Instructions to use bartowski/DeepSeek-V4-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
- Pi
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
- 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
Run on LLamaCPP
How can i run this model in LLamaCPP , i am using the last version from github but it isnt working
Works for me™
Alternatively you can try to build this branch: https://github.com/fairydreaming/llama.cpp/tree/dsv4
What error are you getting?
It also works for me
What error are you getting?
It also works for me
Thank you! It works for me in latest version of llama.cpp. But unfourtunatly it is not usable for agentic tasks, because the context window size very huge.
For example, for model Qwen3.5 397b Q6 (or Nex N2 Pro) I can put to my local machine context window = 262 000 with -ub 14000 -b 14000 that gives me pp=400 t/s, tg=11.5 t/s
For this implementation in llama.cpp I can only put the ctx=92000 with -ub 128 -b 512 that gives me only pp=23 t/s, tg=14 t/s.
It crashed on Mac Studio metal. Got a kernel panic