Instructions to use maxwelhelp/llama.cpp-DFlash2-pascal6-optimized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use maxwelhelp/llama.cpp-DFlash2-pascal6-optimized 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 maxwelhelp/llama.cpp-DFlash2-pascal6-optimized # Run inference directly in the terminal: llama cli -hf maxwelhelp/llama.cpp-DFlash2-pascal6-optimized
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf maxwelhelp/llama.cpp-DFlash2-pascal6-optimized # Run inference directly in the terminal: llama cli -hf maxwelhelp/llama.cpp-DFlash2-pascal6-optimized
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 maxwelhelp/llama.cpp-DFlash2-pascal6-optimized # Run inference directly in the terminal: ./llama-cli -hf maxwelhelp/llama.cpp-DFlash2-pascal6-optimized
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 maxwelhelp/llama.cpp-DFlash2-pascal6-optimized # Run inference directly in the terminal: ./build/bin/llama-cli -hf maxwelhelp/llama.cpp-DFlash2-pascal6-optimized
Use Docker
docker model run hf.co/maxwelhelp/llama.cpp-DFlash2-pascal6-optimized
- LM Studio
- Jan
- Ollama
How to use maxwelhelp/llama.cpp-DFlash2-pascal6-optimized with Ollama:
ollama run hf.co/maxwelhelp/llama.cpp-DFlash2-pascal6-optimized
- Unsloth Desktop
- Docker Model Runner
How to use maxwelhelp/llama.cpp-DFlash2-pascal6-optimized with Docker Model Runner:
docker model run hf.co/maxwelhelp/llama.cpp-DFlash2-pascal6-optimized
- Lemonade
How to use maxwelhelp/llama.cpp-DFlash2-pascal6-optimized with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull maxwelhelp/llama.cpp-DFlash2-pascal6-optimized
Run and chat with the model
lemonade run user.llama.cpp-DFlash2-pascal6-optimized-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
- Xet hash:
- 420c74f2fdabad294d15dd4499edf675b93d1e6a75952198a8a425b0568eebed
- Size of remote file:
- 4.68 kB
- SHA256:
- 2fe29182ac9b82b5e8d6dabe66f94a2cc269531088b43e5debe9322a0eb67be5
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