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/sycl/build.sh from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.33 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/sycl/build.sh
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/examples/sycl/build.sh
-
curl -L -o build.sh https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/sycl/build.sh
1.33 kB
| # MIT license | |
| # Copyright (C) 2024 Intel Corporation | |
| # SPDX-License-Identifier: MIT | |
| print_usage() { | |
| echo "Usage: ./build.sh [fp32|fp16] [--help]" | |
| echo "" | |
| echo "Options:" | |
| echo " fp32 Build with FP32 precision (default)" | |
| echo " fp16 Build with FP16 precision (faster for long-prompt inference)" | |
| echo " --help Print this help message" | |
| } | |
| PRECISION=fp32 | |
| for arg in "$@"; do | |
| case "$arg" in | |
| --help) | |
| print_usage | |
| exit 0 | |
| ;; | |
| fp32|fp16) | |
| PRECISION="$arg" | |
| ;; | |
| *) | |
| echo "Error: unknown option '$arg'" | |
| print_usage | |
| exit 1 | |
| ;; | |
| esac | |
| done | |
| mkdir -p build | |
| cd build | |
| source /opt/intel/oneapi/setvars.sh | |
| if [ "$PRECISION" = "fp16" ]; then | |
| #for FP16 | |
| cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON -DLLAMA_OPENSSL=OFF # faster for long-prompt inference | |
| else | |
| #for FP32 | |
| cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_OPENSSL=OFF | |
| fi | |
| #build example/main | |
| #cmake --build . --config Release --target main | |
| #build example/llama-bench | |
| #cmake --build . --config Release --target llama-bench | |
| #build all binary | |
| cmake --build . --config Release -j$((($(nproc)+1)/2)) -v | |