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
Download tools/ui/src/lib/utils/browser-info.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.28 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/utils/browser-info.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/utils/browser-info.ts
-
curl -L -o browser-info.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/utils/browser-info.ts
1.28 kB
| /** | |
| * Browser fallback for the server's `get_info` tool, offered only when the | |
| * server does not serve one (llama-server without --agent). It tells the model | |
| * which OS the browser runs on and that there is no local file or shell access, | |
| * so it does not plan around tools that are not there. | |
| * | |
| * @see server_tool_get_info in tools/server/server-tools.cpp - the served variant | |
| * @see buildBrowserInfoToolDefinition in constants/browser-info.ts - tool schema sent to the LLM | |
| */ | |
| import { browser } from '$app/environment'; | |
| import { | |
| BROWSER_INFO_NOTE, | |
| BROWSER_INFO_OS_UA_PATTERNS, | |
| BROWSER_INFO_OS_UNKNOWN | |
| } from '$lib/constants'; | |
| import type { ToolExecutionResult } from '$lib/types'; | |
| function detectOs(userAgent: string): string { | |
| for (const [pattern, os] of BROWSER_INFO_OS_UA_PATTERNS) { | |
| if (pattern.test(userAgent)) return os; | |
| } | |
| return BROWSER_INFO_OS_UNKNOWN; | |
| } | |
| /** | |
| * Result shape mirrors the server tool's JSON so the `get_info` renderer reads | |
| * `os` the same way, minus `cwd` - there is no working directory to report. | |
| */ | |
| export function executeBrowserInfoTool(): ToolExecutionResult { | |
| return { | |
| content: JSON.stringify({ | |
| note: BROWSER_INFO_NOTE, | |
| os: browser ? detectOs(navigator.userAgent) : BROWSER_INFO_OS_UNKNOWN | |
| }), | |
| isError: false | |
| }; | |
| } | |