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

bartowski Colin Kealty commited on 7 days ago

Commit

9576d46

0 Parent(s):

Super-squash branch 'main' using huggingface_hub

Browse files

Co-authored-by: Colin Kealty <Colin Kealty@users.noreply.huggingface.co>

Files changed (6) hide show

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+---
+quantized_by: bartowski
+pipeline_tag: image-text-to-text
+license: mit
+base_model_relation: quantized
+base_model: deepseek-ai/DeepSeek-V4-Flash
+---
+## Llamacpp Quantizations of DeepSeek-V4-Flash by deepseek-ai
+Using <a href="https://github.com/ggml-org/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggml-org/llama.cpp/releases/tag/b9843">b9843</a> 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:
+- [llama.cpp](https://github.com/ggml-org/llama.cpp)
+- [ramalama](https://github.com/containers/ramalama)
+- [LM Studio](https://lmstudio.ai/)
+- [koboldcpp](https://github.com/LostRuins/koboldcpp)
+- [Jan AI](https://www.jan.ai/)
+- [Text Generation Web UI](https://github.com/oobabooga/text-generation-webui)
+- [LoLLMs](https://github.com/ParisNeo/lollms)
+- [Atomic Chat](https://atomic.chat/)
+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](https://huggingface.co/bartowski/DeepSeek-V4-Flash-GGUF/tree/main/DeepSeek-V4-Flash-MXFP4) | MXFP4 | 156.00GB | true | Original quality. |
+## Downloading using huggingface-cli
+<details>
+  <summary>Click to view download instructions</summary>
+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 (./)
+</details>
+## 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