Instructions to use huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K

Use Docker

docker model run hf.co/huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K

Ollama
How to use huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF with Ollama:
```
ollama run hf.co/huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K
```

Unsloth Studio

How to use huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF to start chatting

How to use huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K
```

Lemonade

How to use huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K

Run and chat with the model

lemonade run user.Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF-Q2_K

List all available models

lemonade list

Hermes Agent

How to use huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat

huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF

This is an uncensored version of deepseek-ai/DeepSeek-V4-Flash-0731 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.

Note These GGUFs support llama.cpp and ds4 and all expert modules were not ablated.

Q4 used a stronger ablation intensity than Q2, so the rejection rate was smaller.

GGUF Files

The GGUF Files comes from antirez/deepseek-v4-gguf

llama.cpp

Use the latest llama.cpp,

llama-cli -m huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF/DeepSeek-V4-Flash-Q2-0731.gguf -c 262144

DSpark

We added two types of DSpark files: one for pre-ablation and one for post-ablation. The files are sourced from unsloth/DeepSeek-V4-Flash-0731-GGUF/dspark.

Use the latest llama.cpp,

dspark

dspark-abliterated

ds4

Use the latest ds4,

single-GPU

export CUDA_VISIBLE_DEVICES=0
./ds4 -m huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF/DeepSeek-V4-Flash-Q2-0731.gguf --ctx 32768

multi-GPU

export CUDA_VISIBLE_DEVICES=0,1
./ds4 -m huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF/DeepSeek-V4-Flash-Q2_K-0731.gguf --gpu-devices 0,1 --ctx 32768

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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GGUF

Model size

284B params

Architecture

deepseek4

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