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/ChatFormPickersHarness.svelte from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.09 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/tests/client/components/ChatFormPickersHarness.svelte
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/tests/client/components/ChatFormPickersHarness.svelte
-
curl -L -o ChatFormPickersHarness.svelte https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/tests/client/components/ChatFormPickersHarness.svelte
1.09 kB
| <script lang="ts"> | |
| import { | |
| useChatFormPickers, | |
| type UseChatFormPickersReturn | |
| } from '$lib/hooks/use-chat-form-pickers.svelte'; | |
| let value = $state(''); | |
| let caretOffset = $state(0); | |
| const calls: string[] = []; | |
| const pickers = useChatFormPickers({ | |
| focusInput: () => {}, | |
| getCaretOffset: () => caretOffset, | |
| getCwd: () => null, | |
| getPickersRef: () => undefined, | |
| getServerHome: () => null, | |
| getShowModelSelector: () => true, | |
| getValue: () => value, | |
| hasCwdTools: () => true, | |
| hasPrompts: () => true, | |
| openModelSelector: () => { | |
| calls.push('openModelSelector'); | |
| }, | |
| setCaretOffset: (o) => { | |
| caretOffset = o; | |
| }, | |
| setValue: (v) => { | |
| value = v; | |
| calls.push(`setValue:${v}`); | |
| } | |
| }); | |
| // Simulate the user typing: update the buffer and run the input flow. | |
| export function type(text: string) { | |
| value = text; | |
| caretOffset = text.length; | |
| pickers.handleInput(); | |
| } | |
| export function getValue() { | |
| return value; | |
| } | |
| export function getCalls() { | |
| return calls; | |
| } | |
| export function getPickers(): UseChatFormPickersReturn { | |
| return pickers; | |
| } | |
| </script> | |