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: 1,281 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 | /**
* 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
};
}
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