Instructions to use unsloth/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 unsloth/DeepSeek-V4-Flash-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="unsloth/DeepSeek-V4-Flash-GGUF", filename="UD-IQ1_M/DeepSeek-V4-Flash-UD-IQ1_M-00001-of-00003.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use unsloth/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 unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL
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 unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL
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 unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL
Use Docker
docker model run hf.co/unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL
- LM Studio
- Jan
- Ollama
How to use unsloth/DeepSeek-V4-Flash-GGUF with Ollama:
ollama run hf.co/unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL
- Unsloth Studio
How to use unsloth/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 unsloth/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 unsloth/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 unsloth/DeepSeek-V4-Flash-GGUF to start chatting
- Pi
How to use unsloth/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 unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL
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": "unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use unsloth/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 unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL
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 unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use unsloth/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 unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL
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 "unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL" \ --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 unsloth/DeepSeek-V4-Flash-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL
- Lemonade
How to use unsloth/DeepSeek-V4-Flash-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/DeepSeek-V4-Flash-GGUF:UD-Q4_K_XL
Run and chat with the model
lemonade run user.DeepSeek-V4-Flash-GGUF-UD-Q4_K_XL
List all available models
lemonade list
DeepSeek seems to have a much smaller context window
Compared to other models with similar parameter counts, DeepSeek seems to have a much smaller context window. When I use --fit-ctx to automatically allocate context size, models like Qwen and StepFun manage to get 256k, whereas DeepSeek (Q4) only gets around 90k. Why is that? I am using a 16GB 4080 and only keep shared expert on vram.
rn afaik deepseek's lightning indexer isn't implemented in llama.cpp yet so its KV cache tricks aren't usable yet, so KV cache takes up a lot more space than it should