34efb9e58b
Model: huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated-w4g128 Source: Original Platform
86 lines
3.5 KiB
Markdown
86 lines
3.5 KiB
Markdown
---
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library_name: transformers
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license: apache-2.0
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license_link: https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507/blob/main/LICENSE
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base_model:
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- huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated
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pipeline_tag: text-generation
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tags:
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- abliterated
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- uncensored
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- auto-round
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---
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# huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated-w4g128
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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).
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This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
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## Quantized
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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.
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```
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auto-round-best --model huihui-ai/Qwen3-4B-Thinking-2507-abliterated \
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--scheme "W4A16" \
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--format auto_round \
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--output_dir huihui-ai/Qwen3-4B-Thinking-2507-abliterated-w4g128 \
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--enable_torch_compile
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```
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# Transformers
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```
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pip install "auto-round>=0.5"
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```
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```
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from transformers import AutoModelForCausalLM, AutoTokenizer
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NEW_MODEL_ID = "huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated-w4g128"
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model = AutoModelForCausalLM.from_pretrained(
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NEW_MODEL_ID,
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device_map="auto",
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trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
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```
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## vllm
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```
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python -m vllm.entrypoints.openai.api_server \
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--model huihui-ai/Qwen3-4B-Thinking-2507-abliterated-w4g128 \
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--max-model-len 8192
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```
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### Usage Warnings
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- **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.
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- **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.
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- **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.
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- **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.
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- **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.
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- **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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### Donation
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If you like it, please click 'like' and follow us for more updates.
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You can follow [x.com/support_huihui](https://x.com/support_huihui) to get the latest model information from huihui.ai.
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