24GB
Collection
Stuff sized to fit on 24GB rigs like the 3090 4090 etc with enough room for some context. • 5 items • Updated
How to use PsiPi/cwm-Q2_K-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf PsiPi/cwm-Q2_K-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf PsiPi/cwm-Q2_K-GGUF:Q2_K
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf PsiPi/cwm-Q2_K-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf PsiPi/cwm-Q2_K-GGUF:Q2_K
# 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 PsiPi/cwm-Q2_K-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf PsiPi/cwm-Q2_K-GGUF:Q2_K
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 PsiPi/cwm-Q2_K-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf PsiPi/cwm-Q2_K-GGUF:Q2_K
docker model run hf.co/PsiPi/cwm-Q2_K-GGUF:Q2_K
How to use PsiPi/cwm-Q2_K-GGUF with Ollama:
ollama run hf.co/PsiPi/cwm-Q2_K-GGUF:Q2_K
How to use PsiPi/cwm-Q2_K-GGUF with Docker Model Runner:
docker model run hf.co/PsiPi/cwm-Q2_K-GGUF:Q2_K
How to use PsiPi/cwm-Q2_K-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull PsiPi/cwm-Q2_K-GGUF:Q2_K
lemonade run user.cwm-Q2_K-GGUF-Q2_K
lemonade list
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf PsiPi/cwm-Q2_K-GGUF:Q2_K# Run inference directly in the terminal:
llama cli -hf PsiPi/cwm-Q2_K-GGUF:Q2_K# 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 PsiPi/cwm-Q2_K-GGUF:Q2_K# Run inference directly in the terminal:
./llama-cli -hf PsiPi/cwm-Q2_K-GGUF:Q2_Kgit 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 PsiPi/cwm-Q2_K-GGUF:Q2_K# Run inference directly in the terminal:
./build/bin/llama-cli -hf PsiPi/cwm-Q2_K-GGUF:Q2_Kdocker model run hf.co/PsiPi/cwm-Q2_K-GGUF:Q2_KRefer to the original model card for more details on the model.
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
llama-cli --hf-repo PsiPi/cwm-Q2_K-GGUF --hf-file cwm-q2_k.gguf -p "The meaning to life and the universe is"
llama-server --hf-repo PsiPi/cwm-Q2_K-GGUF --hf-file cwm-q2_k.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo PsiPi/cwm-Q2_K-GGUF --hf-file cwm-q2_k.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo PsiPi/cwm-Q2_K-GGUF --hf-file cwm-q2_k.gguf -c 2048
2-bit
Base model
facebook/cwm
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf PsiPi/cwm-Q2_K-GGUF:Q2_K# Run inference directly in the terminal: llama cli -hf PsiPi/cwm-Q2_K-GGUF:Q2_K