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
File size: 1,556 Bytes
c59fd93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | 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);
}
}
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