Instructions to use addychen2/saseucmai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use addychen2/saseucmai with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="addychen2/saseucmai", filename="model.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use addychen2/saseucmai 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 addychen2/saseucmai # Run inference directly in the terminal: llama cli -hf addychen2/saseucmai
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf addychen2/saseucmai # Run inference directly in the terminal: llama cli -hf addychen2/saseucmai
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 addychen2/saseucmai # Run inference directly in the terminal: ./llama-cli -hf addychen2/saseucmai
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 addychen2/saseucmai # Run inference directly in the terminal: ./build/bin/llama-cli -hf addychen2/saseucmai
Use Docker
docker model run hf.co/addychen2/saseucmai
- LM Studio
- Jan
- Ollama
How to use addychen2/saseucmai with Ollama:
ollama run hf.co/addychen2/saseucmai
- Unsloth Studio
How to use addychen2/saseucmai 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 addychen2/saseucmai 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 addychen2/saseucmai to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for addychen2/saseucmai to start chatting
- Atomic Chat new
- Docker Model Runner
How to use addychen2/saseucmai with Docker Model Runner:
docker model run hf.co/addychen2/saseucmai
- Lemonade
How to use addychen2/saseucmai with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull addychen2/saseucmai
Run and chat with the model
lemonade run user.saseucmai-{{QUANT_TAG}}List all available models
lemonade list
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
DeepSeek Base Model in GGUF Format
This is the base DeepSeek 1.5B model converted to GGUF format for efficient inference.
Model Details
- Base model: DeepSeek 1.5B
- Quantization: Q8_0
- Format: GGUF
Usage
This model can be used with llama.cpp and other GGUF-compatible inference engines.
Original Model
This model was converted from deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B.
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="addychen2/saseucmai", filename="model.gguf", )