Instructions to use unsloth/DeepSeek-R1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/DeepSeek-R1-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="unsloth/DeepSeek-R1-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-GGUF", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("unsloth/DeepSeek-R1-GGUF", trust_remote_code=True) - llama-cpp-python
How to use unsloth/DeepSeek-R1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="unsloth/DeepSeek-R1-GGUF", filename="DeepSeek-R1-BF16/DeepSeek-R1.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 Settings
- llama.cpp
How to use unsloth/DeepSeek-R1-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-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf unsloth/DeepSeek-R1-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf unsloth/DeepSeek-R1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf unsloth/DeepSeek-R1-GGUF:Q4_K_M
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-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf unsloth/DeepSeek-R1-GGUF:Q4_K_M
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-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf unsloth/DeepSeek-R1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/unsloth/DeepSeek-R1-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use unsloth/DeepSeek-R1-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-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-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/unsloth/DeepSeek-R1-GGUF:Q4_K_M
- SGLang
How to use unsloth/DeepSeek-R1-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-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-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-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-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use unsloth/DeepSeek-R1-GGUF with Ollama:
ollama run hf.co/unsloth/DeepSeek-R1-GGUF:Q4_K_M
- Unsloth Studio
How to use unsloth/DeepSeek-R1-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-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-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-GGUF to start chatting
- Docker Model Runner
How to use unsloth/DeepSeek-R1-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/DeepSeek-R1-GGUF:Q4_K_M
- Lemonade
How to use unsloth/DeepSeek-R1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/DeepSeek-R1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.DeepSeek-R1-GGUF-Q4_K_M
List all available models
lemonade list
didn't work with ollama: out of memory
I have Ryzen 9 7950x3d 64 gb RAM, rtx 3090 24 gb VRAM and trying to run DeepSeek-R1-UD-IQ1
it's work for me however, the model does not use RAM and VRAM, only the cash and the swap file, so the speed is 1,42 token per second:
$ llama-b4872-bin-ubuntu-x64/build/bin/llama-cli --model DeepSeek-R1-UD-IQ1_S.gguf --cache-type-k q4_0 --threads 24 --prio 2 --temp 0.6 --ctx-size 8192 --seed 3407 --n-gpu-layers 7 -no-cnv --prompt "<|User|>Create a Flappy Bird game in Python.<|Assistant|>"
I want to launch a model on ollama, merge it, make Modelfile:
Modelfile
FROM ./DeepSeek-R1-UD-IQ1.gguf
PARAMETER num_ctx 4096
PARAMETER temperature 0.6
but I get an error:
$ OLLAMA_GPULAYERS=7 OLLAMA_MMLOCK=0 ollama run DeepSeek-R1-UD-IQ1
Error: llama runner process has terminated: cudaMalloc failed: out of memory
ggml_gallocr_reserve_n: failed to allocate CUDA0 buffer of size 6612361216
llama_init_from_model: failed to allocate compute buffers
how to set up ollama correctly?
You are such an idiot, can't you add virtual memory?
there is 128 GB of swap, this does not eliminate the error
You are such an idiot, you increased the virtual memory to 512GB