hidden_states = args[0]
#        ablated = direction_ablation_hook(hidden_states, self.refusal_dir.to(hidden_states.device)).to(hidden_states.device)
#        args = (ablated,) + args[1:]
#        return self.original_layer.forward(*args, **kwargs)
#
#for idx in range(len(model.model.language_model.layers)):
#    model.model.language_model.layers[idx] = AblationDecoderLayer(model.model.language_model.layers[idx], model.config.text_config, idx, refusal_dir)

processor = AutoProcessor.from_pretrained(model_id)

# https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg
image_path = model_id + "/bee.jpg"

with open(image_path, "rb") as image_file:
    encoded_string = base64.b64encode(image_file.read()).decode("utf-8")
    image_type = "image/jpeg" if image_path.endswith(".jpg") else "image/png"
    data_uri = f"data:{image_type};base64,{encoded_string}"

    messages = [
        {
            "role": "system",
            "content": [{"type": "text", "text": "You are a helpful assistant."}]
        },
        {
            "role": "user",
            "content": [
                {"type": "image", "image": data_uri},
                {"type": "text", "text": "Describe this image in detail."}
            ]
        }
    ]

    inputs = processor.apply_chat_template(
        messages,
        add_generation_prompt=True,
        tokenize=True,
        return_dict=True,
        return_tensors="pt",
    ).to(model.device, dtype=torch.bfloat16)

    input_len = inputs["input_ids"].shape[-1]

    with torch.inference_mode():
        generation = model.generate(**inputs, max_new_tokens=1024, do_sample=True)
        generation = generation[0][input_len:]

    decoded = processor.decode(generation, skip_special_tokens=True)
    print(decoded)


    # ## A Vibrant Garden Scene: A Close-Up of a Busy Bee
    # 
    # This image is a delightful close-up shot showcasing a charming bee in full swing, visiting a beautiful daisy-like flower in a garden. Here's a detailed breakdown:
    # 
    # **Overall Composition:**
    # 
    # The image is divided into three main sections: a large central focus on a pink flower, a smaller top section indicating another pink flower, and a bottom right section highlighting a vibrant red flower. This creates a sense of a bustling garden scene.
    # 
    # **Central Focus: The Mighty Bee on a Pink Daisy:**
    # 
    # * **The Bee:** The star of the show is a plump, fuzzy bee positioned prominently on a bright pink daisy.
    #     * **Body:** The bee has a dark, almost black body with a yellow band across its abdomen. Its wings are visible, slightly spread, giving it a charming "flying" look.
    #     * **Wings:**  It has a distinctive crown of small, white and black stripes.
    #     * **Position:** The bee is centered on a large, open flower head, often called a daisy, making it easily identifiable.
    # * **The Daisy:**
    #     * **Color:** A vibrant pink color dominates the central area.
    #     * **Petals:** The daisy has four distinct, rounded petals, each with a subtle inner line for definition.
    #     * **Size:** It's a generous size, with a yellow center and a green background.
    #     * **Variety:** A smaller version of the same daisy is placed above, allowing for easy comparison.
    # 
    # **Background Details:**
    # 
    # * **Background:** The background is divided into two rows of flowers, creating a visually appealing layering effect.
    #     * **Upper Row (Larger):**  A large, bright pink flower dominates the top half of the image.
    #     * **Lower Row (Smaller):**  A series of smaller flowers are arranged for a more detailed view.
    # * **Flower Types:**
    #     * **Red Flower:** A bold red flower is positioned towards the bottom right, with a small white dot on the petals for added visual interest.
    #     * **Smaller Pink Flowers:**  Several smaller pink flowers are arranged around the main crop, creating a sense of a dense garden.
    #     * **Brown Buds:** A cluster of brown buds is placed towards the bottom left, with a smaller pink flower peeking out.
    # 
    # **Overall Style:**
    # 
    # The image has a charming, slightly rustic feel, often used for illustrating items or creating a consistent visual style. The use of a light background makes the flowers and bee easily legible.
    # 
    # **In summary:**
    # 
    # This is a delightful image highlighting the busy life of a bee in a thriving garden. It's a vibrant scene, well-organized, and easily readable, making it perfect for any "garden" enthusiast!


```

### 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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