Instructions to use Volko76/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 Volko76/DeepSeek-V4-Flash-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Volko76/DeepSeek-V4-Flash-GGUF", filename="DeepSeek-V4-Flash-Q2_K.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Volko76/DeepSeek-V4-Flash-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Volko76/DeepSeek-V4-Flash-GGUF:Q2_K # Run inference directly in the terminal: llama-cli -hf Volko76/DeepSeek-V4-Flash-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Volko76/DeepSeek-V4-Flash-GGUF:Q2_K # Run inference directly in the terminal: llama-cli -hf Volko76/DeepSeek-V4-Flash-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 Volko76/DeepSeek-V4-Flash-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf Volko76/DeepSeek-V4-Flash-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 Volko76/DeepSeek-V4-Flash-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf Volko76/DeepSeek-V4-Flash-GGUF:Q2_K
Use Docker
docker model run hf.co/Volko76/DeepSeek-V4-Flash-GGUF:Q2_K
- LM Studio
- Jan
- vLLM
How to use Volko76/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 "Volko76/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": "Volko76/DeepSeek-V4-Flash-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Volko76/DeepSeek-V4-Flash-GGUF:Q2_K
- Ollama
How to use Volko76/DeepSeek-V4-Flash-GGUF with Ollama:
ollama run hf.co/Volko76/DeepSeek-V4-Flash-GGUF:Q2_K
- Unsloth Studio new
How to use Volko76/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 Volko76/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 Volko76/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 Volko76/DeepSeek-V4-Flash-GGUF to start chatting
- Pi new
How to use Volko76/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-server -hf Volko76/DeepSeek-V4-Flash-GGUF:Q2_K
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": "Volko76/DeepSeek-V4-Flash-GGUF:Q2_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Volko76/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-server -hf Volko76/DeepSeek-V4-Flash-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 Volko76/DeepSeek-V4-Flash-GGUF:Q2_K
Run Hermes
hermes
- Docker Model Runner
How to use Volko76/DeepSeek-V4-Flash-GGUF with Docker Model Runner:
docker model run hf.co/Volko76/DeepSeek-V4-Flash-GGUF:Q2_K
- Lemonade
How to use Volko76/DeepSeek-V4-Flash-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Volko76/DeepSeek-V4-Flash-GGUF:Q2_K
Run and chat with the model
lemonade run user.DeepSeek-V4-Flash-GGUF-Q2_K
List all available models
lemonade list
A Q2_K version of https://huggingface.co/ssweens/deepseek-ai__DeepSeek-V4-Flash-GGUF-YMMV
🧪 Experimental GGUFs for DeepSeek-V4-Flash
A stopgap to experiment with DeepSeek-V4-Flash with CUDA and ROCm locally while the tools ecosystem catches up. Expect rough edges. Validated for text and coding coherence.
GGUF files for deepseek-ai/DeepSeek-V4-Flash.
⚠️ You need the custom fork
These GGUFs require a DeepSeek-V4-capable fork of llama.cpp. Vanilla llama.cpp doesn't support this architecture yet.
- llama.cpp fork: ssweens/llama.cpp-deepseek-v4
- Backends: Tested on CUDA and ROCm.
Performance
Example:
llama-server -ngl 99 --no-mmap -fa on -np 1 --reasoning-format auto --jinja --threads 3 -ts 4,4,3 -dev CUDA0,CUDA1,CUDA2 \
-m /mnt/supmodels/gguf/deepseek-ai__DeepSeek-V4-Flash/deepseek-ai__DeepSeek-V4-Flash-Q4_K_M.gguf -c 32768 -b 2048 -ub 512 -ctk q8_0 -ctv q8_0
Speed (custom, n=2)
| Model | Prompt t/s | Gen t/s | TTFT s | Decode s | Backend |
|---|---|---|---|---|---|
| IQ2_XXS | 389.56 | 24.04 | 7.59 | 0.00 | CUDA |
| Q2_K_S | 231.15 | 18.79 | 10.58 | 0.00 | CUDA+ROCm |
| BF16 | 158.58 | 10.07 | 18.17 | 0.00 | CUDA+ROCm |
| Coding (humaneval_instruct, n=30) | |||||
| Model | pass@1 | Backend | |||
| ----- | ------ | ------- | |||
| IQ2_XXS | 0.967±0.033 | CUDA | |||
| Q2_K_S | 0.900±0.056 | CUDA+ROCm | |||
| BF16 | 1.000±0.000 | CUDA+ROCm |
Original model
Thanks
- antirez — llama.cpp fork for Metal and CUDA in llama.cpp-deepseek-v4-flash
- ml-explore/mlx-lm #1192 — MLX DSV4 attention reference that informed the architecture work
- DeepSeek — open inference code and the technical report
- nisparks et al - some early implementation efforts and discussion
- llama.cpp — the project that makes local LLM inference possible
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Model tree for Volko76/DeepSeek-V4-Flash-GGUF
Base model
deepseek-ai/DeepSeek-V4-Flash