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/causal/convert-model.sh from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.43 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/model-conversion/scripts/causal/convert-model.sh
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/examples/model-conversion/scripts/causal/convert-model.sh
-
curl -L -o convert-model.sh https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/model-conversion/scripts/causal/convert-model.sh
1.43 kB
| set -e | |
| # Parse command line arguments | |
| MMPROJ="" | |
| DEBUG="" | |
| while [[ $# -gt 0 ]]; do | |
| case $1 in | |
| --mmproj) | |
| MMPROJ="--mmproj" | |
| shift | |
| ;; | |
| --debug) | |
| DEBUG="1" | |
| shift | |
| ;; | |
| *) | |
| shift | |
| ;; | |
| esac | |
| done | |
| MODEL_NAME="${MODEL_NAME:-$(basename "$MODEL_PATH")}" | |
| OUTPUT_DIR="${OUTPUT_DIR:-../../models}" | |
| TYPE="${OUTTYPE:-f16}" | |
| METADATA_OVERRIDE="${METADATA_OVERRIDE:-}" | |
| if [[ -n "$MMPROJ" ]]; then | |
| CONVERTED_MODEL="${OUTPUT_DIR}/mmproj-${MODEL_NAME}.gguf" | |
| else | |
| CONVERTED_MODEL="${OUTPUT_DIR}/${MODEL_NAME}.gguf" | |
| fi | |
| echo "Model path: ${MODEL_PATH}" | |
| echo "Model name: ${MODEL_NAME}" | |
| echo "Data type: ${TYPE}" | |
| echo "Converted model path:: ${CONVERTED_MODEL}" | |
| echo "Metadata override: ${METADATA_OVERRIDE}" | |
| if [[ -n "$DEBUG" ]]; then | |
| CMD_ARGS=("python" "-m" "pdb") | |
| else | |
| CMD_ARGS=("python") | |
| fi | |
| CMD_ARGS+=("../../convert_hf_to_gguf.py" "--verbose") | |
| CMD_ARGS+=("${MODEL_PATH}") | |
| CMD_ARGS+=("--outfile" "${CONVERTED_MODEL}") | |
| CMD_ARGS+=("--outtype" "${TYPE}") | |
| CMD_ARGS+=("--model-name" "${MODEL_NAME}") | |
| [[ -n "$METADATA_OVERRIDE" ]] && CMD_ARGS+=("--metadata" "${METADATA_OVERRIDE}") | |
| [[ -n "$MMPROJ" ]] && CMD_ARGS+=("${MMPROJ}") | |
| "${CMD_ARGS[@]}" | |
| echo "" | |
| echo "The environment variable CONVERTED_MODEL can be set to this path using:" | |
| echo "export CONVERTED_MODEL=$(realpath ${CONVERTED_MODEL})" | |