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
File size: 1,472 Bytes
c59fd93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | #!/usr/bin/env bash
set -e
CONVERTED_MODEL="${1:-"$CONVERTED_MODEL"}"
QUANTIZED_TYPE="${2:-"$QUANTIZED_TYPE"}"
TOKEN_EMBD_TYPE="${3:-"${TOKEN_EMBD_TYPE}"}"
OUTPUT_TYPE="${4:-"${OUTPUT_TYPE}"}"
BUILD_DIR="${5:-"$BUILD_DIR"}"
QUANTIZED_MODEL=$CONVERTED_MODEL
# Final check if we have a model path
if [ -z "$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 "$QUANTIZED_TYPE" ]; then
echo "Error: QUANTIZED_TYPE is required" >&2
exit 1
fi
echo $CONVERTED_MODEL
# Process the quantized model filename
if [[ "$QUANTIZED_MODEL" == *.gguf ]]; then
# Remove .gguf suffix, add quantized type, then add .gguf back
BASE_NAME="${QUANTIZED_MODEL%.gguf}"
QUANTIZED_MODEL="${BASE_NAME}-${QUANTIZED_TYPE}.gguf"
else
echo "Error: QUANTIZED_MODEL must end with .gguf extension" >&2
exit 1
fi
if [ -z "$BUILD_DIR" ]; then
BUILD_DIR="../../build"
fi
cmake --build $BUILD_DIR --target llama-quantize -j8
echo $TOKEN_EMBD_TYPE
echo $OUTPUT_TYPE
CMD_ARGS=("${BUILD_DIR}/bin/llama-quantize")
[[ -n "$TOKEN_EMBD_TYPE" ]] && CMD_ARGS+=("--token-embedding-type" "$TOKEN_EMBD_TYPE")
[[ -n "$OUTPUT_TYPE" ]] && CMD_ARGS+=("--output-tensor-type" "$OUTPUT_TYPE")
CMD_ARGS+=("$CONVERTED_MODEL" "$QUANTIZED_MODEL" "$QUANTIZED_TYPE")
"${CMD_ARGS[@]}"
echo "Quantized model saved to: $QUANTIZED_MODEL"
|