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/headers.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.14 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/utils/headers.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/utils/headers.ts
-
curl -L -o headers.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/utils/headers.ts
1.14 kB
| /** | |
| * Header utilities for parsing and serializing HTTP headers. | |
| * Generic utilities not specific to MCP. | |
| */ | |
| /** | |
| * Parses a JSON string of headers into an array of key-value pairs. | |
| * Returns empty array if the JSON is invalid or empty. | |
| */ | |
| export function parseHeadersToArray(headersJson: string): { key: string; value: string }[] { | |
| if (!headersJson?.trim()) return []; | |
| try { | |
| const parsed = JSON.parse(headersJson); | |
| if (typeof parsed === 'object' && parsed !== null && !Array.isArray(parsed)) { | |
| return Object.entries(parsed).map(([key, value]) => ({ | |
| key, | |
| value: String(value) | |
| })); | |
| } | |
| } catch { | |
| return []; | |
| } | |
| return []; | |
| } | |
| /** | |
| * Serializes an array of header key-value pairs to a JSON string. | |
| * Filters out pairs with empty keys and returns empty string if no valid pairs. | |
| */ | |
| export function serializeHeaders(pairs: { key: string; value: string }[]): string { | |
| const validPairs = pairs.filter((p) => p.key.trim()); | |
| if (validPairs.length === 0) return ''; | |
| const obj: Record<string, string> = {}; | |
| for (const pair of validPairs) { | |
| obj[pair.key.trim()] = pair.value; | |
| } | |
| return JSON.stringify(obj); | |
| } | |