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: 1,232 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 | /**
* CORS Proxy utility for routing requests through llama-server's CORS proxy.
*/
import { base } from '$app/paths';
import { CORS_PROXY, CORS_PROXY_ENDPOINT } from '$lib/constants';
/**
* Build a proxied URL that routes through llama-server's CORS proxy.
* @param targetUrl - The original URL to proxy
* @returns URL pointing to the CORS proxy with target encoded
*/
export function buildProxiedUrl(targetUrl: string): URL {
const proxyPath = `${base}${CORS_PROXY_ENDPOINT}`;
const proxyUrl = new URL(proxyPath, window.location.origin);
proxyUrl.searchParams.set(CORS_PROXY.URL_PARAM, targetUrl);
return proxyUrl;
}
/**
* Wrap original headers for proxying through the CORS proxy. This avoids issues with duplicated llama.cpp-specific and target headers when using the CORS proxy.
* @param headers - The original headers to be proxied to target
* @returns List of "wrapped" headers to be sent to the CORS proxy
*/
export function buildProxiedHeaders(headers: Record<string, string>): Record<string, string> {
const proxiedHeaders: Record<string, string> = {};
for (const [key, value] of Object.entries(headers)) {
proxiedHeaders[`${CORS_PROXY.HEADER_PREFIX}${key}`] = value;
}
return proxiedHeaders;
}
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