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/image.constants.ts 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/constants/image.constants.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/constants/image.constants.ts
-
curl -L -o image.constants.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/constants/image.constants.ts
1.09 kB
| /** Image handling constants */ | |
| export const IMAGE = { | |
| /** JPEG quality used when transcoding HEIC images. */ | |
| HEIC_JPEG_QUALITY: 0.85, | |
| /** Unit conversion: pixels per megapixel. */ | |
| MEGAPIXELS_TO_PIXELS: 1_000_000 | |
| } as const; | |
| /** | |
| * JPEG and EXIF binary format constants for orientation parsing. | |
| */ | |
| export const EXIF = { | |
| /** APP1 segment marker byte, carries the EXIF payload */ | |
| APP1_MARKER: 0xe1, | |
| /** "Exif" signature opening the APP1 payload, big endian uint32 */ | |
| EXIF_SIGNATURE: 0x45786966, | |
| /** Size in bytes of one IFD directory entry */ | |
| IFD_ENTRY_SIZE: 12, | |
| /** JPEG start of image marker */ | |
| JPEG_SOI_MARKER: 0xffd8, | |
| /** EXIF tag id holding the orientation value */ | |
| ORIENTATION_TAG: 0x0112, | |
| /** Bytes of file prefix to scan, the APP1 EXIF segment sits near the start */ | |
| SCAN_BYTE_LIMIT: 128 * 1024, | |
| /** Start of scan marker byte, compressed data begins and no EXIF follows */ | |
| SOS_MARKER: 0xda, | |
| /** TIFF byte order mark for little endian ("II") */ | |
| TIFF_LITTLE_ENDIAN: 0x4949, | |
| /** TIFF magic number following the byte order mark */ | |
| TIFF_MAGIC: 42 | |
| } as const; | |