Instructions to use unsloth/DeepSeek-R1-0528-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/DeepSeek-R1-0528-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="unsloth/DeepSeek-R1-0528-GGUF", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("unsloth/DeepSeek-R1-0528-GGUF", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("unsloth/DeepSeek-R1-0528-GGUF", trust_remote_code=True) - llama-cpp-python
How to use unsloth/DeepSeek-R1-0528-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="unsloth/DeepSeek-R1-0528-GGUF", filename="BF16/DeepSeek-R1-0528-BF16-00001-of-00030.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 unsloth/DeepSeek-R1-0528-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf unsloth/DeepSeek-R1-0528-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-R1-0528-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf unsloth/DeepSeek-R1-0528-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-R1-0528-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL
Use Docker
docker model run hf.co/unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL
- LM Studio
- Jan
- vLLM
How to use unsloth/DeepSeek-R1-0528-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unsloth/DeepSeek-R1-0528-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": "unsloth/DeepSeek-R1-0528-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL
- SGLang
How to use unsloth/DeepSeek-R1-0528-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "unsloth/DeepSeek-R1-0528-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsloth/DeepSeek-R1-0528-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "unsloth/DeepSeek-R1-0528-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsloth/DeepSeek-R1-0528-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use unsloth/DeepSeek-R1-0528-GGUF with Ollama:
ollama run hf.co/unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL
- Unsloth Studio new
How to use unsloth/DeepSeek-R1-0528-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-R1-0528-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-R1-0528-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-R1-0528-GGUF to start chatting
- Pi new
How to use unsloth/DeepSeek-R1-0528-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf unsloth/DeepSeek-R1-0528-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-R1-0528-GGUF:UD-Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use unsloth/DeepSeek-R1-0528-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 unsloth/DeepSeek-R1-0528-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-R1-0528-GGUF:UD-Q4_K_XL
Run Hermes
hermes
- Docker Model Runner
How to use unsloth/DeepSeek-R1-0528-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL
- Lemonade
How to use unsloth/DeepSeek-R1-0528-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL
Run and chat with the model
lemonade run user.DeepSeek-R1-0528-GGUF-UD-Q4_K_XL
List all available models
lemonade list
New 1.66-bit TQ1_0 quant that is 162GB
We added a new TQ1_0 quant that is 1.66-bit and 162GB in size. For those who want it to fit exactly on some setups and some more lenient combinations
Made for setups with 192GB RAM and Ollama
What’s the difference compared to IQ1_S? What layers are compressed more?
could you please share how to run it with llama cpp? Thanks.
Edit: it seems working
./llama.cpp/llama-cli \
-hf unsloth/DeepSeek-R1-0528-GGUF:TQ1_0 \
--cache-type-k q4_0 \
--threads -1 \
--n-gpu-layers 99 \
--prio 3 \
--temp 0.6 \
--top_p 0.95 \
--min_p 0.01 \
--ctx-size 16384 \
--seed 3407 \
-ot ".ffn_.*_exps.=CPU"
What’s the difference compared to IQ1_S? What layers are compressed more?
Correct but the correct layers
Is there a comparison of performance (benchmark score) between each quantized version and the original checkpoint? Thanks!