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 examples/model-conversion/scripts/utils/compare_tokens.py from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 2.02 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/model-conversion/scripts/utils/compare_tokens.py
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/examples/model-conversion/scripts/utils/compare_tokens.py
-
curl -L -o compare_tokens.py https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/model-conversion/scripts/utils/compare_tokens.py
2.02 kB
| #!/usr/bin/env python3 | |
| import argparse | |
| import sys | |
| from common import compare_tokens # type: ignore[import-not-found, ty:unresolved-import] | |
| def parse_arguments(): | |
| parser = argparse.ArgumentParser( | |
| description='Compare tokens between two models', | |
| formatter_class=argparse.RawDescriptionHelpFormatter, | |
| epilog=""" | |
| Examples: | |
| %(prog)s pytorch-gemma-3-270m-it llamacpp-gemma-3-270m-it-bf16 | |
| """ | |
| ) | |
| parser.add_argument( | |
| 'original', | |
| help='Original model name' | |
| ) | |
| parser.add_argument( | |
| 'converted', | |
| help='Converted model name' | |
| ) | |
| parser.add_argument( | |
| '-s', '--suffix', | |
| default='', | |
| help='Type suffix (e.g., "-embeddings")' | |
| ) | |
| parser.add_argument( | |
| '-d', '--data-dir', | |
| default='data', | |
| help='Directory containing token files (default: data)' | |
| ) | |
| parser.add_argument( | |
| '-v', '--verbose', | |
| action='store_true', | |
| help='Print prompts from both models' | |
| ) | |
| return parser.parse_args() | |
| def main(): | |
| args = parse_arguments() | |
| if args.verbose: | |
| from pathlib import Path | |
| data_dir = Path(args.data_dir) | |
| prompt1_file = data_dir / f"{args.original}{args.suffix}-prompt.txt" | |
| prompt2_file = data_dir / f"{args.converted}{args.suffix}-prompt.txt" | |
| if prompt1_file.exists(): | |
| print(f"\nOriginal model prompt ({args.original}):") | |
| print(f" {prompt1_file.read_text().strip()}") | |
| if prompt2_file.exists(): | |
| print(f"\nConverted model prompt ({args.converted}):") | |
| print(f" {prompt2_file.read_text().strip()}") | |
| print() | |
| result = compare_tokens( | |
| args.original, | |
| args.converted, | |
| type_suffix=args.suffix, | |
| output_dir=args.data_dir | |
| ) | |
| # Enable the script to be used in shell scripts so that they can check | |
| # the exit code for success/failure. | |
| sys.exit(0 if result else 1) | |
| if __name__ == "__main__": | |
| main() | |