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/utils/api-key-validation.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.56 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/utils/api-key-validation.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/utils/api-key-validation.ts
-
curl -L -o api-key-validation.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/utils/api-key-validation.ts
1.56 kB
| import { error } from '@sveltejs/kit'; | |
| import { browser } from '$app/environment'; | |
| import { base } from '$app/paths'; | |
| import { HEADERS } from '$lib/constants'; | |
| import { MimeTypeApplication } from '$lib/enums'; | |
| import { settingsStore } from '$lib/stores/settings/index.svelte'; | |
| /** | |
| * Validates API key by making a request to the server props endpoint | |
| * Throws SvelteKit errors for authentication failures or server issues | |
| */ | |
| export async function validateApiKey(fetch: typeof globalThis.fetch): Promise<void> { | |
| if (!browser) { | |
| return; | |
| } | |
| const apiKey = settingsStore.config.apiKey; | |
| try { | |
| const headers: Record<string, string> = { | |
| [HEADERS.CONTENT_TYPE]: MimeTypeApplication.JSON | |
| }; | |
| // Probe /props even without a stored key: on a server started with | |
| // --api-key the unauthenticated request returns 401 and surfaces the | |
| // API key splash, which is the onboarding path for entering the key. | |
| if (apiKey) { | |
| headers[HEADERS.AUTHORIZATION] = `${HEADERS.BEARER}${apiKey}`; | |
| } | |
| const response = await fetch(`${base}/props`, { headers }); | |
| if (!response.ok) { | |
| if (response.status === 401 || response.status === 403) { | |
| throw error(401, 'Access denied'); | |
| } | |
| console.warn(`Server responded with status ${response.status} during API key validation`); | |
| return; | |
| } | |
| } catch (err) { | |
| // If it's already a SvelteKit error, re-throw it | |
| if (err && typeof err === 'object' && 'status' in err) { | |
| throw err; | |
| } | |
| // Network or other errors | |
| console.warn('Cannot connect to server for API key validation:', err); | |
| } | |
| } | |