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 Desktop

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 @earendil-works/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

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

OpenClaw

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"

Weights

by HFMAIC - opened 25 days ago

Discussion

HFMAIC

25 days ago

First of all, thanks for being so fast to release your abliterated version. I've been using it since it's release and it's pretty solid.
However, I just saw some discussions on Reddit by the AtomicChat group on this model:

"By default, converters downconverts FP8 tensors to Q8_0, hard-coded in the file conversion/deepseek.py. This causes the model to deviate from the original weights by 0.219 on average KLD even before quantization begins. Correct these tensors by replacing them with BF16, and the base model became bit-exact"

Would this be something that could be looked into, or is it incompatible with the way you do abliterations?

huihui-ai

Owner 25 days ago

Reddit link?

HFMAIC

24 days ago

Reddit link?

https://www.reddit.com/r/LocalLLaMA/comments/1vlurlv/we_quantized_deepseek_v4_0731_and_benchmarked_it/

NIGGERTor

22 days ago

This comment has been hidden

Vlad100

18 days ago

First of all, thanks for being so fast to release your abliterated version. I've been using it since it's release and it's pretty solid.
However, I just saw some discussions on Reddit by the AtomicChat group on this model:

"By default, converters downconverts FP8 tensors to Q8_0, hard-coded in the file conversion/deepseek.py. This causes the model to deviate from the original weights by 0.219 on average KLD even before quantization begins. Correct these tensors by replacing them with BF16, and the base model became bit-exact"

Would this be something that could be looked into, or is it incompatible with the way you do abliterations?

This model (K2) mines 17+ tokens per second on my hardware, while AtomicChat's model (IQ_2M) mines only 12+ tokens per second. I-quants are too heavy...

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