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

Libraries

How to use bartowski/DeepSeek-V4-Flash-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bartowski/DeepSeek-V4-Flash-GGUF",
	filename="DeepSeek-V4-Flash-MXFP4/DeepSeek-V4-Flash-MXFP4-00001-of-00004.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use bartowski/DeepSeek-V4-Flash-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 bartowski/DeepSeek-V4-Flash-GGUF
# Run inference directly in the terminal:
llama cli -hf bartowski/DeepSeek-V4-Flash-GGUF

Install from WinGet (Windows)

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

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 bartowski/DeepSeek-V4-Flash-GGUF
# Run inference directly in the terminal:
./llama-cli -hf bartowski/DeepSeek-V4-Flash-GGUF

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 bartowski/DeepSeek-V4-Flash-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/DeepSeek-V4-Flash-GGUF

Use Docker

docker model run hf.co/bartowski/DeepSeek-V4-Flash-GGUF

LM Studio
Jan

vLLM

How to use bartowski/DeepSeek-V4-Flash-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/DeepSeek-V4-Flash-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": "bartowski/DeepSeek-V4-Flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bartowski/DeepSeek-V4-Flash-GGUF

Ollama
How to use bartowski/DeepSeek-V4-Flash-GGUF with Ollama:
```
ollama run hf.co/bartowski/DeepSeek-V4-Flash-GGUF
```

Unsloth Studio

How to use bartowski/DeepSeek-V4-Flash-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 bartowski/DeepSeek-V4-Flash-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 bartowski/DeepSeek-V4-Flash-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use bartowski/DeepSeek-V4-Flash-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bartowski/DeepSeek-V4-Flash-GGUF

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": "bartowski/DeepSeek-V4-Flash-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use bartowski/DeepSeek-V4-Flash-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 bartowski/DeepSeek-V4-Flash-GGUF

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 bartowski/DeepSeek-V4-Flash-GGUF

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use bartowski/DeepSeek-V4-Flash-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bartowski/DeepSeek-V4-Flash-GGUF

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 "bartowski/DeepSeek-V4-Flash-GGUF" \
  --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 bartowski/DeepSeek-V4-Flash-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/DeepSeek-V4-Flash-GGUF
```

Lemonade

How to use bartowski/DeepSeek-V4-Flash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/DeepSeek-V4-Flash-GGUF

Run and chat with the model

lemonade run user.DeepSeek-V4-Flash-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

DeepSeek-V4-Flash-GGUF / README.md

bartowski

Update README.md

f9815d7 verified 27 days ago

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metadata

quantized_by: bartowski
pipeline_tag: text-generation
license: mit
base_model_relation: quantized
base_model: deepseek-ai/DeepSeek-V4-Flash

Llamacpp Quantizations of DeepSeek-V4-Flash by deepseek-ai

Using llama.cpp release b9843 for quantization.

Original model: https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash

This model is in MXFP4 and as such has only been provided in MXFP4 format!

No other sizes can be provided unfortunately as MXFP4 does not quantize properly.

Run in your choice of tools:

Note: since it's a newly supported model, you may need to wait for an update from the developers.

Prompt format

No prompt format found

Download the MXFP4 files:

Filename	Quant type	File Size	Split	Description
DeepSeek-V4-Flash-MXFP4.gguf	MXFP4	156.00GB	true	Original quality.

Downloading using huggingface-cli

Click to view download instructions

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

huggingface-cli download bartowski/DeepSeek-V4-Flash-GGUF --include "DeepSeek-V4-Flash-MXFP4*" --local-dir ./

You can either specify a new local-dir (DeepSeek-V4-Flash-MXFP4) or download them all in place (./)

Credits

Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.

Thank you ZeroWw for the inspiration to experiment with embed/output.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski