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: 1,085 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 | /** 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;
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