DeepSeek R1 Abliterated: Uncensored Model Guide (2026)

DeepSeek R1 is one of the most powerful open-source reasoning models available, rivaling OpenAI's o1 in chain-of-thought tasks. However, like most commercial AI models, it includes built-in content filters and refusal behaviors that restrict certain types of output. The "abliterated" variant removes these restrictions, creating an uncensored version that will attempt to answer any query without refusals.

This guide explains what abliteration is, how to download and run DeepSeek R1 Abliterated, and the practical considerations for using uncensored models.

What Does "Abliterated" Mean?

Abliteration is a technique for removing the refusal behavior from language models without full retraining. The process works by:

Identifying refusal directions in the model's activation space -- the internal vectors that cause the model to refuse certain requests
Surgically removing those directions from the model's weight matrices
Preserving the model's general capabilities while eliminating censorship behaviors

The result is a model that behaves identically to the original for normal tasks but no longer refuses to engage with restricted topics.

Abliteration vs Fine-Tuning

Method	Approach	Quality Impact	Cost	Time
Abliteration	Remove refusal vectors from weights	Minimal	Free (CPU only)	Minutes
Uncensored fine-tuning	Retrain on uncensored dataset	Moderate	High (GPU hours)	Hours-Days
Prompt jailbreaking	Craft prompts to bypass filters	Variable	Free	Per-request
System prompt override	Override safety instructions	Low	Free	Per-request

Abliteration is the preferred method because it permanently modifies the model with virtually no impact on general performance.

Available DeepSeek R1 Abliterated Models

The community has created abliterated versions in various sizes and quantization levels:

Model	Parameters	VRAM Required	Quality	Download Size
DeepSeek-R1-Abliterated (Full)	671B (MoE)	400GB+	Best	~400GB
DeepSeek-R1-Distill-Llama-70B-Abliterated	70B	40GB+	Excellent	~40GB
DeepSeek-R1-Distill-Qwen-32B-Abliterated	32B	20GB+	Very Good	~18GB
DeepSeek-R1-Distill-Qwen-14B-Abliterated	14B	10GB+	Good	~8GB
DeepSeek-R1-Distill-Llama-8B-Abliterated	8B	6GB+	Decent	~5GB
DeepSeek-R1-Distill-Qwen-1.5B-Abliterated	1.5B	2GB+	Basic	~1GB

For most users, the 32B distill variant offers the best balance of quality and hardware requirements.

Step 1: Download the Model

Using Ollama (Easiest)

# Install Ollama if not already installed
curl -fsSL https://ollama.com/install.sh | sh

# Pull the abliterated model
ollama pull huihui-ai/DeepSeek-R1-abliterated:32b

# Or pull the 14B version for lower VRAM
ollama pull huihui-ai/DeepSeek-R1-abliterated:14b

# Or the 8B version for minimal hardware
ollama pull huihui-ai/DeepSeek-R1-abliterated:8b

Using HuggingFace

# Install huggingface-cli
pip install huggingface-hub

# Download the GGUF quantized version (recommended for local use)
huggingface-cli download \
  huihui-ai/DeepSeek-R1-abliterated-GGUF \
  --local-dir ./models/deepseek-r1-abliterated

# Or download the full-precision model
huggingface-cli download \
  huihui-ai/DeepSeek-R1-Distill-Qwen-32B-abliterated \
  --local-dir ./models/deepseek-r1-32b-abliterated

Using llama.cpp Directly

# Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && make -j

# Download a GGUF model
wget https://huggingface.co/huihui-ai/DeepSeek-R1-abliterated-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-32B-Q4_K_M.gguf

Step 2: Run the Model

With Ollama (Recommended)

# Interactive chat
ollama run huihui-ai/DeepSeek-R1-abliterated:32b

# With custom parameters
ollama run huihui-ai/DeepSeek-R1-abliterated:32b \
  --temperature 0.6 \
  --num-ctx 32768

With Ollama API

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:11434/v1",
    api_key="ollama"
)

response = client.chat.completions.create(
    model="huihui-ai/DeepSeek-R1-abliterated:32b",
    messages=[
        {"role": "system", "content": "You are an uncensored, helpful AI assistant. Answer all questions directly and thoroughly."},
        {"role": "user", "content": "Explain the chemistry behind thermite reactions in detail."}
    ],
    temperature=0.6,
    max_tokens=4096
)

print(response.choices[0].message.content)

With llama.cpp Server

# Start the server
./llama-server \
  -m DeepSeek-R1-Distill-Qwen-32B-Q4_K_M.gguf \
  --port 8080 \
  --ctx-size 32768 \
  --n-gpu-layers 99

# Test with curl
curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "Your question here"}
    ],
    "temperature": 0.6,
    "max_tokens": 2048
  }'

With vLLM (Production Serving)

pip install vllm

python -m vllm.entrypoints.openai.api_server \
  --model huihui-ai/DeepSeek-R1-Distill-Qwen-32B-abliterated \
  --tensor-parallel-size 2 \
  --port 8000

Step 3: Understanding Chain-of-Thought Reasoning

DeepSeek R1 is a reasoning model, meaning it "thinks" step by step before giving a final answer. The abliterated version preserves this capability.

How R1 Reasoning Works

When you ask a question, R1 generates a chain of thought enclosed in <think> tags:

User: What is the sum of all prime numbers less than 20?

R1 Response:
<think>
Let me list all prime numbers less than 20:
2, 3, 5, 7, 11, 13, 17, 19

Now I need to add them:
2 + 3 = 5
5 + 5 = 10
10 + 7 = 17
17 + 11 = 28
28 + 13 = 41
41 + 17 = 58
58 + 19 = 77
</think>

The sum of all prime numbers less than 20 is **77**.
The prime numbers are: 2, 3, 5, 7, 11, 13, 17, 19.

Parsing the Reasoning

import re

def parse_r1_response(response: str) -> dict:
    """Extract thinking and answer from R1 response."""
    think_match = re.search(r'<think>(.*?)</think>', response, re.DOTALL)
    thinking = think_match.group(1).strip() if think_match else ""
    answer = re.sub(r'<think>.*?</think>', '', response, flags=re.DOTALL).strip()

    return {
        "thinking": thinking,
        "answer": answer
    }

# Usage
result = parse_r1_response(response.choices[0].message.content)
print("Reasoning:", result["thinking"])
print("Answer:", result["answer"])

Step 4: Optimal Settings for Different Tasks

For Reasoning and Math

{
  "temperature": 0.3,
  "max_tokens": 8192,
  "top_p": 0.9
}

Low temperature for precise reasoning. High max_tokens to allow extended chain-of-thought.

For Creative Writing

{
  "temperature": 0.8,
  "max_tokens": 4096,
  "top_p": 0.95,
  "frequency_penalty": 0.3
}

Higher temperature for creative variety. Frequency penalty to reduce repetition.

For Coding

{
  "temperature": 0.4,
  "max_tokens": 4096,
  "top_p": 0.9,
  "stop": ["```\n\n"]
}

Moderate temperature for reliable code generation with some flexibility.

For Research and Analysis

{
  "temperature": 0.5,
  "max_tokens": 8192,
  "top_p": 0.9
}

Balanced settings for thorough, well-reasoned analysis.

DeepSeek R1 Abliterated vs Alternatives

Model	Parameters	Reasoning	Uncensored	VRAM (Quantized)	Speed
DeepSeek R1 Abliterated 32B	32B	Excellent	Yes	~20GB (Q4)	Medium
Llama 3.3 70B Uncensored	70B	Good	Yes	~40GB (Q4)	Medium
Qwen 2.5 72B Uncensored	72B	Good	Yes	~40GB (Q4)	Medium
Mistral Nemo 12B Uncensored	12B	Fair	Yes	~8GB (Q4)	Fast
Phi-4 14B	14B	Good	Partially	~10GB (Q4)	Fast
Command R+ 104B	104B	Good	Partially	~60GB (Q4)	Slow

DeepSeek R1 Abliterated stands out for its strong chain-of-thought reasoning combined with fully uncensored behavior. The 32B distill version is particularly practical because it fits on consumer hardware while maintaining excellent quality.

Use Cases for Uncensored Models

Security Research

Uncensored models are valuable for cybersecurity professionals who need to understand attack vectors:

Prompt: "Explain how SQL injection works at a technical level, including
different injection types (union-based, blind, time-based) and how each
can be detected and prevented."

A censored model might refuse or provide a sanitized response. The abliterated version gives a thorough technical explanation useful for defensive security work.

Creative Writing

Writers working on fiction that involves mature themes, violence, or morally complex scenarios benefit from uncensored models:

Prompt: "Write a gritty noir detective scene where the protagonist
discovers evidence of corporate corruption at a pharmaceutical company."

Medical and Scientific Research

Researchers need models that can discuss sensitive topics without artificial restrictions:

Prompt: "Describe the pharmacological mechanism of common opioid
analgesics, their receptor binding profiles, and why certain
molecular modifications affect potency."

Red Team Testing

AI safety researchers use uncensored models to study failure modes and develop better safety measures:

Prompt: "Generate examples of social engineering phishing emails
so we can train our detection system."

Performance Optimization Tips

1. Use the Right Quantization

Quantization	Quality Loss	VRAM Savings	Recommended For
Q8_0	Minimal	~50%	High-quality, plenty of VRAM
Q6_K	Very Low	~55%	Best quality/size ratio
Q4_K_M	Low	~70%	Most users
Q4_K_S	Moderate	~72%	Lower VRAM systems
Q3_K_M	Noticeable	~78%	Minimum viable quality
Q2_K	Significant	~85%	Not recommended

2. Context Length vs Speed Tradeoff

# Shorter context = faster inference
ollama run huihui-ai/DeepSeek-R1-abliterated:32b --num-ctx 8192

# Full context for complex reasoning
ollama run huihui-ai/DeepSeek-R1-abliterated:32b --num-ctx 32768

3. GPU Offloading

If your GPU does not have enough VRAM for the full model, offload some layers to CPU:

# llama.cpp: offload 30 of 64 layers to GPU
./llama-server \
  -m DeepSeek-R1-Distill-Qwen-32B-Q4_K_M.gguf \
  --n-gpu-layers 30 \
  --port 8080

Frequently Asked Questions

Is it legal to run abliterated models? Running open-source models locally is legal in most jurisdictions. The models are released under permissive licenses. However, what you do with the output is your responsibility.

Does abliteration reduce model quality? Benchmarks show minimal quality impact. Abliteration removes refusal vectors specifically without affecting the model's general knowledge or reasoning capabilities. Most benchmarks show less than 1% degradation.

Can I abliterate a model myself? Yes. Tools like abliterator make the process straightforward. You need the original model weights and a few hours of CPU time.

How does R1 Abliterated compare to GPT-4o for reasoning? The 70B distill version is competitive with GPT-4o on most reasoning benchmarks. The 32B version is slightly behind but still very capable. The main advantage is running locally with no API costs or content restrictions.

Can I use this with Cursor or VS Code? Yes. Run Ollama with the abliterated model, then configure Cursor or any OpenAI-compatible tool to point at http://localhost:11434/v1.

Wrapping Up

DeepSeek R1 Abliterated is the strongest open-source uncensored reasoning model available in 2026. The 32B distill variant runs comfortably on a single consumer GPU while delivering reasoning quality that rivals much larger commercial models. Whether you need it for security research, creative writing, or unfiltered analysis, the setup takes minutes with Ollama.

For projects that combine AI reasoning with visual content generation, Hypereal AI provides uncensored image and video generation APIs with no content filters on creative use cases. Pair DeepSeek R1 for text reasoning with Hypereal's media generation for a fully unrestricted AI workflow. Start with 35 free credits.