Instructions to use sokann/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 sokann/DeepSeek-V4-Flash-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="sokann/DeepSeek-V4-Flash-GGUF", filename="DeepSeek-V4-Flash.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 sokann/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 sokann/DeepSeek-V4-Flash-GGUF # Run inference directly in the terminal: llama cli -hf sokann/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 sokann/DeepSeek-V4-Flash-GGUF # Run inference directly in the terminal: llama cli -hf sokann/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 sokann/DeepSeek-V4-Flash-GGUF # Run inference directly in the terminal: ./llama-cli -hf sokann/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 sokann/DeepSeek-V4-Flash-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf sokann/DeepSeek-V4-Flash-GGUF
Use Docker
docker model run hf.co/sokann/DeepSeek-V4-Flash-GGUF
- LM Studio
- Jan
- Ollama
How to use sokann/DeepSeek-V4-Flash-GGUF with Ollama:
ollama run hf.co/sokann/DeepSeek-V4-Flash-GGUF
- Unsloth Studio
How to use sokann/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 sokann/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 sokann/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 sokann/DeepSeek-V4-Flash-GGUF to start chatting
- Pi
How to use sokann/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 sokann/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": "sokann/DeepSeek-V4-Flash-GGUF" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use sokann/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 sokann/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 sokann/DeepSeek-V4-Flash-GGUF
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use sokann/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 sokann/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 "sokann/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 sokann/DeepSeek-V4-Flash-GGUF with Docker Model Runner:
docker model run hf.co/sokann/DeepSeek-V4-Flash-GGUF
- Lemonade
How to use sokann/DeepSeek-V4-Flash-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sokann/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
model is not working any idea for fix ?
CUDA_VISIBLE_DEVICES=2,3,0,1
~/llama.cpp/build/bin/llama-server
--model /mnt/nvme/nex/DeepSeek-V4-Flash.gguf
--tensor-split 1,1,1,1
--cpu-moe
--cache-type-k q8_0
--cache-type-v q8_0
--ctx-size 1000
--batch-size 1000
--ubatch-size 1000
--parallel 1
--temp 0.6
--top-p 0.95
--top-k 20
--threads 35
--threads-batch 35
--jinja
--host 0.0.0.0
--port 8080
i am running like this but models is producing ghebriesh
CUDA_VISIBLE_DEVICES=2,3,0,1
~/llama.cpp/build/bin/llama-server
--model /mnt/nvme/nex/DeepSeek-V4-Flash.gguf
--tensor-split 1,1,1,1
--cpu-moe
--cache-type-k q8_0
--cache-type-v q8_0
--ctx-size 1000
--batch-size 1000
--ubatch-size 1000
--parallel 1
--temp 0.6
--top-p 0.95
--top-k 20
--threads 35
--threads-batch 35
--jinja
--host 0.0.0.0
--port 8080
i just ran "./llama-server -hf sokann/DeepSeek-V4-Flash-GGUF (or -m path/to/DeepSeek-V4-Flash.gguf) -c 32768 --host 0.0.0.0 --port 8085 --jinja -np 1 -lv 4" and it worked fine on 1x3090 + some ram
i think multi-gpu setup might be broken for now
also i don't think the context, batch and ubatch sizes are correct
i just left everything on default and it figured it out by itself
Looks like the gibberish is caused by the Q8 KV cache. Works fine with the default F16 KV cache
Looks like the gibberish is caused by the Q8 KV cache. Works fine with the default F16 KV cache
exactly right looks like kv cache as some problem
i just ran "./llama-server -hf sokann/DeepSeek-V4-Flash-GGUF (or -m path/to/DeepSeek-V4-Flash.gguf) -c 32768 --host 0.0.0.0 --port 8085 --jinja -np 1 -lv 4" and it worked fine on 1x3090 + some ram
i think multi-gpu setup might be broken for now
also i don't think the context, batch and ubatch sizes are correct
i just left everything on default and it figured it out by itself
correct