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/tests/client/components/McpServerFormWrapper.svelte from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 942 Bytes
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/tests/client/components/McpServerFormWrapper.svelte
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/tests/client/components/McpServerFormWrapper.svelte
-
curl -L -o McpServerFormWrapper.svelte https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/tests/client/components/McpServerFormWrapper.svelte
942 Bytes
| <script lang="ts"> | |
| import McpServerForm from '$lib/components/app/mcp/McpServerForm.svelte'; | |
| import { untrack } from 'svelte'; | |
| interface Props { | |
| headers?: string; | |
| } | |
| let { headers = '' }: Props = $props(); | |
| let headersState = $state(untrack(() => headers)); | |
| let lastCapturedHeaders = $state(untrack(() => headers)); | |
| $effect(() => { | |
| if (headers !== lastCapturedHeaders) { | |
| headersState = headers; | |
| lastCapturedHeaders = headers; | |
| } | |
| }); | |
| </script> | |
| <!-- | |
| Drives McpServerForm with a controlled `headers` string and exposes the | |
| latest captured value through `data-captured-headers` so the client test | |
| can read it back without a custom binding API. | |
| --> | |
| <McpServerForm | |
| headers={headersState} | |
| id="mcp-server-form-test" | |
| onHeadersChange={(value) => { | |
| headersState = value; | |
| }} | |
| onUrlChange={() => {}} | |
| url="https://example.test/mcp" | |
| /> | |
| <div data-captured-headers={headersState} data-testid="captured-headers" hidden></div> | |