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/cors-proxy.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.23 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/utils/cors-proxy.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/utils/cors-proxy.ts
-
curl -L -o cors-proxy.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/utils/cors-proxy.ts
1.23 kB
| /** | |
| * 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; | |
| } | |