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/init.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.12 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/stores/init.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/stores/init.ts
-
curl -L -o init.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/stores/init.ts
1.12 kB
| // direct imports, not via the barrel, to avoid circular deps | |
| import { conversationsStore } from './conversations/index.svelte'; | |
| import { permissionsStore } from './permissions.svelte'; | |
| import { settingsStore } from './settings/index.svelte'; | |
| import { tabsStore } from './tabs.svelte'; | |
| import { toolsStore } from './tools.svelte'; | |
| import { versionStore } from './version.svelte'; | |
| import { browser } from '$app/environment'; | |
| import { MigrationService } from '$lib/services/migration.service'; | |
| let startup: Promise<void> | null = null; | |
| export function initStores(): Promise<void> { | |
| if (!browser) return Promise.resolve(); | |
| startup ??= (async () => { | |
| await MigrationService.runAllMigrations(); | |
| settingsStore.initialize(); | |
| permissionsStore.initialize(); | |
| toolsStore.initialize(); | |
| void versionStore.initialize(); | |
| // the full conversation list loads in the background; once it is back, | |
| // prune persisted tabs against the conversations that still exist | |
| void conversationsStore.initialize().then(() => { | |
| tabsStore.init(conversationsStore.conversations.map((c) => c.id)); | |
| }); | |
| })(); | |
| return startup; | |
| } | |