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/stores/index.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.6 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/stores/index.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/stores/index.ts
-
curl -L -o index.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/stores/index.ts
1.6 kB
| /** | |
| * STORES | |
| * | |
| * Reactive Svelte runes state layer. Stores own application state and | |
| * expose it as plain Svelte 5 runes (`$state`, `$derived`, `$effect`), | |
| * consumed by components, routes, hooks and services. | |
| * | |
| * Import from this barrel in leaf consumers: | |
| * | |
| * ```ts | |
| * import { chatStore, modelsStore } from '$lib/stores'; | |
| * ``` | |
| * | |
| * Store modules keep direct imports between each other (and from services/ | |
| * utils they depend on) to avoid circular dependency chains. | |
| * | |
| * Each store below documents its primary responsibility. | |
| */ | |
| // CHAT / MESSAGING | |
| export { chatStore } from './chat/index.svelte'; | |
| export { draftMessagesStore } from './chat/drafts.svelte'; | |
| // CONVERSATION TABS | |
| export { tabsStore } from './tabs.svelte'; | |
| // CONTEXT STATS (active conversation context window usage) | |
| export { contextStatsStore } from './chat/context-stats.svelte'; | |
| // AGENTIC (multi-turn tool orchestration) | |
| export { agenticStore } from './agentic/index.svelte'; | |
| // CONVERSATIONS | |
| export { conversationsStore } from './conversations/index.svelte'; | |
| // MCP | |
| export { mcpStore } from './mcp/index.svelte'; | |
| // MODELS | |
| export { modelsStore } from './models/index.svelte'; | |
| // SERVER | |
| export { serverStore } from './server.svelte'; | |
| // UI / LAYOUT | |
| export { uiStore } from './ui.svelte'; | |
| // SETTINGS / UI PREFERENCES | |
| export { settingsStore } from './settings/index.svelte'; | |
| export { permissionsStore } from './permissions.svelte'; | |
| // TOOLS | |
| export { toolsStore } from './tools.svelte'; | |
| // ENVIRONMENT / META | |
| export { versionStore } from './version.svelte'; | |
| export { deviceStore } from './device.svelte'; | |