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/version.svelte.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.57 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/stores/version.svelte.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/stores/version.svelte.ts
-
curl -L -o version.svelte.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/stores/version.svelte.ts
1.57 kB
| /** | |
| * versionStore - Build version information | |
| * | |
| * - `build`: llama.cpp build number from `build.json`, embedded at llama.cpp | |
| * build time (LLAMA_BUILD_NUMBER). Shown in the UI when `showBuildVersion` | |
| * is enabled. | |
| * - `frontend`: frontend build version from SvelteKit's `_app/version.json`, | |
| * generated by the @vite-pwa/sveltekit plugin. Changes on every build, so | |
| * comparing it against localStorage reliably detects server upgrades. | |
| * | |
| * In dev mode both fall back to `'dev'`. | |
| */ | |
| import { browser } from '$app/environment'; | |
| import { base } from '$app/paths'; | |
| class VersionStore { | |
| build = $state<string>(''); | |
| frontend = $state<string>(''); | |
| /** | |
| * Fetch the version files. Called by initStores(); order-independent, | |
| * so it runs in the background. | |
| */ | |
| initialize(): void { | |
| if (!browser) return; | |
| if (import.meta.env.DEV) { | |
| this.build = 'dev'; | |
| this.frontend = 'dev'; | |
| return; | |
| } | |
| void this.load(); | |
| } | |
| private async load(): Promise<void> { | |
| try { | |
| const res = await fetch(`${base}/build.json`, { cache: 'no-store' }); | |
| if (res.ok) { | |
| const data = await res.json(); | |
| this.build = data.version ?? ''; | |
| } | |
| } catch { | |
| // build.json missing or unreachable - leave as empty string | |
| } | |
| try { | |
| const res = await fetch(`${base}/_app/version.json`, { cache: 'no-store' }); | |
| if (res.ok) { | |
| const data = await res.json(); | |
| this.frontend = data.version ?? ''; | |
| } | |
| } catch { | |
| // version.json missing or unreachable - leave as empty string | |
| } | |
| } | |
| } | |
| export const versionStore = new VersionStore(); | |