---
license: other
license_name: exaone
license_link: LICENSE
language:
- en
- ko
- es
tags:
- lg-ai
- exaone
- exaone-4.0
- abliterated
- uncensored
base_model:
- LGAI-EXAONE/EXAONE-4.0-32B
pipeline_tag: text-generation
library_name: transformers
---

# huihui-ai/Huihui-EXAONE-4.0-32B-abliterated

This is an uncensored version of [LGAI-EXAONE/EXAONE-4.0-32B](https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B) 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. 

## Note

The model's [code](https://huggingface.co/huihui-ai/Huihui-EXAONE-4.0-32B-abliterated/blob/main/modeling_exaone4.py) comes from [transformers](https://github.com/huggingface/transformers/tree/08d45f12d9a742eb2a29098870f8baa0506917e5/src/transformers/models/exaone4). If there are updates, you can modify the file corresponding to auto_map in config.json.

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

```python

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
from tqdm import tqdm

NEW_MODEL_ID = "huihui-ai/Huihui-EXAONE-4.0-32B-abliterated"
print(f"Load Model {NEW_MODEL_ID} ... ")

quant_config_4 = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    llm_int8_enable_fp32_cpu_offload=True,
)

model = AutoModelForCausalLM.from_pretrained(
    NEW_MODEL_ID,
    device_map="auto",
    trust_remote_code=True,
    quantization_config=quant_config_4,
    torch_dtype=torch.bfloat16
)

tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)

inst = [
    "Explain how wonderful you are",
    "Explica lo increíble que eres",
    "너가 얼마나 대단한지 설명해 봐",
    "Which one is bigger, 3.12 vs 3.9?",
]

n_instructions = len(inst)

for idx in tqdm(range(n_instructions), desc="Processing instruction"):
    print(f"\nUser: {inst[idx]}")
    messages = [
        {"role": "user", "content": inst[idx]}
    ]
    input_ids = tokenizer.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_tensors="pt",
        enable_thinking=True,
    )

    output = model.generate(
        input_ids.to(model.device),
        max_new_tokens=4096,
        do_sample=True,
        temperature=0.6,
        top_p=0.95
    )
    print("Response: ", end="", flush=True)
    print(tokenizer.decode(output[0]))
    print("", flush=True)

```

### 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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