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 (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -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 serve -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
- Atomic Chat new
- 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
Support Vllm?
code from https://docs.vllm.ai/en/latest/features/quantization/gguf.html
from vllm import LLM, SamplingParams
# In this script, we demonstrate how to pass input to the chat method:
conversation = [
{
"role": "system",
"content": "You are a helpful assistant"
},
{
"role": "user",
"content": "Hello"
},
{
"role": "assistant",
"content": "Hello! How can I assist you today?"
},
{
"role": "user",
"content": "Write an essay about the importance of higher education.",
},
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
# Create an LLM.
llm = LLM(model="unsloth/DeepSeek-R1-Distill-Qwen-32B-GGUF",
tokenizer="Qwen/Qwen2.5-32B-Instruct")
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.chat(conversation, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
modify model and tokenizer
raise a error:
OSERROR: It looks like the config file at 'xxxx/DeepSeek-R1-Distill-Qwen-32B-Q4_K_M.gguf' is not a valid JSON file.
Question
this gguf not supported by vllm?
code from https://docs.vllm.ai/en/latest/features/quantization/gguf.html
from vllm import LLM, SamplingParams # In this script, we demonstrate how to pass input to the chat method: conversation = [ { "role": "system", "content": "You are a helpful assistant" }, { "role": "user", "content": "Hello" }, { "role": "assistant", "content": "Hello! How can I assist you today?" }, { "role": "user", "content": "Write an essay about the importance of higher education.", }, ] # Create a sampling params object. sampling_params = SamplingParams(temperature=0.8, top_p=0.95) # Create an LLM. llm = LLM(model="unsloth/DeepSeek-R1-Distill-Qwen-32B-GGUF", tokenizer="Qwen/Qwen2.5-32B-Instruct") # Generate texts from the prompts. The output is a list of RequestOutput objects # that contain the prompt, generated text, and other information. outputs = llm.chat(conversation, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")modify model and tokenizer
raise a error:OSERROR: It looks like the config file at 'xxxx/DeepSeek-R1-Distill-Qwen-32B-Q4_K_M.gguf' is not a valid JSON file.Question
this gguf not supported by vllm?
GGUF was just supportedfor DeepSeek. Follow along the issue here: https://github.com/vllm-project/vllm/issues/12573
Thank you for reply. I get it.