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/llama.android/gradle.properties 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/llama.android/gradle.properties
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/examples/llama.android/gradle.properties
-
curl -L -o gradle.properties https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/llama.android/gradle.properties
1.39 kB
| # Project-wide Gradle settings. | |
| # IDE (e.g. Android Studio) users: | |
| # Gradle settings configured through the IDE *will override* | |
| # any settings specified in this file. | |
| # For more details on how to configure your build environment visit | |
| # http://www.gradle.org/docs/current/userguide/build_environment.html | |
| # Specifies the JVM arguments used for the daemon process. | |
| # The setting is particularly useful for tweaking memory settings. | |
| org.gradle.jvmargs=-Xmx2048m -Dfile.encoding=UTF-8 | |
| # When configured, Gradle will run in incubating parallel mode. | |
| # This option should only be used with decoupled projects. More details, visit | |
| # http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects | |
| # org.gradle.parallel=true | |
| # AndroidX package structure to make it clearer which packages are bundled with the | |
| # Android operating system, and which are packaged with your app's APK | |
| # https://developer.android.com/topic/libraries/support-library/androidx-rn | |
| android.useAndroidX=true | |
| # Kotlin code style for this project: "official" or "obsolete": | |
| kotlin.code.style=official | |
| # Enables namespacing of each library's R class so that its R class includes only the | |
| # resources declared in the library itself and none from the library's dependencies, | |
| # thereby reducing the size of the R class for that library | |
| android.nonTransitiveRClass=true | |
| android.native.buildOutput=verbose | |