Instructions to use QingHong258/Deepseek-R1-8b-JiangPing-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use QingHong258/Deepseek-R1-8b-JiangPing-v1 with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("fill-in-model-name") model.load_adapter("QingHong258/Deepseek-R1-8b-JiangPing-v1", set_active=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QingHong258/Deepseek-R1-8b-JiangPing-v1 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 QingHong258/Deepseek-R1-8b-JiangPing-v1 # Run inference directly in the terminal: llama cli -hf QingHong258/Deepseek-R1-8b-JiangPing-v1
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf QingHong258/Deepseek-R1-8b-JiangPing-v1 # Run inference directly in the terminal: llama cli -hf QingHong258/Deepseek-R1-8b-JiangPing-v1
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 QingHong258/Deepseek-R1-8b-JiangPing-v1 # Run inference directly in the terminal: ./llama-cli -hf QingHong258/Deepseek-R1-8b-JiangPing-v1
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 QingHong258/Deepseek-R1-8b-JiangPing-v1 # Run inference directly in the terminal: ./build/bin/llama-cli -hf QingHong258/Deepseek-R1-8b-JiangPing-v1
Use Docker
docker model run hf.co/QingHong258/Deepseek-R1-8b-JiangPing-v1
- LM Studio
- Jan
- vLLM
How to use QingHong258/Deepseek-R1-8b-JiangPing-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QingHong258/Deepseek-R1-8b-JiangPing-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QingHong258/Deepseek-R1-8b-JiangPing-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/QingHong258/Deepseek-R1-8b-JiangPing-v1
- Ollama
How to use QingHong258/Deepseek-R1-8b-JiangPing-v1 with Ollama:
ollama run hf.co/QingHong258/Deepseek-R1-8b-JiangPing-v1
- Unsloth Desktop
- Docker Model Runner
How to use QingHong258/Deepseek-R1-8b-JiangPing-v1 with Docker Model Runner:
docker model run hf.co/QingHong258/Deepseek-R1-8b-JiangPing-v1
- Lemonade
How to use QingHong258/Deepseek-R1-8b-JiangPing-v1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QingHong258/Deepseek-R1-8b-JiangPing-v1
Run and chat with the model
lemonade run user.Deepseek-R1-8b-JiangPing-v1-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Ctrl+K