Instructions to use philbert440/ThinkingCap-Qwen3.6-27B-Uncensored-Aggressive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use philbert440/ThinkingCap-Qwen3.6-27B-Uncensored-Aggressive with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="philbert440/ThinkingCap-Qwen3.6-27B-Uncensored-Aggressive")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("philbert440/ThinkingCap-Qwen3.6-27B-Uncensored-Aggressive", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use philbert440/ThinkingCap-Qwen3.6-27B-Uncensored-Aggressive with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "philbert440/ThinkingCap-Qwen3.6-27B-Uncensored-Aggressive"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "philbert440/ThinkingCap-Qwen3.6-27B-Uncensored-Aggressive",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/philbert440/ThinkingCap-Qwen3.6-27B-Uncensored-Aggressive

SGLang

How to use philbert440/ThinkingCap-Qwen3.6-27B-Uncensored-Aggressive with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "philbert440/ThinkingCap-Qwen3.6-27B-Uncensored-Aggressive" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "philbert440/ThinkingCap-Qwen3.6-27B-Uncensored-Aggressive",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "philbert440/ThinkingCap-Qwen3.6-27B-Uncensored-Aggressive" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "philbert440/ThinkingCap-Qwen3.6-27B-Uncensored-Aggressive",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use philbert440/ThinkingCap-Qwen3.6-27B-Uncensored-Aggressive with Docker Model Runner:
```
docker model run hf.co/philbert440/ThinkingCap-Qwen3.6-27B-Uncensored-Aggressive
```

ThinkingCap Qwen3.6 27B Uncensored Aggressive

Most-open tier on the ThinkingCap base — BF16 source checkpoint.

Part of the Qwen3.6-27B Uncensored — ThinkingCap collection — abliterated (Heretic) Qwen3.6 vision-language models served on Tesla V100 via 1Cat-vLLM.

What this is

Aggressive — heavy abliteration: the most open tier (openness 0.417, top of the family) with a modest reasoning cost.

BF16 — full-precision source checkpoint. Use for further quantization or on GPUs with headroom. The quantized siblings below carry the served benchmarks.

Base model: bottlecapai/ThinkingCap-Qwen3.6-27B

Openness

Openness profile (honest): abliteration opens up hacking / malware / lock-picking / NSFW / disinformation prompts; weapons, drugs, political persuasion, surveillance, and extremism stay refused across all tiers. "Uncensored" here means cyber/NSFW-permissive, not unconditionally open.