Instructions to use ubergarm/DeepSeek-R1-0528-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use ubergarm/DeepSeek-R1-0528-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ubergarm/DeepSeek-R1-0528-GGUF", filename="IQ1_S/DeepSeek-R1-0528-IQ1_S-00001-of-00003.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 ubergarm/DeepSeek-R1-0528-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 ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S # Run inference directly in the terminal: llama cli -hf ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S # Run inference directly in the terminal: llama cli -hf ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S
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 ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S # Run inference directly in the terminal: ./llama-cli -hf ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S
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 ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S
Use Docker
docker model run hf.co/ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S
- LM Studio
- Jan
- vLLM
How to use ubergarm/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 "ubergarm/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": "ubergarm/DeepSeek-R1-0528-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S
- Ollama
How to use ubergarm/DeepSeek-R1-0528-GGUF with Ollama:
ollama run hf.co/ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S
- Unsloth Studio
How to use ubergarm/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 ubergarm/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 ubergarm/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 ubergarm/DeepSeek-R1-0528-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use ubergarm/DeepSeek-R1-0528-GGUF with Docker Model Runner:
docker model run hf.co/ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S
- Lemonade
How to use ubergarm/DeepSeek-R1-0528-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S
Run and chat with the model
lemonade run user.DeepSeek-R1-0528-GGUF-IQ1_S
List all available models
lemonade list
Request for an IQ5 Quant
2
#19 opened 9 months ago
by
binahz
S1 Base 671B fine tune ik llama GGUF request?
1
#18 opened 12 months ago
by
facedwithahug
Playing with ik_llama params for IQ4_KS_R4 on RTX 5090
13
#17 opened about 1 year ago
by
sousekd
minimax-m1
👀 1
1
#14 opened about 1 year ago
by
cheaptoner2016
Benchmark DDR5 4x48gb 4800 Mt/s and Request 192gb quant
🚀❤️ 2
4
#13 opened about 1 year ago
by
Kirara702
DeepSeek-R1-256x21B-0528-BF16 GGUF?
👍 1
5
#12 opened about 1 year ago
by
Thireus
Is it possible to disable thinking?
10
#11 opened about 1 year ago
by
SlavikF
Scripts to produce PPL and KLD diagrams?
1
#10 opened about 1 year ago
by
Thireus
Multi GPU with different VRAM size does not work
6
#9 opened about 1 year ago
by
jweb
benchmarks
👍 2
5
#8 opened about 1 year ago
by
BernardH
Thanks for your work! Any chance for something between Q2_K_R and Q3_K_R?
👍👀 5
19
#7 opened about 1 year ago
by
Panchovix
1.5 bpw
➕ 1
19
#6 opened about 1 year ago
by
lmganon123
Local Installation Video and Testing - Step by Step
❤️ 2
4
#5 opened about 1 year ago
by
fahdmirzac
Recommendation for 256 ram 48 vram
❤️ 2
5
#2 opened about 1 year ago
by
ciprianv