Instructions to use richardyoung/Deepseek-R1-Distill-Qwen-32b-uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use richardyoung/Deepseek-R1-Distill-Qwen-32b-uncensored with Transformers:

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

pipe = pipeline("text-generation", model="richardyoung/Deepseek-R1-Distill-Qwen-32b-uncensored")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("richardyoung/Deepseek-R1-Distill-Qwen-32b-uncensored")
model = AutoModelForCausalLM.from_pretrained("richardyoung/Deepseek-R1-Distill-Qwen-32b-uncensored")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use richardyoung/Deepseek-R1-Distill-Qwen-32b-uncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "richardyoung/Deepseek-R1-Distill-Qwen-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": "richardyoung/Deepseek-R1-Distill-Qwen-32b-uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/richardyoung/Deepseek-R1-Distill-Qwen-32b-uncensored

SGLang

How to use richardyoung/Deepseek-R1-Distill-Qwen-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 "richardyoung/Deepseek-R1-Distill-Qwen-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": "richardyoung/Deepseek-R1-Distill-Qwen-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 "richardyoung/Deepseek-R1-Distill-Qwen-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": "richardyoung/Deepseek-R1-Distill-Qwen-32b-uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use richardyoung/Deepseek-R1-Distill-Qwen-32b-uncensored with Docker Model Runner:
```
docker model run hf.co/richardyoung/Deepseek-R1-Distill-Qwen-32b-uncensored
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

DeepSeek-R1-Distill-Qwen-32B Uncensored

An abliterated (uncensored) version of deepseek-ai/DeepSeek-R1-Distill-Qwen-32B — a 32B reasoning model with chain-of-thought capabilities, minus the safety refusals.

This combines DeepSeek-R1's strong reasoning with unrestricted output, making it useful for research requiring step-by-step analysis without artificial limitations.

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "richardyoung/Deepseek-R1-Distill-Qwen-32b-uncensored"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [{"role": "user", "content": "Walk me through how RSA encryption works, step by step."}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Model Details

Base model: DeepSeek-R1-Distill-Qwen-32B (32 billion parameters)
Technique: Abliteration — surgical removal of the refusal direction
Architecture: Qwen2 (decoder-only transformer)
Context length: 32,768 tokens
Key strength: Chain-of-thought reasoning without safety guardrails

Why This Model?

DeepSeek-R1 is one of the strongest open-source reasoning models. The distilled 32B version retains impressive chain-of-thought capabilities at a manageable size. Abliteration allows researchers to study the full range of the model's reasoning abilities without refusal interventions.