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/scripts/vite-plugin-nerdamer.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.63 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/scripts/vite-plugin-nerdamer.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/scripts/vite-plugin-nerdamer.ts
-
curl -L -o vite-plugin-nerdamer.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/scripts/vite-plugin-nerdamer.ts
1.63 kB
| import { build } from 'esbuild'; | |
| import { dirname, resolve } from 'path'; | |
| import { fileURLToPath } from 'url'; | |
| import type { Plugin } from 'vite'; | |
| const __dirname = dirname(fileURLToPath(import.meta.url)); | |
| const VENDORS_DIR = resolve(__dirname, '../src/lib/vendors'); | |
| const VIRTUAL_ID = 'virtual:nerdamer'; | |
| const RESOLVED_ID = '\0' + VIRTUAL_ID; | |
| /** | |
| * Bundle the vendored nerdamer-prime source into a minified IIFE string, | |
| * exposed as the `virtual:nerdamer` module. Flags mirror the upstream | |
| * build (esbuild --bundle --minify --format=iife --global-name=nerdamer), | |
| * so only human readable source lives in the repo and minification is a | |
| * build artifact. Vendored under src/lib/vendors/, upstream snapshot: | |
| * https://github.com/together-science/nerdamer-prime/commit/1936145f8af306ec0d883b9bfd7730aedd175c24 | |
| */ | |
| export function nerdamerPlugin(): Plugin { | |
| let bundled: string | null = null; | |
| return { | |
| async load(id) { | |
| if (id !== RESOLVED_ID) return undefined; | |
| if (bundled === null) { | |
| const result = await build({ | |
| alias: { | |
| 'big-integer': resolve(VENDORS_DIR, 'big-integer/BigInteger.js'), | |
| 'decimal.js': resolve(VENDORS_DIR, 'decimal.js/decimal.js') | |
| }, | |
| bundle: true, | |
| entryPoints: [resolve(VENDORS_DIR, 'nerdamer-prime/all.js')], | |
| format: 'iife', | |
| globalName: 'nerdamer', | |
| logLevel: 'silent', | |
| minify: true, | |
| write: false | |
| }); | |
| bundled = result.outputFiles[0].text; | |
| } | |
| return `export default ${JSON.stringify(bundled)};`; | |
| }, | |
| name: 'llamacpp:nerdamer', | |
| resolveId(id) { | |
| return id === VIRTUAL_ID ? RESOLVED_ID : undefined; | |
| } | |
| }; | |
| } | |