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: 1,935 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 51 52 53 54 55 56 57 58 59 60 61 62 | <script lang="ts">
import { ScrollCarousel } from '$lib/components/app';
import { ICON_CLASS_DEFAULT, UI_DATA_ATTRS } from '$lib/constants';
import { BooleanString } from '$lib/enums';
import { useScrollCarousel } from '$lib/hooks/use-scroll-carousel.svelte';
import type { SettingsSection, SettingsSectionTitle } from '$lib/types';
import { onMount, tick } from 'svelte';
interface Props {
sections: SettingsSection[];
isActive: (section: SettingsSection) => boolean;
onSectionChange?: (section: SettingsSectionTitle) => void;
}
let { isActive, onSectionChange, sections }: Props = $props();
const carousel = useScrollCarousel();
onMount(async () => {
await tick();
if (carousel.scrollContainer) {
const activeTab = carousel.scrollContainer.querySelector(
`[${UI_DATA_ATTRS.ACTIVE}="${BooleanString.TRUE}"]`
);
if (activeTab instanceof HTMLElement) {
carousel.scrollToCenter(activeTab);
}
}
});
export function updateCarousel() {
setTimeout(carousel.updateScrollButtons, 100);
}
</script>
<div class="flex flex-col bg-background md:hidden sticky top-13 z-50">
<div class="border-b border-border/30">
<ScrollCarousel alwaysShowArrows {carousel} containerClass="py-2" innerClass="gap-2">
{#each sections as section (section.title)}
<button
class="flex cursor-pointer items-center gap-2 rounded-lg px-3 py-2 text-sm whitespace-nowrap transition-colors first:ml-4 last:mr-4 hover:bg-accent {isActive(
section
)
? 'bg-accent text-accent-foreground'
: 'text-muted-foreground'}"
{...{ [UI_DATA_ATTRS.ACTIVE]: isActive(section) }}
onclick={(e: MouseEvent) => {
onSectionChange?.(section.title);
carousel.scrollToCenter(e.currentTarget as HTMLElement);
}}
>
<section.icon class="{ICON_CLASS_DEFAULT} flex-shrink-0" />
<span>{section.title}</span>
</button>
{/each}
</ScrollCarousel>
</div>
</div>
|