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: 1,702 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 | <script lang="ts">
import { ConversationSelection } from '$lib/components/app';
import * as Dialog from '$lib/components/ui/dialog';
interface Props {
conversations: DatabaseConversation[];
messageCountMap?: Map<string, number>;
mode: 'export' | 'import';
onCancel: () => void;
onConfirm: (selectedConversations: DatabaseConversation[]) => void;
open?: boolean;
}
let {
conversations,
messageCountMap = new Map(),
mode,
onCancel,
onConfirm,
open = $bindable(false)
}: Props = $props();
let conversationSelectionRef: ConversationSelection | undefined = $state();
let previousOpen = $state(false);
$effect(() => {
if (open && !previousOpen && conversationSelectionRef) {
conversationSelectionRef.reset();
} else if (!open && previousOpen) {
onCancel();
}
previousOpen = open;
});
</script>
<Dialog.Root bind:open>
<Dialog.Portal>
<Dialog.Overlay class="z-1000000" />
<Dialog.Content class="z-1000001 max-w-2xl">
<Dialog.Header>
<Dialog.Title>
Select Conversations to {mode === 'export' ? 'Export' : 'Import'}
</Dialog.Title>
<Dialog.Description>
{#if mode === 'export'}
Choose which conversations you want to export. Selected conversations will be downloaded
as a JSON file.
{:else}
Choose which conversations you want to import. Selected conversations will be merged
with your existing conversations.
{/if}
</Dialog.Description>
</Dialog.Header>
<ConversationSelection
bind:this={conversationSelectionRef}
{conversations}
isOpen={open}
{messageCountMap}
{mode}
{onCancel}
{onConfirm}
/>
</Dialog.Content>
</Dialog.Portal>
</Dialog.Root>
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