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 /ChatMessageCwdChange.svelte
Download tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageCwdChange.svelte from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.09 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageCwdChange.svelte
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageCwdChange.svelte
-
curl -L -o ChatMessageCwdChange.svelte https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageCwdChange.svelte
1.09 kB
| <script lang="ts"> | |
| import { Folder, FolderX } from '@lucide/svelte'; | |
| import type { DatabaseMessage } from '$lib/types'; | |
| import { parseCwdMessage } from '$lib/utils'; | |
| interface Props { | |
| class?: string; | |
| message: DatabaseMessage; | |
| } | |
| let { class: className = '', message }: Props = $props(); | |
| // Parse the synthetic message content in the UI so the row reuses the | |
| // exact same text the model saw, including any guidance suffix. | |
| let info = $derived(parseCwdMessage(message.content)); | |
| </script> | |
| {#if info} | |
| <div class="text-muted-foreground flex items-center gap-2 py-1.5 {className}"> | |
| {#if info.path === null} | |
| <FolderX class="text-muted-foreground/60 h-3.5 w-3.5 shrink-0" /> | |
| <span class="text-foreground/80 text-sm font-medium">Working directory cleared</span> | |
| {:else} | |
| <Folder class="text-muted-foreground/60 h-3.5 w-3.5 shrink-0" /> | |
| <span class="text-foreground/80 text-sm font-medium">Set working directory to </span> | |
| <span class="font-mono text-foreground/90 text-sm break-all" title={info.path}> | |
| {info.display} | |
| </span> | |
| {/if} | |
| </div> | |
| {/if} | |