Instructions to use taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-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 taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16 # Run inference directly in the terminal: llama cli -hf taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16 # Run inference directly in the terminal: llama cli -hf taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16
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 taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16 # Run inference directly in the terminal: ./llama-cli -hf taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16
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 taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16
Use Docker
docker model run hf.co/taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16
- LM Studio
- Jan
- vLLM
How to use taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-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": "taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16
- Ollama
How to use taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF with Ollama:
ollama run hf.co/taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16
- Unsloth Desktop
- Pi
How to use taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF with Docker Model Runner:
docker model run hf.co/taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16
- Lemonade
How to use taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16
Run and chat with the model
lemonade run user.DeepSeek-V4.1-Flash-GSQ-RCO-GGUF-BF16
List all available models
lemonade list
- Hermes Agent
How to use taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-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 taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16
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 taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16
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 "taurusduan/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF:BF16" \ --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"
DeepSeek evaluation results
The 3.0-bit model scored 1,220/2,000 (61.00%) on Spark's fixed MMLU-Pro subset. Held-out PPL is 3.4845570141 over 4,088 scored positions. The runtime uses F32 matrix multiplication and KV cache, with Flash Attention disabled.
IFEval has 16 passes from 16 prompts, including one truncated answer. The other 15 completed. All eight GSM8K answers completed correctly. The official parser accepted all 23 completed responses; the truncated response was not parsed.
From this directory, recompute the MMLU-Pro score from the saved outputs:
python3 analyze_mmlu.py --config results/mmlu/deepseek-analysis-config.json \
--output-prefix results/mmlu/deepseek-analysis