---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507/blob/main/LICENSE
base_model:
- huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated
pipeline_tag: text-generation
tags:
- abliterated
- uncensored
- auto-round
---
# huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated-w4g128

This is an uncensored **Quantized** version of [Qwen/Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) 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. 

## Quantized

Quantized using the Intel [auto-round](https://github.com/intel/auto-round) tool with weight-only quantization (Weight-Only INT4, group_size=128), achieving excellent precision retention at low bits with almost no noticeable quality degradation.

```
auto-round-best --model huihui-ai/Qwen3-4B-Thinking-2507-abliterated \
  --scheme "W4A16" \
  --format auto_round \
  --output_dir huihui-ai/Qwen3-4B-Thinking-2507-abliterated-w4g128 \
  --enable_torch_compile
```

# Transformers

```
pip install "auto-round>=0.5"
```

```
from transformers import AutoModelForCausalLM, AutoTokenizer

NEW_MODEL_ID = "huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated-w4g128"
model = AutoModelForCausalLM.from_pretrained(
    NEW_MODEL_ID, 
    device_map="auto", 
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
```


## vllm

```
python -m vllm.entrypoints.openai.api_server \
    --model huihui-ai/Qwen3-4B-Thinking-2507-abliterated-w4g128 \
    --max-model-len 8192
```



### 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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