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/constants/browser-info.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.48 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/constants/browser-info.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/constants/browser-info.ts
-
curl -L -o browser-info.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/constants/browser-info.ts
1.48 kB
| import { CLI_FLAGS } from './cli-flags.constants'; | |
| import { BuiltInTool, JsonSchemaType, ToolCallType } from '$lib/enums'; | |
| import type { OpenAIToolDefinition } from '$lib/types'; | |
| // get_info is served by the server, but the browser falls back to this | |
| // implementation when the server does not provide it - same wire name. | |
| export const BROWSER_INFO_TOOL_NAME = BuiltInTool.SERVER_GET_INFO; | |
| /** UA token to OS name, first match wins - Android and iOS UAs also carry the Linux / Mac OS X tokens */ | |
| export const BROWSER_INFO_OS_UA_PATTERNS: readonly [RegExp, string][] = [ | |
| [/Windows NT/, 'Windows'], | |
| [/Android/, 'Android'], | |
| [/iPhone|iPad|iPod/, 'iOS'], | |
| [/CrOS/, 'ChromeOS'], | |
| [/Mac OS X/, 'macOS'], | |
| [/Linux/, 'Linux'] | |
| ]; | |
| export const BROWSER_INFO_OS_UNKNOWN = 'unknown'; | |
| /** Sent to the model as the `note` field of the tool result, next to the OS name */ | |
| export const BROWSER_INFO_NOTE = `This environment is browser-only, it cannot read or modify local files, and it cannot run shell commands. To get local file access, tell user to launch llama-server with the ${CLI_FLAGS.AGENT} argument.`; | |
| export function buildBrowserInfoToolDefinition(): OpenAIToolDefinition { | |
| return { | |
| function: { | |
| description: | |
| 'Get runtime info (OS name), may call when user asks about local files or shell commands', | |
| name: BROWSER_INFO_TOOL_NAME, | |
| parameters: { | |
| properties: {}, | |
| required: [], | |
| type: JsonSchemaType.OBJECT | |
| } | |
| }, | |
| type: ToolCallType.FUNCTION | |
| }; | |
| } | |