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 tools/ui/src/lib/constants/cache.constants.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 924 Bytes
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/constants/cache.constants.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/constants/cache.constants.ts
-
curl -L -o cache.constants.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/constants/cache.constants.ts
924 Bytes
| /** | |
| * Cache configuration constants | |
| */ | |
| /** | |
| * Default cache limits when no per-cache overrides are given. | |
| */ | |
| export const CACHE = { | |
| /** Default maximum number of entries in a cache */ | |
| DEFAULT_MAX_ENTRIES: 100, | |
| /** Default TTL (Time-To-Live) for cache entries in milliseconds (5 minutes) */ | |
| DEFAULT_TTL_MS: 5 * 60 * 1000 | |
| } as const; | |
| /** | |
| * TTL and size for the model props cache. | |
| * Props don't change frequently, so we can cache them longer. | |
| */ | |
| export const MODEL_PROPS_CACHE = { | |
| /** Maximum number of model props to cache */ | |
| MAX_ENTRIES: 50, | |
| /** TTL for model props cache entries in milliseconds (10 minutes) */ | |
| TTL_MS: 10 * 60 * 1000 | |
| } as const; | |
| /** | |
| * TTL and size for the MCP resource cache. | |
| */ | |
| export const MCP_RESOURCE_CACHE = { | |
| /** Maximum number of MCP resources to cache */ | |
| MAX_ENTRIES: 50, | |
| /** TTL for MCP resource cache entries in milliseconds (5 minutes) */ | |
| TTL_MS: 5 * 60 * 1000 | |
| } as const; | |