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: 1,823 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 | /**
* PropsService - Fetches server properties from /props
*
* Returns global server settings and capabilities, including per-model
* modalities in MODEL mode. No reactive state; consumed by serverStore and
* the model props manager.
*/
import { apiFetchWithParams } from '$lib/utils';
export class PropsService {
/**
* Fetches global server properties from the `/props` endpoint.
* In MODEL mode, returns modalities for the single loaded model.
* In ROUTER mode, returns server-wide settings without model-specific modalities.
*
* @param autoload - If false, prevents automatic model loading (default: false)
* @returns Server properties including default generation settings and capabilities
* @throws {Error} If the request fails or returns invalid data
*/
static async fetch(autoload = false): Promise<ApiLlamaCppServerProps> {
const params: Record<string, string> = {};
if (!autoload) {
params.autoload = 'false';
}
return apiFetchWithParams<ApiLlamaCppServerProps>('./props', params, { authOnly: true });
}
/**
* Fetches server properties for a specific model (ROUTER mode only).
* Required in ROUTER mode because global `/props` does not include per-model modalities.
*
* @param modelId - The model ID to fetch properties for
* @param autoload - If false, prevents automatic model loading (default: false)
* @returns Server properties specific to the requested model
* @throws {Error} If the request fails, model not found, or model not loaded
*/
static async fetchForModel(modelId: string, autoload = false): Promise<ApiLlamaCppServerProps> {
const params: Record<string, string> = { model: modelId };
if (!autoload) {
params.autoload = 'false';
}
return apiFetchWithParams<ApiLlamaCppServerProps>('./props', params, { authOnly: true });
}
}
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