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
llama-cpp-K2-FAST2 / tools /ui /src /lib /components /app /chat /ChatMessages /ChatMessage /ChatMessageSynthetic.svelte
Download tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageSynthetic.svelte from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 794 Bytes
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageSynthetic.svelte
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageSynthetic.svelte
-
curl -L -o ChatMessageSynthetic.svelte https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageSynthetic.svelte
794 Bytes
| <script lang="ts"> | |
| import ChatMessageCwdChange from './ChatMessageCwdChange.svelte'; | |
| import type { DatabaseMessage } from '$lib/types'; | |
| import { parseCwdMessage } from '$lib/utils'; | |
| interface Props { | |
| class?: string; | |
| message: DatabaseMessage; | |
| } | |
| let { class: className = '', message }: Props = $props(); | |
| // Synthetic messages render a dedicated UI, never a user bubble. The only | |
| // kind today is the working-directory change; parse the content so the | |
| // row reuses the exact synthetic text (and future kinds slot in here). | |
| let isCwdChange = $derived(parseCwdMessage(message.content) !== null); | |
| </script> | |
| {#if isCwdChange} | |
| <ChatMessageCwdChange class={className} {message} /> | |
| {:else} | |
| <span class="text-muted-foreground block text-sm {className}">{message.content}</span> | |
| {/if} | |