Instructions to use VladHong/llama-cpp-K2-FAST2 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 VladHong/llama-cpp-K2-FAST2 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 VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: llama cli -hf VladHong/llama-cpp-K2-FAST2
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: llama cli -hf VladHong/llama-cpp-K2-FAST2
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: ./build/bin/llama cli -hf VladHong/llama-cpp-K2-FAST2
Use Docker
docker model run hf.co/VladHong/llama-cpp-K2-FAST2
- Llama new
- LM Studio
- Jan
- Ollama
How to use VladHong/llama-cpp-K2-FAST2 with Ollama:
ollama run hf.co/VladHong/llama-cpp-K2-FAST2
- Unsloth Desktop
- Docker Model Runner
How to use VladHong/llama-cpp-K2-FAST2 with Docker Model Runner:
docker model run hf.co/VladHong/llama-cpp-K2-FAST2
- Lemonade
How to use VladHong/llama-cpp-K2-FAST2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull VladHong/llama-cpp-K2-FAST2
Run and chat with the model
lemonade run user.llama-cpp-K2-FAST2-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
llama-cpp-K2-FAST2 / examples /model-conversion /scripts /causal /run-converted-model-embeddings-logits.sh
Download examples/model-conversion/scripts/causal/run-converted-model-embeddings-logits.sh from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 636 Bytes
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/model-conversion/scripts/causal/run-converted-model-embeddings-logits.sh
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/examples/model-conversion/scripts/causal/run-converted-model-embeddings-logits.sh
-
curl -L -o run-converted-model-embeddings-logits.sh https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/model-conversion/scripts/causal/run-converted-model-embeddings-logits.sh
636 Bytes
| set -e | |
| # First try command line argument, then environment variable, then file | |
| CONVERTED_MODEL="${1:-"$CONVERTED_MODEL"}" | |
| BUILD_DIR="${2:-"$BUILD_DIR"}" | |
| # Final check if we have a model path | |
| if [ -z "$CONVERTED_MODEL" ]; then | |
| echo "Error: Model path must be provided either as:" >&2 | |
| echo " 1. Command line argument" >&2 | |
| echo " 2. CONVERTED_MODEL environment variable" >&2 | |
| exit 1 | |
| fi | |
| if [ -z "$BUILD_DIR" ]; then | |
| BUILD_DIR="../../build" | |
| fi | |
| cmake --build ${BUILD_DIR} --target llama-debug -j8 | |
| ${BUILD_DIR}/bin/llama-debug -m $CONVERTED_MODEL --embedding -p "Hello world today" --save-logits | |