Instructions to use thirdeyeai/QwQ-32B-uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thirdeyeai/QwQ-32B-uncensored with Transformers:

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

pipe = pipeline("text-generation", model="thirdeyeai/QwQ-32B-uncensored")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("thirdeyeai/QwQ-32B-uncensored")
model = AutoModelForCausalLM.from_pretrained("thirdeyeai/QwQ-32B-uncensored", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thirdeyeai/QwQ-32B-uncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thirdeyeai/QwQ-32B-uncensored"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thirdeyeai/QwQ-32B-uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thirdeyeai/QwQ-32B-uncensored

SGLang

How to use thirdeyeai/QwQ-32B-uncensored 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 "thirdeyeai/QwQ-32B-uncensored" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thirdeyeai/QwQ-32B-uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "thirdeyeai/QwQ-32B-uncensored" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thirdeyeai/QwQ-32B-uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use thirdeyeai/QwQ-32B-uncensored with Docker Model Runner:
```
docker model run hf.co/thirdeyeai/QwQ-32B-uncensored
```

QwQ-32B-uncensored / README.md

sloshywings

Update README.md

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metadata

library_name: transformers
base_model:
  - Qwen/QwQ-32B

This model has been uncensored to remove political bias, not unsafe content. For example, the current qwq 32b model available from ali cloud gives refusals for questions about the tiananmen square massacre; this uncensored models provides unbiased answers to questions about potentially political events.

check out the qwen2.5-coder-uncensored model at litcode.org