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: 2,627 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 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | <script lang="ts">
import ChevronDown from '@lucide/svelte/icons/chevron-down';
import * as Collapsible from '$lib/components/ui/collapsible/index.js';
import { cn } from '$lib/components/ui/utils';
import { ICON_CLASS_DEFAULT } from '$lib/constants';
import type { Snippet } from 'svelte';
import type { Component } from 'svelte';
interface Props {
open?: boolean;
class?: string;
icon?: Component;
iconClass?: string;
iconUrl?: string | null;
title?: string;
titleSnippet?: Snippet;
subtitle?: string;
shimmerTitle?: boolean;
onToggle?: () => void;
children: Snippet;
}
let {
children,
class: className = '',
icon: IconComponent,
iconClass = ICON_CLASS_DEFAULT,
iconUrl = null,
onToggle,
open = $bindable(false),
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subtitle,
title = '',
titleSnippet
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function hideBrokenIcon(event: Event) {
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</script>
<Collapsible.Root
class={cn('group/collapsible', 'overflow-hidden rounded-md', className)}
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<Collapsible.Trigger
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>
<div class="flex min-w-0 items-start gap-2 text-muted-foreground">
{#if iconUrl}
<img
alt=""
class={cn('shrink-0 rounded-sm mt-0.5', iconClass)}
onerror={hideBrokenIcon}
src={iconUrl}
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{:else if IconComponent}
<IconComponent class={cn('shrink-0 text-muted-foreground/60 mt-0.5', iconClass)} />
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<span class={cn('text-sm font-medium', shimmerTitle ? 'shimmer-text' : 'text-foreground/80')}>
{#if titleSnippet}
{@render titleSnippet()}
{:else}
{title}
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</span>
{#if subtitle}
<span class="text-xs italic text-muted-foreground/70">{subtitle}</span>
{/if}
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<ChevronDown
class={cn(
'size-4 shrink-0 text-muted-foreground/60 transition-all duration-150 ease-out opacity-0 group-hover/collapsible:opacity-100 mt-0.5',
open && 'rotate-180'
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<span class="sr-only">Toggle content</span>
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<Collapsible.Content>
<!-- See CollapsibleContentBlock: bits-ui keeps closed content mounted, which
makes a collapsed tool result re-render on every streamed token. -->
{#if open}
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{@render children()}
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|