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: 1,542 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 | <script lang="ts">
import ModelId from './ModelId.svelte';
import { Package } from '@lucide/svelte';
import { ActionIconCopyToClipboard, BadgeInfo } from '$lib/components/app';
import * as Tooltip from '$lib/components/ui/tooltip';
import { modelsStore, serverStore } from '$lib/stores';
interface Props {
class?: string;
model?: string;
onclick?: () => void;
showCopyIcon?: boolean;
showTooltip?: boolean;
}
let {
class: className = '',
model: modelProp,
onclick,
showCopyIcon = false,
showTooltip = false
}: Props = $props();
let model = $derived(modelProp || modelsStore.singleModelName);
let isModelMode = $derived(serverStore.isModelMode);
let shouldShow = $derived(model && (modelProp !== undefined || isModelMode));
</script>
{#snippet badgeContent(triggerProps?: Record<string, unknown>)}
<BadgeInfo {...triggerProps ?? {}} class={className} {onclick}>
{#snippet icon()}
<Package class="h-3 w-3" />
{/snippet}
{#if model}
<ModelId modelId={model} />
{/if}
{#if showCopyIcon}
<ActionIconCopyToClipboard ariaLabel="Copy model name" text={model || ''} />
{/if}
</BadgeInfo>
{/snippet}
{#if shouldShow}
{#if showTooltip}
<Tooltip.Root>
<Tooltip.Trigger>
<!-- prevent another nested button element -->
{#snippet child({ props })}
{@render badgeContent(props)}
{/snippet}
</Tooltip.Trigger>
<Tooltip.Content>
{onclick ? 'Click for model details' : model}
</Tooltip.Content>
</Tooltip.Root>
{:else}
{@render badgeContent()}
{/if}
{/if}
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