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/services/sandbox-worker.js from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.04 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/services/sandbox-worker.js
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/services/sandbox-worker.js
-
curl -L -o sandbox-worker.js https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/services/sandbox-worker.js
1.04 kB
| const logs = []; | |
| const fmt = (value) => { | |
| if (typeof value === 'string') return value; | |
| try { | |
| return JSON.stringify(value); | |
| } catch { | |
| return String(value); | |
| } | |
| }; | |
| const capture = | |
| (level, prefix) => | |
| (...args) => { | |
| logs.push(prefix + args.map(fmt).join(' ')); | |
| }; | |
| console.log = capture('log', ''); | |
| console.info = capture('info', ''); | |
| console.debug = capture('debug', ''); | |
| console.warn = capture('warn', 'warn: '); | |
| console.error = capture('error', 'error: '); | |
| self.onmessage = async (event) => { | |
| const reply = { logs, result: null, error: null }; | |
| try { | |
| const AsyncFunction = Object.getPrototypeOf(async function () {}).constructor; | |
| // The prelude bundled ahead of this shim defines self.nerdamer, | |
| // passed into the execution scope as the `nerdamer` parameter. | |
| const value = await new AsyncFunction('nerdamer', event.data.code)(self.nerdamer); | |
| if (value !== undefined) reply.result = fmt(value); | |
| } catch (err) { | |
| reply.error = err instanceof Error ? err.stack || err.message : String(err); | |
| } | |
| self.postMessage(reply); | |
| }; | |