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: 1,367 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 | <script lang="ts">
import { AlertTriangle, Loader2, RefreshCw } from '@lucide/svelte';
import * as Alert from '$lib/components/ui/alert';
import { ICON_CLASS_DEFAULT } from '$lib/constants';
import { serverStore } from '$lib/stores';
let hasError = $derived(!!serverStore.error);
let isLoadingModel = $derived(serverStore.status === 503);
</script>
{#if hasError}
<div class="pointer-events-auto mx-auto mb-4 max-w-[48rem] px-1">
<Alert.Root variant={isLoadingModel ? 'default' : 'destructive'}>
{#if isLoadingModel}
<Loader2 class="{ICON_CLASS_DEFAULT} animate-spin" />
{:else}
<AlertTriangle class={ICON_CLASS_DEFAULT} />
{/if}
<Alert.Title class="flex items-center justify-between">
<span>{isLoadingModel ? 'Loading model' : 'Server unavailable'}</span>
{#if !isLoadingModel}
<button
class="flex items-center gap-1.5 rounded-lg bg-destructive/20 px-2 py-1 text-xs font-medium hover:bg-destructive/30 disabled:opacity-50"
disabled={serverStore.loading}
onclick={() => serverStore.fetch()}
>
<RefreshCw class="h-3 w-3 {serverStore.loading ? 'animate-spin' : ''}" />
{serverStore.loading ? 'Retrying...' : 'Retry'}
</button>
{/if}
</Alert.Title>
{#if !isLoadingModel}
<Alert.Description>{serverStore.error}</Alert.Description>
{/if}
</Alert.Root>
</div>
{/if}
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