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
Download examples/model-conversion/scripts/embedding/run-converted-model.sh from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.39 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/model-conversion/scripts/embedding/run-converted-model.sh
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/examples/model-conversion/scripts/embedding/run-converted-model.sh
-
curl -L -o run-converted-model.sh https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/model-conversion/scripts/embedding/run-converted-model.sh
1.39 kB
| set -e | |
| # Parse command line arguments | |
| CONVERTED_MODEL="" | |
| PROMPTS_FILE="" | |
| EMBD_NORMALIZE="2" | |
| while [[ $# -gt 0 ]]; do | |
| case $1 in | |
| -p|--prompts-file) | |
| PROMPTS_FILE="$2" | |
| shift 2 | |
| ;; | |
| --embd-normalize) | |
| EMBD_NORMALIZE="$2" | |
| shift 2 | |
| ;; | |
| *) | |
| if [ -z "$CONVERTED_MODEL" ]; then | |
| CONVERTED_MODEL="$1" | |
| fi | |
| shift | |
| ;; | |
| esac | |
| done | |
| # First try command line argument, then environment variable | |
| CONVERTED_MODEL="${CONVERTED_MODEL:-"$CONVERTED_EMBEDDING_MODEL"}" | |
| BUILD_DIR="${BUILD_DIR:-"../../build"}" | |
| # Final check if we have a model path | |
| if [ -z "$CONVERTED_MODEL" ]; then | |
| echo "Error: Model path must be provided either as:" >&2 | |
| echo " 1. Command line argument" >&2 | |
| echo " 2. CONVERTED_EMBEDDING_MODEL environment variable" >&2 | |
| exit 1 | |
| fi | |
| # Read prompt from file or use default | |
| if [ -n "$PROMPTS_FILE" ]; then | |
| if [ ! -f "$PROMPTS_FILE" ]; then | |
| echo "Error: Prompts file '$PROMPTS_FILE' not found" >&2 | |
| exit 1 | |
| fi | |
| PROMPT=$(cat "$PROMPTS_FILE") | |
| else | |
| PROMPT="Hello world today" | |
| fi | |
| echo $CONVERTED_MODEL | |
| cmake --build ${BUILD_DIR} --target llama-debug -j8 | |
| ${BUILD_DIR}/bin/llama-debug -m "$CONVERTED_MODEL" --embedding -p "$PROMPT" --save-logits --embd-normalize $EMBD_NORMALIZE | |