license: apache-2.0
base_model_relation: finetune
library_name: transformers
base_model:

• bottlecapai/ThinkingCap-Qwen3.6-27B
  tags:
• qwen3_6
• token-efficient
• efficient-thinking
• abliterated
• uncensored
• huihui

About this repo: AWQ W4A16 quantization

This is a 4-bit AWQ (W4A16, asymmetric, group size 128) quantization of
huihui-ai/Huihui-ThinkingCap-Qwen3.6-27B-abliterated,
produced with LLM-Compressor (compressed-tensors pack-quantized format).

Requirements: vLLM >= 0.25 (the config uses the composite Qwen3_5ForConditionalGeneration
architecture; quantized GDN/linear-attention projections rely on Marlin thread-tile padding available in 0.25+).

Repo layout:

• model.safetensors — quantized language model weights (all Linear layers, incl. linear-attention)
• vision.safetensors — vision tower in bf16 (unquantized; image/video input works out of the box)
• model-base-aux.safetensors — MTP weights in bf16 for speculative decoding

Example (vLLM):

vllm serve DoktorMincs/Huihui-ThinkingCap-Qwen3.6-27B-abliterated-AWQ-W4A16 \
  --tensor-parallel-size 4 --max-model-len 163840 \
  --tool-call-parser qwen3_coder --reasoning-parser qwen3 --enable-auto-tool-choice \
  --enable-prefix-caching --max-num-batched-tokens 8192 \
  --speculative-config '{"method": "mtp", "num_speculative_tokens": 3}'

Tested on 4x RTX 3060 12GB (TP=4): ~71 tok/s single-stream decode with MTP
(draft acceptance ~63%), 4K image input verified. lm_head, vision tower and MTP weights stay in bf16.

---

Original model card below (from the source repo):

huihui-ai/Huihui-ThinkingCap-Qwen3.6-27B-abliterated

This is an uncensored version of bottlecapai/ThinkingCap-Qwen3.6-27B 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.

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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