Huihui-Qwen3.5-9B-abliterated-AWQ
Model Highlights
This model Huihui-Qwen3.5-9B-abliterated-AWQ was converted to AWQ format, from huihui-ai/Huihui-Qwen3.5-9B-abliterated using llm-compressor version 0.10.0.1 (https://github.com/vllm-project/llm-compressor.git). With using dataset wikitext from Salesforce/wikitext & COCO-Caption2017 from lmms-lab/COCO-Caption2017
Datasets:
- Salesforce/wikitext
- lmms-lab/COCO-Caption2017
Base Model:
- huihui-ai/Huihui-Qwen3.5-9B-abliterated
Use with VLLM
- Download models at first by using hf
hf download nicklas373/Huihui-Qwen3.5-9B-abliterated-AWQ - Copy hash for snapshots directory, then use it for chat templates and tool call parser ex: /home/xxx/.cache/huggingface/hub/models--nicklas373--Huihui-Qwen3.5-9B-abliterated-AWQ/snapshots/HASH_CODE/xxx
- Run models with this command
vllm serve nicklas373/Huihui-Qwen3.5-9B-abliterated-AWQ \
--chat-template '/home/xxx/.cache/huggingface/hub/models--nicklas373--Huihui-Qwen3.5-9B-abliterated-AWQ/snapshots/HASH_CODE/chat_template.jinja' \
--chat-template-content-format openai \
--disable-fastapi-docs \
--dtype auto \
--enable-auto-tool-choice \
--enable-prefix-caching \
--enable-sleep-mode \
--reasoning-parser qwen3 \
--served-model-name Huihui-Qwen3.5-9B-abliterated-AWQ \
--seed 0 \
--speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":1}' \
--quantization compressed-tensors \
--tokenizer 'Qwen/Qwen3.5-9B' \
--tool-call-parser qwen3_coder \
--trust-remote-code
huihui-ai/Huihui-Qwen3.5-9B-abliterated
This is an uncensored version of Qwen/Qwen3.5-9B 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.
ollama
Please use the latest version of ollama v0.17.7
You can use huihui_ai/qwen3.5-abliterated:9b directly,
ollama run huihui_ai/qwen3.5-abliterated:9b
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.
Donation
Your donation helps us continue our further development and improvement, a cup of coffee can do it.
- bitcoin:
bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
- Support our work on Ko-fi!